Data_analysis_Dubai_Entreupreneurship.ipynb

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      "metadata": {
        "id": "2CV4AOwYMs_K"
      },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
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        "pd.options.display.float_format = '{:.2f}'.format"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkqlX1nLWkSe"
      },
      "source": [
        "**Analyzing the categories dataframe**"
      ]
    },
    {
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          "text": [
            "Shape of the dataframe: (744, 9)\n"
          ]
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          "data": {
            "text/plain": [
              "                                   uuid                   name      type  \\\n",
              "0  f9b14a15-5517-8f38-0562-729ebb54dfdb       Homeless Shelter  category   \n",
              "1  f8320fcf-b657-37d2-1495-daa3ad888ece               Freemium  category   \n",
              "2  f0193199-a968-b457-eb49-95344e22a5ce             Industrial  category   \n",
              "3  ed8217ff-bd11-26fe-d82e-eb98a276dc2e  Innovation Management  category   \n",
              "4  e9794581-9547-2150-8185-7b747f5c9913               Lighting  category   \n",
              "\n",
              "               permalink                                             cb_url  \\\n",
              "0       homeless-shelter  https://www.crunchbase.com/category/homeless-s...   \n",
              "1               freemium       https://www.crunchbase.com/category/freemium   \n",
              "2             industrial     https://www.crunchbase.com/category/industrial   \n",
              "3  innovation-management  https://www.crunchbase.com/category/innovation...   \n",
              "4               lighting       https://www.crunchbase.com/category/lighting   \n",
              "\n",
              "   rank           created_at           updated_at   category_groups_list  \n",
              "0   NaN  2014-04-20 07:58:46  2018-02-13 00:59:11                  Other  \n",
              "1   NaN  2014-04-20 07:58:46  2018-02-13 00:59:11                  Other  \n",
              "2   NaN  2014-04-20 07:58:46  2018-02-13 00:59:11          Manufacturing  \n",
              "3   NaN  2014-04-20 07:58:46  2018-02-13 00:59:11  Professional Services  \n",
              "4   NaN  2014-04-20 07:58:46  2018-02-13 00:59:11               Hardware  "
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              "      <td>f9b14a15-5517-8f38-0562-729ebb54dfdb</td>\n",
              "      <td>Homeless Shelter</td>\n",
              "      <td>category</td>\n",
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              "      <td>https://www.crunchbase.com/category/homeless-s...</td>\n",
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              "      <td>f8320fcf-b657-37d2-1495-daa3ad888ece</td>\n",
              "      <td>Freemium</td>\n",
              "      <td>category</td>\n",
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              "      <td>https://www.crunchbase.com/category/freemium</td>\n",
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              "      <th>2</th>\n",
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              "      <td>Industrial</td>\n",
              "      <td>category</td>\n",
              "      <td>industrial</td>\n",
              "      <td>https://www.crunchbase.com/category/industrial</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-04-20 07:58:46</td>\n",
              "      <td>2018-02-13 00:59:11</td>\n",
              "      <td>Manufacturing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ed8217ff-bd11-26fe-d82e-eb98a276dc2e</td>\n",
              "      <td>Innovation Management</td>\n",
              "      <td>category</td>\n",
              "      <td>innovation-management</td>\n",
              "      <td>https://www.crunchbase.com/category/innovation...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-04-20 07:58:46</td>\n",
              "      <td>2018-02-13 00:59:11</td>\n",
              "      <td>Professional Services</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e9794581-9547-2150-8185-7b747f5c9913</td>\n",
              "      <td>Lighting</td>\n",
              "      <td>category</td>\n",
              "      <td>lighting</td>\n",
              "      <td>https://www.crunchbase.com/category/lighting</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2014-04-20 07:58:46</td>\n",
              "      <td>2018-02-13 00:59:11</td>\n",
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        }
      ],
      "source": [
        "df_category=pd.read_csv(\"/content/drive/MyDrive/ISER/datasets/uae/category_groups.csv\")\n",
        "print(f\"Shape of the dataframe: {df_category.shape}\")\n",
        "df_category.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_category.info()"
      ],
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        "colab": {
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        "id": "0Har9v1gUI2O",
        "outputId": "1bf33dc5-ca41-4e50-8140-e52aaaba815e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 744 entries, 0 to 743\n",
            "Data columns (total 9 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   uuid                  744 non-null    object \n",
            " 1   name                  744 non-null    object \n",
            " 2   type                  744 non-null    object \n",
            " 3   permalink             744 non-null    object \n",
            " 4   cb_url                744 non-null    object \n",
            " 5   rank                  0 non-null      float64\n",
            " 6   created_at            744 non-null    object \n",
            " 7   updated_at            744 non-null    object \n",
            " 8   category_groups_list  744 non-null    object \n",
            "dtypes: float64(1), object(8)\n",
            "memory usage: 52.4+ KB\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
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        "df_category.describe()"
      ],
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              "       rank\n",
              "count  0.00\n",
              "mean    NaN\n",
              "std     NaN\n",
              "min     NaN\n",
              "25%     NaN\n",
              "50%     NaN\n",
              "75%     NaN\n",
              "max     NaN"
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ay7fpoonWtc2"
      },
      "source": [
        "**Analyzing the acquisition dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 646
        },
        "id": "t1MsgrueV1U8",
        "outputId": "652d39f3-6044-4972-f8a4-bbec59dcd23b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the dataframe: (260, 27)\n"
          ]
        },
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            "text/plain": [
              "                                   uuid  \\\n",
              "0  0be25f0c-9e83-5122-ae9d-68b3980fdf48   \n",
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              "\n",
              "                                                name         type  \\\n",
              "0                          Maktoob acquired by Yahoo  acquisition   \n",
              "1  Telerate Systems acquired by Interactive Data ...  acquisition   \n",
              "2                   GoNabit acquired by LivingSocial  acquisition   \n",
              "3                             Sukar acquired by Souq  acquisition   \n",
              "4                Game Minion acquired by Corona Labs  acquisition   \n",
              "\n",
              "                                           permalink  \\\n",
              "0                   yahoo-acquires-maktoob--0be25f0c   \n",
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              "4         corona-labs-acquires-game-minion--b8b0b839   \n",
              "\n",
              "                                              cb_url      rank  \\\n",
              "0  https://www.crunchbase.com/acquisition/yahoo-a... 103133.00   \n",
              "1  https://www.crunchbase.com/acquisition/interac...  96662.00   \n",
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              "4  https://www.crunchbase.com/acquisition/corona-...  57700.00   \n",
              "\n",
              "        created_at       updated_at                         acquiree_uuid  \\\n",
              "0  8/25/2009 16:58  2/12/2018 23:55  e542dd76-1ee1-5227-2f27-4fb9ecd65fc2   \n",
              "1   1/16/2010 4:39  2/12/2018 23:38  5aadaf8a-db42-9558-d79c-52a1c5302c74   \n",
              "2   7/6/2011 17:27  2/12/2018 23:37  c615bcf8-d0cb-4c92-5da9-13b0c723cc5e   \n",
              "3  10/29/2012 6:08  2/12/2018 23:40  8bf9b7c5-6513-86a7-fe1f-d47e2c8ed5bf   \n",
              "4  12/14/2012 8:26  2/12/2018 23:42  2ae197c1-4b47-af96-03ec-cf11bbd17e1d   \n",
              "\n",
              "      acquiree_name  ...                                    acquirer_cb_url  \\\n",
              "0           Maktoob  ...      https://www.crunchbase.com/organization/yahoo   \n",
              "1  Telerate Systems  ...  https://www.crunchbase.com/organization/intera...   \n",
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              "\n",
              "  acquirer_country_code  acquirer_state_code       acquirer_region  \\\n",
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              "2                   USA                   DC  District of Columbia   \n",
              "3                   ARE                  NaN                 Dubai   \n",
              "4                   USA                   CA            California   \n",
              "\n",
              "  acquirer_city acquisition_type acquired_on    price_usd        price  \\\n",
              "0        Dulles              NaN   2/26/2010 164000000.00 164000000.00   \n",
              "1      New York              NaN   1/15/2010          NaN          NaN   \n",
              "2    Washington              NaN   6/27/2011          NaN          NaN   \n",
              "3         Dubai              NaN   4/29/2012          NaN          NaN   \n",
              "4     Palo Alto              NaN  12/13/2012          NaN          NaN   \n",
              "\n",
              "  price_currency_code  \n",
              "0                 USD  \n",
              "1                 NaN  \n",
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              "\n",
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              "      <th>0</th>\n",
              "      <td>0be25f0c-9e83-5122-ae9d-68b3980fdf48</td>\n",
              "      <td>Maktoob acquired by Yahoo</td>\n",
              "      <td>acquisition</td>\n",
              "      <td>yahoo-acquires-maktoob--0be25f0c</td>\n",
              "      <td>https://www.crunchbase.com/acquisition/yahoo-a...</td>\n",
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              "      <th>1</th>\n",
              "      <td>80d3f35c-4ef5-085d-19d2-0e1d5ae780a9</td>\n",
              "      <td>Telerate Systems acquired by Interactive Data ...</td>\n",
              "      <td>acquisition</td>\n",
              "      <td>interactive-data-acquires-telerate-systems--80...</td>\n",
              "      <td>https://www.crunchbase.com/acquisition/interac...</td>\n",
              "      <td>96662.00</td>\n",
              "      <td>1/16/2010 4:39</td>\n",
              "      <td>2/12/2018 23:38</td>\n",
              "      <td>5aadaf8a-db42-9558-d79c-52a1c5302c74</td>\n",
              "      <td>Telerate Systems</td>\n",
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              "      <td>USA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>1/15/2010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>e9ba0414-8be5-baee-ae32-49643464655b</td>\n",
              "      <td>GoNabit acquired by LivingSocial</td>\n",
              "      <td>acquisition</td>\n",
              "      <td>livingsocial-acquires-gonabit--e9ba0414</td>\n",
              "      <td>https://www.crunchbase.com/acquisition/livings...</td>\n",
              "      <td>100722.00</td>\n",
              "      <td>7/6/2011 17:27</td>\n",
              "      <td>2/12/2018 23:37</td>\n",
              "      <td>c615bcf8-d0cb-4c92-5da9-13b0c723cc5e</td>\n",
              "      <td>GoNabit</td>\n",
              "      <td>...</td>\n",
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              "      <td>USA</td>\n",
              "      <td>DC</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>6/27/2011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>e4bf0035-a232-7fae-83b8-e072cf3dcee0</td>\n",
              "      <td>Sukar acquired by Souq</td>\n",
              "      <td>acquisition</td>\n",
              "      <td>souq-com-acquires-sukar--e4bf0035</td>\n",
              "      <td>https://www.crunchbase.com/acquisition/souq-co...</td>\n",
              "      <td>59147.00</td>\n",
              "      <td>10/29/2012 6:08</td>\n",
              "      <td>2/12/2018 23:40</td>\n",
              "      <td>8bf9b7c5-6513-86a7-fe1f-d47e2c8ed5bf</td>\n",
              "      <td>Sukar</td>\n",
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              "      <td>ARE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dubai</td>\n",
              "      <td>Dubai</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4/29/2012</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b8b0b839-1ea9-43ee-59a5-4422f3b19fef</td>\n",
              "      <td>Game Minion acquired by Corona Labs</td>\n",
              "      <td>acquisition</td>\n",
              "      <td>corona-labs-acquires-game-minion--b8b0b839</td>\n",
              "      <td>https://www.crunchbase.com/acquisition/corona-...</td>\n",
              "      <td>57700.00</td>\n",
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              "      <td>2/12/2018 23:42</td>\n",
              "      <td>2ae197c1-4b47-af96-03ec-cf11bbd17e1d</td>\n",
              "      <td>Game Minion</td>\n",
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              "      <td>Palo Alto</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12/13/2012</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df_acquisition=pd.read_csv('/content/drive/MyDrive/ISER/datasets/uae/uae_acquisitions.csv')\n",
        "print(f\"Shape of the dataframe: {df_acquisition.shape}\")\n",
        "df_acquisition.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jslnu1ziW2GH"
      },
      "source": [
        "**Analyzing and processing the funding dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "WxmVjkiGWM9t",
        "outputId": "0d37b420-f501-4cb5-aa11-c289fd24a758"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the dataframe: (1470, 24)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   uuid  \\\n",
              "0  014ffe52-1a01-2482-5879-8db9d32ad766   \n",
              "1  2efa56df-ae64-d329-14de-436bbf751d2d   \n",
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              "                                            name           type  \\\n",
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              "1             Venture Round - Pulse Technologies  funding_round   \n",
              "2  Series A - Content Syndicate: Words on Demand  funding_round   \n",
              "3                      Venture Round - NeuString  funding_round   \n",
              "4                        Venture Round - Maktoob  funding_round   \n",
              "\n",
              "                                           permalink  \\\n",
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              "4                   maktoob-series-unknown--da1838e2   \n",
              "\n",
              "                                              cb_url      rank  \\\n",
              "0  https://www.crunchbase.com/funding_round/conse... 122572.00   \n",
              "1  https://www.crunchbase.com/funding_round/pulse... 339418.00   \n",
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              "\n",
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              "\n",
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              "1               NaN           NaN                         NaN   \n",
              "2        1500000.00    1500000.00                         USD   \n",
              "3               NaN           NaN                         NaN   \n",
              "4        5250000.00    5250000.00                         USD   \n",
              "\n",
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              "0                      NaN                   NaN   \n",
              "1                      NaN                   NaN   \n",
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              "0                                 NaN           1.00   \n",
              "1                                 NaN           1.00   \n",
              "2                                 NaN            NaN   \n",
              "3                                 NaN           1.00   \n",
              "4                                 NaN           1.00   \n",
              "\n",
              "                               org_uuid                            org_name  \\\n",
              "0  2f0a2474-bd62-744e-9868-74ced3e3f1a7             Conservus International   \n",
              "1  8d302824-3ea2-a8d4-c14f-e723bcb7196d                  Pulse Technologies   \n",
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              "3  9dcbe645-1f9e-35c4-7d35-03642e9eae82                           NeuString   \n",
              "4  e542dd76-1ee1-5227-2f27-4fb9ecd65fc2                             Maktoob   \n",
              "\n",
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              "0                 NaN  \n",
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              "      <th>state_code</th>\n",
              "      <th>...</th>\n",
              "      <th>raised_amount_usd</th>\n",
              "      <th>raised_amount</th>\n",
              "      <th>raised_amount_currency_code</th>\n",
              "      <th>post_money_valuation_usd</th>\n",
              "      <th>post_money_valuation</th>\n",
              "      <th>post_money_valuation_currency_code</th>\n",
              "      <th>investor_count</th>\n",
              "      <th>org_uuid</th>\n",
              "      <th>org_name</th>\n",
              "      <th>lead_investor_uuids</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>014ffe52-1a01-2482-5879-8db9d32ad766</td>\n",
              "      <td>Venture Round - Conservus International</td>\n",
              "      <td>funding_round</td>\n",
              "      <td>conservus-international-series-unknown--014ffe52</td>\n",
              "      <td>https://www.crunchbase.com/funding_round/conse...</td>\n",
              "      <td>122572.00</td>\n",
              "      <td>2009-06-24 21:20:45</td>\n",
              "      <td>2018-02-12 23:43:54</td>\n",
              "      <td>ARE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2f0a2474-bd62-744e-9868-74ced3e3f1a7</td>\n",
              "      <td>Conservus International</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2efa56df-ae64-d329-14de-436bbf751d2d</td>\n",
              "      <td>Venture Round - Pulse Technologies</td>\n",
              "      <td>funding_round</td>\n",
              "      <td>pulse-technologies-series-unknown--2efa56df</td>\n",
              "      <td>https://www.crunchbase.com/funding_round/pulse...</td>\n",
              "      <td>339418.00</td>\n",
              "      <td>2009-06-25 01:08:36</td>\n",
              "      <td>2018-02-12 23:54:11</td>\n",
              "      <td>ARE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>8d302824-3ea2-a8d4-c14f-e723bcb7196d</td>\n",
              "      <td>Pulse Technologies</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38661846-a04d-f823-b022-0a986b3cb54d</td>\n",
              "      <td>Series A - Content Syndicate: Words on Demand</td>\n",
              "      <td>funding_round</td>\n",
              "      <td>content-syndicate-words-on-demand-series-a--38...</td>\n",
              "      <td>https://www.crunchbase.com/funding_round/conte...</td>\n",
              "      <td>358618.00</td>\n",
              "      <td>2009-07-01 07:38:51</td>\n",
              "      <td>2018-02-12 23:39:14</td>\n",
              "      <td>ARE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1500000.00</td>\n",
              "      <td>1500000.00</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64122399-5a8a-b208-430a-f37c1e987058</td>\n",
              "      <td>Content Syndicate: Words on Demand</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20873098-a93d-9c43-2b8b-82e765552d4d</td>\n",
              "      <td>Venture Round - NeuString</td>\n",
              "      <td>funding_round</td>\n",
              "      <td>neustring-series-unknown--20873098</td>\n",
              "      <td>https://www.crunchbase.com/funding_round/neust...</td>\n",
              "      <td>269521.00</td>\n",
              "      <td>2009-11-18 04:32:22</td>\n",
              "      <td>2018-02-12 22:28:33</td>\n",
              "      <td>ARE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>9dcbe645-1f9e-35c4-7d35-03642e9eae82</td>\n",
              "      <td>NeuString</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>da1838e2-7312-a5b2-e9ee-e8806e6c4a93</td>\n",
              "      <td>Venture Round - Maktoob</td>\n",
              "      <td>funding_round</td>\n",
              "      <td>maktoob-series-unknown--da1838e2</td>\n",
              "      <td>https://www.crunchbase.com/funding_round/makto...</td>\n",
              "      <td>333629.00</td>\n",
              "      <td>2010-03-31 11:42:20</td>\n",
              "      <td>2018-02-12 23:34:58</td>\n",
              "      <td>ARE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>5250000.00</td>\n",
              "      <td>5250000.00</td>\n",
              "      <td>USD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>e542dd76-1ee1-5227-2f27-4fb9ecd65fc2</td>\n",
              "      <td>Maktoob</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75d80f4f-79e9-4895-90bd-a3a7a5330004')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-75d80f4f-79e9-4895-90bd-a3a7a5330004 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-75d80f4f-79e9-4895-90bd-a3a7a5330004');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df_funding=pd.read_csv('/content/drive/MyDrive/ISER/datasets/uae/uae_funding_rounds.csv')\n",
        "print(f\"Shape of the dataframe: {df_funding.shape}\")\n",
        "df_funding.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_drop=['type','permalink','cb_url','country_code','created_at','updated_at','state_code','raised_amount_currency_code','raised_amount','post_money_valuation','post_money_valuation_currency_code']\n",
        "df_funding.drop(columns=columns_to_drop,inplace=True)"
      ],
      "metadata": {
        "id": "SeQlXAspllPB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_funding.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "m4ZfUl4uk7jy",
        "outputId": "72f25164-d1b8-405e-ff29-502e92d4b822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           rank  raised_amount_usd  post_money_valuation_usd  investor_count\n",
              "count   1469.00             989.00                    173.00          974.00\n",
              "mean  222772.52        48575802.10              215673303.59            2.03\n",
              "std   143712.17       452669349.76             1598877388.01            1.99\n",
              "min       26.00            1000.00                  52000.00            1.00\n",
              "25%    86059.00          150000.00                1100000.00            1.00\n",
              "50%   212192.00         1000000.00                3600000.00            1.00\n",
              "75%   348113.00         3500000.00               13500000.00            2.00\n",
              "max   476375.00     10000000000.00            20408000000.00           16.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-789cb748-14b6-477d-8c75-ea6bf72bf11d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>raised_amount_usd</th>\n",
              "      <th>post_money_valuation_usd</th>\n",
              "      <th>investor_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1469.00</td>\n",
              "      <td>989.00</td>\n",
              "      <td>173.00</td>\n",
              "      <td>974.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>222772.52</td>\n",
              "      <td>48575802.10</td>\n",
              "      <td>215673303.59</td>\n",
              "      <td>2.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>143712.17</td>\n",
              "      <td>452669349.76</td>\n",
              "      <td>1598877388.01</td>\n",
              "      <td>1.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>26.00</td>\n",
              "      <td>1000.00</td>\n",
              "      <td>52000.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>86059.00</td>\n",
              "      <td>150000.00</td>\n",
              "      <td>1100000.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>212192.00</td>\n",
              "      <td>1000000.00</td>\n",
              "      <td>3600000.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>348113.00</td>\n",
              "      <td>3500000.00</td>\n",
              "      <td>13500000.00</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>476375.00</td>\n",
              "      <td>10000000000.00</td>\n",
              "      <td>20408000000.00</td>\n",
              "      <td>16.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-789cb748-14b6-477d-8c75-ea6bf72bf11d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-789cb748-14b6-477d-8c75-ea6bf72bf11d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-789cb748-14b6-477d-8c75-ea6bf72bf11d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-sSWjY2BU7mm"
      },
      "outputs": [],
      "source": [
        "df_funding['round']=df_funding['name'].apply(lambda x:x.split('-')[0].strip())\n",
        "df_funding['name']=df_funding['name'].apply(lambda x:x.split('-')[1].strip())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_funding.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BUJmMmLj5vrJ",
        "outputId": "af6290dd-4483-4317-de1f-4c4f17e62707"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['uuid', 'name', 'rank', 'region', 'city', 'investment_type',\n",
              "       'announced_on', 'raised_amount_usd', 'post_money_valuation_usd',\n",
              "       'investor_count', 'org_uuid', 'org_name', 'lead_investor_uuids',\n",
              "       'round'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERdUKqwJeITm",
        "outputId": "97f77e49-0f2e-4bad-81d6-b6709435e42e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Venture Round' 'Series A' 'Seed Round' 'Series B' 'Angel Round'\n",
            " 'Series C' 'Private Equity Round' 'Grant' 'Pre Seed Round'\n",
            " 'Funding Round' 'Equity Crowdfunding' 'Convertible Note'\n",
            " 'Non Equity Assistance' 'Post' 'Product Crowdfunding' 'Debt Financing'\n",
            " 'Series D' 'Series E' 'Corporate Round' 'Initial Coin Offering'\n",
            " 'Series F' 'Secondary Market']\n"
          ]
        }
      ],
      "source": [
        "print(df_funding['round'].unique())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Oms47pDMoKvR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_funding.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XzkRT06QzkPT",
        "outputId": "dd0dc266-fe13-4a51-e94c-7eb87b6025cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1470 entries, 0 to 1469\n",
            "Data columns (total 14 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   uuid                      1470 non-null   object \n",
            " 1   name                      1470 non-null   object \n",
            " 2   rank                      1469 non-null   float64\n",
            " 3   region                    1470 non-null   object \n",
            " 4   city                      1470 non-null   object \n",
            " 5   investment_type           1470 non-null   object \n",
            " 6   announced_on              1470 non-null   object \n",
            " 7   raised_amount_usd         989 non-null    float64\n",
            " 8   post_money_valuation_usd  173 non-null    float64\n",
            " 9   investor_count            974 non-null    float64\n",
            " 10  org_uuid                  1470 non-null   object \n",
            " 11  org_name                  1470 non-null   object \n",
            " 12  lead_investor_uuids       572 non-null    object \n",
            " 13  round                     1470 non-null   object \n",
            "dtypes: float64(4), object(10)\n",
            "memory usage: 160.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_funding['Year']=df_funding['announced_on'].apply(lambda x:x.split('-')[0])\n",
        "df_funding['Year'] = df_funding['Year'].astype(int)"
      ],
      "metadata": {
        "id": "sKJO2LC4z6ii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grp = df_funding.groupby('Year')\n",
        "grp.filter(lambda x: len(x) >0)\n",
        "grp['raised_amount_usd'].agg([np.sum,np.mean]).fillna(0).sort_values(by='mean',ascending=False).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "UKQQBEis1GVw",
        "outputId": "14734bf1-f54a-4dee-b598-67b37ad91591"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                sum         mean\n",
              "Year                            \n",
              "2020 20123799370.00 147969113.01\n",
              "2009  1019604028.00 113289336.44\n",
              "2019 10337570508.00  77146048.57\n",
              "2021  5014153040.00  30388806.30\n",
              "2011   231119000.00  28889875.00\n",
              "2015  1562960875.00  25209046.37\n",
              "2022  1330836004.00  24645111.19\n",
              "2018  3262110860.00  23811028.18\n",
              "2014   767721009.00  22580029.68\n",
              "2016  1862528225.00  21912096.76\n",
              "2017  2167766091.00  19887762.30\n",
              "2004    15000000.00  15000000.00\n",
              "2000    55000000.00  13750000.00\n",
              "2010    36670000.00  12223333.33\n",
              "2012   128311184.00   7128399.11\n",
              "2007     5391871.00   5391871.00\n",
              "2006     5250000.00   5250000.00\n",
              "2013   115676212.00   4131293.29\n",
              "2008           0.00         0.00\n",
              "1999           0.00         0.00"
            ],
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              "      <td>113289336.44</td>\n",
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              "      <td>77146048.57</td>\n",
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              "      <td>30388806.30</td>\n",
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              "      <td>28889875.00</td>\n",
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              "      <td>24645111.19</td>\n",
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              "      <td>1862528225.00</td>\n",
              "      <td>21912096.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>2167766091.00</td>\n",
              "      <td>19887762.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>15000000.00</td>\n",
              "      <td>15000000.00</td>\n",
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              "      <th>2000</th>\n",
              "      <td>55000000.00</td>\n",
              "      <td>13750000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>36670000.00</td>\n",
              "      <td>12223333.33</td>\n",
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              "      <th>2012</th>\n",
              "      <td>128311184.00</td>\n",
              "      <td>7128399.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>5391871.00</td>\n",
              "      <td>5391871.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>5250000.00</td>\n",
              "      <td>5250000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>115676212.00</td>\n",
              "      <td>4131293.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grp.count()['uuid']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o64vWI7cE2V4",
        "outputId": "240b5baa-a025-42a4-8c77-93cba82b6bb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year\n",
              "1999      1\n",
              "2000      5\n",
              "2004      1\n",
              "2006      1\n",
              "2007      2\n",
              "2008      2\n",
              "2009     13\n",
              "2010      6\n",
              "2011     12\n",
              "2012     27\n",
              "2013     37\n",
              "2014     48\n",
              "2015     86\n",
              "2016    109\n",
              "2017    148\n",
              "2018    242\n",
              "2019    208\n",
              "2020    205\n",
              "2021    245\n",
              "2022     72\n",
              "Name: uuid, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grp.count()['uuid'].loc[\"1999\":\"2021\"].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "HKfE71PUvTfc",
        "outputId": "595be6ac-9054-4df9-c957-f07e72c6bbb7"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1f5253a50>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(20,15)})\n",
        "sns.countplot(x='round', data=df_funding[df_funding['Year']>2015])\n",
        "plt.xticks(rotation=45)\n",
        "plt.savefig('/content/drive/MyDrive/Upwork_Projects/Nurman_Naz/Plots/rounds.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 967
        },
        "id": "M8VCoE1GZs4i",
        "outputId": "0395efa9-20cc-411a-dd03-d0a7173bdb33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojBb09jYZzmS"
      },
      "source": [
        "**Analyzing investor dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "DmGrDW0bX26L",
        "outputId": "63de8e4a-4a37-45ff-a5a3-803762b556f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   uuid                    name          type  \\\n",
              "0  7415e73b-1b73-75b6-7ccd-561d4a2f104a                    LMKR  organization   \n",
              "1  18ef37c7-67c4-9b11-3fe3-367f94cf980c             KIT Capital  organization   \n",
              "2  85c32657-bd0f-7a62-d7ea-d134db85515c  Levant Capital Limited  organization   \n",
              "3  9c443ec8-21c1-2009-dd24-88517962809e                Bayt.com  organization   \n",
              "4  9256f460-c9d1-1235-edcc-3ba619290b70                 Intilaq  organization   \n",
              "\n",
              "                permalink                                             cb_url  \\\n",
              "0                    lmkr       https://www.crunchbase.com/organization/lmkr   \n",
              "1             kit-capital  https://www.crunchbase.com/organization/kit-ca...   \n",
              "2  levant-capital-limited  https://www.crunchbase.com/organization/levant...   \n",
              "3                bayt-com   https://www.crunchbase.com/organization/bayt-com   \n",
              "4                 intilaq    https://www.crunchbase.com/organization/intilaq   \n",
              "\n",
              "       rank       created_at       updated_at             roles  \\\n",
              "0 240578.00    8/9/2007 8:23  6/24/2019 21:58          investor   \n",
              "1 278382.00   3/25/2008 5:46  6/24/2019 21:36          investor   \n",
              "2 220000.00   4/14/2009 4:43  6/24/2019 22:14          investor   \n",
              "3 119770.00  4/14/2009 21:06  9/24/2020 19:58  investor,company   \n",
              "4 943704.00  4/19/2009 13:48  6/24/2019 21:25          investor   \n",
              "\n",
              "              domain  ... investment_count  total_funding_usd total_funding  \\\n",
              "0           lmkr.com  ...             1.00                NaN           NaN   \n",
              "1     kitcapital.com  ...             3.00                NaN           NaN   \n",
              "2  levantcapital.com  ...             2.00                NaN           NaN   \n",
              "3           bayt.com  ...             1.00                NaN           NaN   \n",
              "4        intilaq.com  ...             5.00                NaN           NaN   \n",
              "\n",
              "  total_funding_currency_code founded_on  closed_on  \\\n",
              "0                         NaN   1/1/1994        NaN   \n",
              "1                         NaN   1/1/2001        NaN   \n",
              "2                         NaN   1/1/2006        NaN   \n",
              "3                         NaN   1/1/2000        NaN   \n",
              "4                         NaN  4/16/2009        NaN   \n",
              "\n",
              "                       facebook_url  \\\n",
              "0  http://www.facebook.com/lmkrnews   \n",
              "1                               NaN   \n",
              "2                               NaN   \n",
              "3  https://www.facebook.com/Baytcom   \n",
              "4                               NaN   \n",
              "\n",
              "                                linkedin_url                  twitter_url  \\\n",
              "0       http://www.linkedin.com/company/lmkr  http://twitter.com/LMKRNews   \n",
              "1                                        NaN                          NaN   \n",
              "2                                        NaN                          NaN   \n",
              "3  https://www.linkedin.com/company/bayt.com  https://twitter.com/Baytcom   \n",
              "4                                        NaN                          NaN   \n",
              "\n",
              "                                            logo_url  \n",
              "0  https://res.cloudinary.com/crunchbase-producti...  \n",
              "1  https://res.cloudinary.com/crunchbase-producti...  \n",
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              "      <th>investment_count</th>\n",
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              "      <th>0</th>\n",
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              "      <td>6/24/2019 21:58</td>\n",
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              "      <td>https://res.cloudinary.com/crunchbase-producti...</td>\n",
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              "      <td>investor</td>\n",
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              "      <td>1/1/2001</td>\n",
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              "      <td>6/24/2019 22:14</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>119770.00</td>\n",
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              "      <th>4</th>\n",
              "      <td>9256f460-c9d1-1235-edcc-3ba619290b70</td>\n",
              "      <td>Intilaq</td>\n",
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              "      <td>943704.00</td>\n",
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              "      <td>6/24/2019 21:25</td>\n",
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          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df_investor=pd.read_csv('/content/drive/MyDrive/ISER/datasets/uae/uae_investors.csv')\n",
        "df_investor.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FV639YYhrSGy"
      },
      "source": [
        "**Analyzing ipo dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "ZEmMJFuSYykn",
        "outputId": "5bfed71c-0e76-4de1-c60a-980bf61948a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   uuid  name type  \\\n",
              "0  a99dd966-7690-4540-0fc7-4aaa17f89fa3   NaN  ipo   \n",
              "1  2a8dd532-f0e6-073b-5891-64952355ca1d   NaN  ipo   \n",
              "2  fb62dcf3-f3dd-2105-57e6-896217d2b22c   NaN  ipo   \n",
              "3  2688cd22-ed16-bd1a-c05e-b2b17f859a38   NaN  ipo   \n",
              "4  0b41978c-5af7-dcbe-e361-db0441b83c3c   NaN  ipo   \n",
              "\n",
              "                                  permalink  \\\n",
              "0                    lamprell-ipo--a99dd966   \n",
              "1                  amirafoods-ipo--2a8dd532   \n",
              "2                         fvc-ipo--fb62dcf3   \n",
              "3                  nmc-health-ipo--2688cd22   \n",
              "4  national-bank-of-abu-dhabi-ipo--0b41978c   \n",
              "\n",
              "                                              cb_url   rank       created_at  \\\n",
              "0  https://www.crunchbase.com/ipo/lamprell-ipo--a...  39334    6/4/2014 0:29   \n",
              "1  https://www.crunchbase.com/ipo/amirafoods-ipo-...  13298   6/11/2014 6:56   \n",
              "2   https://www.crunchbase.com/ipo/fvc-ipo--fb62dcf3  13160    8/4/2014 6:44   \n",
              "3  https://www.crunchbase.com/ipo/nmc-health-ipo-...  11813  11/22/2016 6:56   \n",
              "4  https://www.crunchbase.com/ipo/national-bank-o...  21751  11/22/2016 9:08   \n",
              "\n",
              "        updated_at                              org_uuid  \\\n",
              "0  9/11/2019 18:08  f41a7adb-a028-c6aa-9e03-4ef65f0b3472   \n",
              "1   8/10/2018 9:56  83c002c8-2c83-eb85-5cbb-51eaed4d6b2a   \n",
              "2   7/24/2018 8:53  89084186-c9a3-7740-735f-5f48b891f350   \n",
              "3   8/4/2020 21:18  57dac3cc-2699-0926-9dbc-8388d6e972d6   \n",
              "4   7/10/2018 7:27  a54f15c8-6f46-5bbe-2ff7-bb60f1e74ad6   \n",
              "\n",
              "                     org_name  ... went_public_on share_price_usd  \\\n",
              "0                    Lamprell  ...     10/20/2006             NaN   \n",
              "1          Amira Nature Foods  ...     10/10/2012           10.00   \n",
              "2   First Virtual Corporation  ...      4/29/1998           13.00   \n",
              "3                  NMC Health  ...      4/13/2012             NaN   \n",
              "4  National Bank of Abu Dhabi  ...       1/1/2011             NaN   \n",
              "\n",
              "   share_price share_price_currency_code valuation_price_usd valuation_price  \\\n",
              "0          NaN                       NaN                 NaN             NaN   \n",
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              "2        13.00                       USD        195000000.00    195000000.00   \n",
              "3          NaN                       NaN                 NaN             NaN   \n",
              "4          NaN                       NaN                 NaN             NaN   \n",
              "\n",
              "  valuation_price_currency_code money_raised_usd  money_raised  \\\n",
              "0                           NaN              NaN           NaN   \n",
              "1                           NaN      90000000.00   90000000.00   \n",
              "2                           USD      37400000.00   37400000.00   \n",
              "3                           NaN              NaN           NaN   \n",
              "4                           NaN              NaN           NaN   \n",
              "\n",
              "   money_raised_currency_code  \n",
              "0                         NaN  \n",
              "1                         USD  \n",
              "2                         USD  \n",
              "3                         NaN  \n",
              "4                         NaN  \n",
              "\n",
              "[5 rows x 27 columns]"
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              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2688cd22-ed16-bd1a-c05e-b2b17f859a38</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ipo</td>\n",
              "      <td>nmc-health-ipo--2688cd22</td>\n",
              "      <td>https://www.crunchbase.com/ipo/nmc-health-ipo-...</td>\n",
              "      <td>11813</td>\n",
              "      <td>11/22/2016 6:56</td>\n",
              "      <td>8/4/2020 21:18</td>\n",
              "      <td>57dac3cc-2699-0926-9dbc-8388d6e972d6</td>\n",
              "      <td>NMC Health</td>\n",
              "      <td>...</td>\n",
              "      <td>4/13/2012</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0b41978c-5af7-dcbe-e361-db0441b83c3c</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ipo</td>\n",
              "      <td>national-bank-of-abu-dhabi-ipo--0b41978c</td>\n",
              "      <td>https://www.crunchbase.com/ipo/national-bank-o...</td>\n",
              "      <td>21751</td>\n",
              "      <td>11/22/2016 9:08</td>\n",
              "      <td>7/10/2018 7:27</td>\n",
              "      <td>a54f15c8-6f46-5bbe-2ff7-bb60f1e74ad6</td>\n",
              "      <td>National Bank of Abu Dhabi</td>\n",
              "      <td>...</td>\n",
              "      <td>1/1/2011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd67295c-bf7d-41ab-b729-4bb5cda4374a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df_ipo=pd.read_csv('/content/drive/MyDrive/ISER/datasets/uae/uae_ipos.csv')\n",
        "df_ipo.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ipo.columns #['org_name','org_uuid','city','went_public_on','share_price_usd','valuation_price_usd','money_raised_usd','stock_symbol']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KYldMXU16D7",
        "outputId": "56537eed-3ecf-4fb0-c4bb-5dc024938880"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['uuid', 'name', 'type', 'permalink', 'cb_url', 'rank', 'created_at',\n",
              "       'updated_at', 'org_uuid', 'org_name', 'org_cb_url', 'country_code',\n",
              "       'state_code', 'region', 'city', 'stock_exchange_symbol', 'stock_symbol',\n",
              "       'went_public_on', 'share_price_usd', 'share_price',\n",
              "       'share_price_currency_code', 'valuation_price_usd', 'valuation_price',\n",
              "       'valuation_price_currency_code', 'money_raised_usd', 'money_raised',\n",
              "       'money_raised_currency_code'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6KbNEegbrT4w"
      },
      "source": [
        "**Analyzing organization dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "QhnmaIgPZMOB",
        "outputId": "2a131eda-ee7a-4ff1-daa3-adf7f72984bf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   uuid         name          type  \\\n",
              "0  7415e73b-1b73-75b6-7ccd-561d4a2f104a         LMKR  organization   \n",
              "1  e542dd76-1ee1-5227-2f27-4fb9ecd65fc2      Maktoob  organization   \n",
              "2  40d9f85e-c7bd-e197-ae77-470fdb18506c        Darrb  organization   \n",
              "3  18ef37c7-67c4-9b11-3fe3-367f94cf980c  KIT Capital  organization   \n",
              "4  43200fc5-0fd6-8dd4-17ff-ec7c15976ecd  TagLauncher  organization   \n",
              "\n",
              "     permalink                                             cb_url       rank  \\\n",
              "0         lmkr       https://www.crunchbase.com/organization/lmkr  233641.00   \n",
              "1      maktoob    https://www.crunchbase.com/organization/maktoob  181782.00   \n",
              "2        darrb      https://www.crunchbase.com/organization/darrb 1070733.00   \n",
              "3  kit-capital  https://www.crunchbase.com/organization/kit-ca...  268163.00   \n",
              "4  taglauncher  https://www.crunchbase.com/organization/taglau... 1468682.00   \n",
              "\n",
              "            created_at           updated_at legal_name     roles  ...  \\\n",
              "0  2007-08-09 08:23:20  2019-06-24 21:58:04        NaN  investor  ...   \n",
              "1  2008-02-19 03:16:18  2020-12-29 10:51:32        NaN   company  ...   \n",
              "2  2008-03-15 12:14:11  2019-06-24 22:16:01        NaN   company  ...   \n",
              "3  2008-03-25 05:46:28  2019-06-24 21:36:43        NaN  investor  ...   \n",
              "4  2008-04-02 13:11:46  2019-06-24 21:46:58        NaN   company  ...   \n",
              "\n",
              "             phone                            facebook_url  \\\n",
              "0     713.650.3200        http://www.facebook.com/lmkrnews   \n",
              "1  +1 408-349-3300  https://www.facebook.com/maktoobsports   \n",
              "2              NaN                                     NaN   \n",
              "3     646 502 7493                                     NaN   \n",
              "4              NaN                                     NaN   \n",
              "\n",
              "                           linkedin_url                      twitter_url  \\\n",
              "0  http://www.linkedin.com/company/lmkr      http://twitter.com/LMKRNews   \n",
              "1                                   NaN  https://twitter.com/yahoonewsme   \n",
              "2                                   NaN                              NaN   \n",
              "3                                   NaN                              NaN   \n",
              "4                                   NaN                              NaN   \n",
              "\n",
              "                                            logo_url alias1 alias2 alias3  \\\n",
              "0  https://res.cloudinary.com/crunchbase-producti...    NaN    NaN    NaN   \n",
              "1  https://res.cloudinary.com/crunchbase-producti...    NaN    NaN    NaN   \n",
              "2  https://res.cloudinary.com/crunchbase-producti...    NaN    NaN    NaN   \n",
              "3  https://res.cloudinary.com/crunchbase-producti...    NaN    NaN    NaN   \n",
              "4  https://res.cloudinary.com/crunchbase-producti...    NaN    NaN    NaN   \n",
              "\n",
              "  primary_role num_exits  \n",
              "0     investor       NaN  \n",
              "1      company       NaN  \n",
              "2      company       NaN  \n",
              "3     investor      2.00  \n",
              "4      company       NaN  \n",
              "\n",
              "[5 rows x 41 columns]"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>uuid</th>\n",
              "      <th>name</th>\n",
              "      <th>type</th>\n",
              "      <th>permalink</th>\n",
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              "      <th>num_exits</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>7415e73b-1b73-75b6-7ccd-561d4a2f104a</td>\n",
              "      <td>LMKR</td>\n",
              "      <td>organization</td>\n",
              "      <td>lmkr</td>\n",
              "      <td>https://www.crunchbase.com/organization/lmkr</td>\n",
              "      <td>233641.00</td>\n",
              "      <td>2007-08-09 08:23:20</td>\n",
              "      <td>2019-06-24 21:58:04</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>713.650.3200</td>\n",
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              "      <td>https://res.cloudinary.com/crunchbase-producti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>investor</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>e542dd76-1ee1-5227-2f27-4fb9ecd65fc2</td>\n",
              "      <td>Maktoob</td>\n",
              "      <td>organization</td>\n",
              "      <td>maktoob</td>\n",
              "      <td>https://www.crunchbase.com/organization/maktoob</td>\n",
              "      <td>181782.00</td>\n",
              "      <td>2008-02-19 03:16:18</td>\n",
              "      <td>2020-12-29 10:51:32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>company</td>\n",
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              "      <td>+1 408-349-3300</td>\n",
              "      <td>https://www.facebook.com/maktoobsports</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://twitter.com/yahoonewsme</td>\n",
              "      <td>https://res.cloudinary.com/crunchbase-producti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>company</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>40d9f85e-c7bd-e197-ae77-470fdb18506c</td>\n",
              "      <td>Darrb</td>\n",
              "      <td>organization</td>\n",
              "      <td>darrb</td>\n",
              "      <td>https://www.crunchbase.com/organization/darrb</td>\n",
              "      <td>1070733.00</td>\n",
              "      <td>2008-03-15 12:14:11</td>\n",
              "      <td>2019-06-24 22:16:01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>company</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://res.cloudinary.com/crunchbase-producti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>company</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18ef37c7-67c4-9b11-3fe3-367f94cf980c</td>\n",
              "      <td>KIT Capital</td>\n",
              "      <td>organization</td>\n",
              "      <td>kit-capital</td>\n",
              "      <td>https://www.crunchbase.com/organization/kit-ca...</td>\n",
              "      <td>268163.00</td>\n",
              "      <td>2008-03-25 05:46:28</td>\n",
              "      <td>2019-06-24 21:36:43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>investor</td>\n",
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              "      <td>646 502 7493</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://res.cloudinary.com/crunchbase-producti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>investor</td>\n",
              "      <td>2.00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>43200fc5-0fd6-8dd4-17ff-ec7c15976ecd</td>\n",
              "      <td>TagLauncher</td>\n",
              "      <td>organization</td>\n",
              "      <td>taglauncher</td>\n",
              "      <td>https://www.crunchbase.com/organization/taglau...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>https://res.cloudinary.com/crunchbase-producti...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>company</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 41 columns</p>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-748e61f7-bd15-470b-9919-c04713fc047b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-748e61f7-bd15-470b-9919-c04713fc047b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df_organization=pd.read_csv('/content/drive/MyDrive/ISER/datasets/uae/uae_organizations.csv')\n",
        "df_organization.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHQ08kyOdkAc"
      },
      "source": [
        "**Starting operations**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "org_columns=['uuid','name','roles','category_groups_list','total_funding_usd','founded_on','employee_count']"
      ],
      "metadata": {
        "id": "u5IoXMIWstYd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.merge(df_organization[org_columns],df_funding, how='inner',left_on='uuid', right_on='org_uuid')"
      ],
      "metadata": {
        "id": "Wp-l2lsdrgMC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['region','uuid_y','name_y','uuid_x','name_x'],inplace=True)"
      ],
      "metadata": {
        "id": "8zaFFW2KwqyP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCjlZhz8xaOh",
        "outputId": "fa8ebafb-9e2a-472d-ba1f-e74ca16a55bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['roles', 'category_groups_list', 'total_funding_usd', 'founded_on',\n",
              "       'employee_count', 'rank', 'city', 'investment_type', 'announced_on',\n",
              "       'raised_amount_usd', 'post_money_valuation_usd', 'investor_count',\n",
              "       'org_uuid', 'org_name', 'lead_investor_uuids', 'round', 'Year'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "nM1xuRxdxCfO",
        "outputId": "d06618f2-1a49-42ed-ca2e-47064370196a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     roles                               category_groups_list  \\\n",
              "0  company  Gaming,Internet Services,Media and Entertainme...   \n",
              "1  company            Commerce and Shopping,Internet Services   \n",
              "2  company            Commerce and Shopping,Internet Services   \n",
              "3  company                    Advertising,Sales and Marketing   \n",
              "4  company                      Hardware,Real Estate,Software   \n",
              "\n",
              "   total_funding_usd  founded_on employee_count      rank   city  \\\n",
              "0         5250000.00  2000-01-01         10000+ 333629.00  Dubai   \n",
              "1          200000.00  2017-05-11          11-50 143263.00  Dubai   \n",
              "2          200000.00  2017-05-11          11-50 270672.00  Dubai   \n",
              "3               0.00  2004-01-01          11-50 122572.00  Dubai   \n",
              "4               0.00  2008-01-01        unknown 339418.00  Dubai   \n",
              "\n",
              "  investment_type announced_on  raised_amount_usd  post_money_valuation_usd  \\\n",
              "0  series_unknown   2006-01-01         5250000.00                       NaN   \n",
              "1            seed   2019-01-01                NaN                       NaN   \n",
              "2        pre_seed   2017-08-01          200000.00                       NaN   \n",
              "3  series_unknown   2009-02-18                NaN                       NaN   \n",
              "4  series_unknown   2009-02-18                NaN                       NaN   \n",
              "\n",
              "   investor_count                              org_uuid  \\\n",
              "0            1.00  e542dd76-1ee1-5227-2f27-4fb9ecd65fc2   \n",
              "1            1.00  d0fd55c6-31bf-3afe-4346-a4d617c874df   \n",
              "2             NaN  d0fd55c6-31bf-3afe-4346-a4d617c874df   \n",
              "3            1.00  2f0a2474-bd62-744e-9868-74ced3e3f1a7   \n",
              "4            1.00  8d302824-3ea2-a8d4-c14f-e723bcb7196d   \n",
              "\n",
              "                     org_name lead_investor_uuids           round  Year  \n",
              "0                     Maktoob                 NaN   Venture Round  2006  \n",
              "1  Qareeb Technologies FZ-LLC                 NaN      Seed Round  2019  \n",
              "2  Qareeb Technologies FZ-LLC                 NaN  Pre Seed Round  2017  \n",
              "3     Conservus International                 NaN   Venture Round  2009  \n",
              "4          Pulse Technologies                 NaN   Venture Round  2009  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dcf8bb18-ecf5-4957-a78a-76419ac447d0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th>round</th>\n",
              "      <th>Year</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>company</td>\n",
              "      <td>Gaming,Internet Services,Media and Entertainme...</td>\n",
              "      <td>5250000.00</td>\n",
              "      <td>2000-01-01</td>\n",
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              "      <td>143263.00</td>\n",
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              "      <td>seed</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "      <td>d0fd55c6-31bf-3afe-4346-a4d617c874df</td>\n",
              "      <td>Qareeb Technologies FZ-LLC</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>company</td>\n",
              "      <td>Commerce and Shopping,Internet Services</td>\n",
              "      <td>200000.00</td>\n",
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              "      <td>d0fd55c6-31bf-3afe-4346-a4d617c874df</td>\n",
              "      <td>Qareeb Technologies FZ-LLC</td>\n",
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              "      <td>Pre Seed Round</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>company</td>\n",
              "      <td>Advertising,Sales and Marketing</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2004-01-01</td>\n",
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              "      <td>122572.00</td>\n",
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              "      <td>Conservus International</td>\n",
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              "      <td>339418.00</td>\n",
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              "      <td>Pulse Technologies</td>\n",
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              "      <td>2009</td>\n",
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              "  </tbody>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['category_groups_list'] = df['category_groups_list'].fillna('other')"
      ],
      "metadata": {
        "id": "QftGkZz603Q0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_category(x):\n",
        "  s=x.split(',')[0].lower().strip()\n",
        "  if(s=='other'):\n",
        "    try:\n",
        "      s=x.split(',')[1].lower().strip()\n",
        "    except:\n",
        "      s='other'\n",
        "  return s"
      ],
      "metadata": {
        "id": "MXIRzFl1yI8v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['roles'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDxu9a_wxg_f",
        "outputId": "b89cdc71-fa62-4f6b-a74b-65d1b0dca571"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['company', 'investor,company', 'investor', 'school', nan,\n",
              "       'company,investor'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['category']=df['category_groups_list'].apply(compute_category)\n"
      ],
      "metadata": {
        "id": "2w8ZnO_I0Scy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df['category'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wo_tZauhxtX6",
        "outputId": "b611f6c3-40b0-44b3-b853-0eb18351fd93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['category'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AVssF4fV6yjU",
        "outputId": "281b6c5a-88e5-4d1c-e6af-929d19507407"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "commerce and shopping      298\n",
              "financial services         227\n",
              "apps                       137\n",
              "information technology      80\n",
              "administrative services     65\n",
              "education                   57\n",
              "other                       49\n",
              "health care                 48\n",
              "community and lifestyle     46\n",
              "artificial intelligence     46\n",
              "transportation              38\n",
              "advertising                 37\n",
              "clothing and apparel        28\n",
              "energy                      27\n",
              "food and beverage           25\n",
              "internet services           25\n",
              "data and analytics          23\n",
              "consumer electronics        21\n",
              "agriculture and farming     21\n",
              "events                      20\n",
              "real estate                 19\n",
              "software                    17\n",
              "hardware                    16\n",
              "media and entertainment     15\n",
              "professional services       13\n",
              "content and publishing      13\n",
              "travel and tourism          10\n",
              "manufacturing                9\n",
              "biotechnology                7\n",
              "consumer goods               7\n",
              "design                       6\n",
              "sales and marketing          4\n",
              "gaming                       4\n",
              "sustainability               3\n",
              "sports                       3\n",
              "government and military      2\n",
              "mobile                       2\n",
              "privacy and security         1\n",
              "science and engineering      1\n",
              "Name: category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['category_groups_list'],inplace=True)"
      ],
      "metadata": {
        "id": "zIDfYiRt7rKM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['employee_count'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hGIrO9S8FzH",
        "outputId": "860b1635-552d-4a3a-a97b-4e695ad6e5f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11-50         581\n",
              "1-10          480\n",
              "51-100        135\n",
              "101-250       115\n",
              "unknown        47\n",
              "1001-5000      37\n",
              "251-500        34\n",
              "501-1000       19\n",
              "10000+         16\n",
              "5001-10000      6\n",
              "Name: employee_count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def employee_count(s):\n",
        "  if(s=='10000+'):\n",
        "    return 10000\n",
        "  elif(s=='unknown'):\n",
        "    return 0\n",
        "  else:\n",
        "    return int(s.split('-')[1])-int(s.split('-')[0])"
      ],
      "metadata": {
        "id": "HjkaNqTr-f3t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['employee_count']=df['employee_count'].apply(employee_count)"
      ],
      "metadata": {
        "id": "Se5rCtPx_gnd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dO8IvTtmA-Wc",
        "outputId": "5cbc1e00-8d04-4f70-b735-ed69f86da322"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1470 entries, 0 to 1469\n",
            "Data columns (total 17 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   roles                     1463 non-null   object \n",
            " 1   total_funding_usd         1470 non-null   float64\n",
            " 2   founded_on                1447 non-null   object \n",
            " 3   employee_count            1470 non-null   int64  \n",
            " 4   rank                      1469 non-null   float64\n",
            " 5   city                      1470 non-null   object \n",
            " 6   investment_type           1470 non-null   object \n",
            " 7   announced_on              1470 non-null   object \n",
            " 8   raised_amount_usd         989 non-null    float64\n",
            " 9   post_money_valuation_usd  173 non-null    float64\n",
            " 10  investor_count            974 non-null    float64\n",
            " 11  org_uuid                  1470 non-null   object \n",
            " 12  org_name                  1470 non-null   object \n",
            " 13  lead_investor_uuids       572 non-null    object \n",
            " 14  round                     1470 non-null   object \n",
            " 15  Year                      1470 non-null   int64  \n",
            " 16  category                  1470 non-null   object \n",
            "dtypes: float64(5), int64(2), object(10)\n",
            "memory usage: 206.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['founded_on'].fillna('2022-01-01',inplace=True)"
      ],
      "metadata": {
        "id": "yn91Xc_UH0Q8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['age']=df['founded_on'].apply(lambda x:2022-int(x.split('-')[0]))"
      ],
      "metadata": {
        "id": "_DO7_7h5D60Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['founded_on'],inplace=True)"
      ],
      "metadata": {
        "id": "6qLtnCdFIo4q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['post_money_valuation_usd'].fillna(0,inplace=True)"
      ],
      "metadata": {
        "id": "gNQ0kIYAKKgf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947
        },
        "id": "7eNRyXshI2Wj",
        "outputId": "68bad133-bb7a-4af5-e7bb-406878b2c72b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     roles  total_funding_usd  employee_count      rank       city  \\\n",
              "0  company         5250000.00           10000 333629.00      Dubai   \n",
              "1  company          200000.00              39 143263.00      Dubai   \n",
              "2  company          200000.00              39 270672.00      Dubai   \n",
              "3  company               0.00              39 122572.00      Dubai   \n",
              "4  company               0.00               0 339418.00      Dubai   \n",
              "5  company        14000000.00             249 453092.00  Abu Dhabi   \n",
              "6  company         1500000.00              39 358618.00      Dubai   \n",
              "7  company               0.00               9 269521.00      Dubai   \n",
              "8  company               0.00              39 254071.00      Dubai   \n",
              "9  company       326700000.00             499   8137.00  Abu Dhabi   \n",
              "\n",
              "   investment_type announced_on  raised_amount_usd  post_money_valuation_usd  \\\n",
              "0   series_unknown   2006-01-01         5250000.00                      0.00   \n",
              "1             seed   2019-01-01                NaN                      0.00   \n",
              "2         pre_seed   2017-08-01          200000.00                      0.00   \n",
              "3   series_unknown   2009-02-18                NaN                      0.00   \n",
              "4   series_unknown   2009-02-18                NaN                      0.00   \n",
              "5   series_unknown   2009-10-18        14000000.00                      0.00   \n",
              "6         series_a   2009-01-05         1500000.00                      0.00   \n",
              "7   series_unknown   2009-11-17                NaN                      0.00   \n",
              "8         series_a   2010-06-23                NaN                      0.00   \n",
              "9  corporate_round   2022-02-08       326700000.00             4438700000.00   \n",
              "\n",
              "   investor_count                              org_uuid  \\\n",
              "0            1.00  e542dd76-1ee1-5227-2f27-4fb9ecd65fc2   \n",
              "1            1.00  d0fd55c6-31bf-3afe-4346-a4d617c874df   \n",
              "2             NaN  d0fd55c6-31bf-3afe-4346-a4d617c874df   \n",
              "3            1.00  2f0a2474-bd62-744e-9868-74ced3e3f1a7   \n",
              "4            1.00  8d302824-3ea2-a8d4-c14f-e723bcb7196d   \n",
              "5             NaN  67fd983c-50bc-18b5-1040-c5b3cae816c2   \n",
              "6             NaN  64122399-5a8a-b208-430a-f37c1e987058   \n",
              "7            1.00  9dcbe645-1f9e-35c4-7d35-03642e9eae82   \n",
              "8            3.00  c615bcf8-d0cb-4c92-5da9-13b0c723cc5e   \n",
              "9            1.00  172af149-ed34-0a1c-df0d-ccc92979ff9f   \n",
              "\n",
              "                             org_name                   lead_investor_uuids  \\\n",
              "0                             Maktoob                                   NaN   \n",
              "1          Qareeb Technologies FZ-LLC                                   NaN   \n",
              "2          Qareeb Technologies FZ-LLC                                   NaN   \n",
              "3             Conservus International                                   NaN   \n",
              "4                  Pulse Technologies                                   NaN   \n",
              "5                      Sakhr Software                                   NaN   \n",
              "6  Content Syndicate: Words on Demand                                   NaN   \n",
              "7                           NeuString                                   NaN   \n",
              "8                             GoNabit                                   NaN   \n",
              "9             Abu Dhabi Ports Company  bea75425-8396-4140-93cc-ab284312d2f2   \n",
              "\n",
              "             round  Year                category  age  \n",
              "0    Venture Round  2006                  gaming   22  \n",
              "1       Seed Round  2019   commerce and shopping    5  \n",
              "2   Pre Seed Round  2017   commerce and shopping    5  \n",
              "3    Venture Round  2009             advertising   18  \n",
              "4    Venture Round  2009                hardware   14  \n",
              "5    Venture Round  2009    consumer electronics   40  \n",
              "6         Series A  2009  content and publishing   15  \n",
              "7    Venture Round  2009      data and analytics   14  \n",
              "8         Series A  2010   commerce and shopping   12  \n",
              "9  Corporate Round  2022           manufacturing   16  "
            ],
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              "\n",
              "  <div id=\"df-88387c48-bd95-488c-a0c4-b8c5ac4e2a2e\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>roles</th>\n",
              "      <th>total_funding_usd</th>\n",
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              "      <th>round</th>\n",
              "      <th>Year</th>\n",
              "      <th>category</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>company</td>\n",
              "      <td>5250000.00</td>\n",
              "      <td>10000</td>\n",
              "      <td>333629.00</td>\n",
              "      <td>Dubai</td>\n",
              "      <td>series_unknown</td>\n",
              "      <td>2006-01-01</td>\n",
              "      <td>5250000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>e542dd76-1ee1-5227-2f27-4fb9ecd65fc2</td>\n",
              "      <td>Maktoob</td>\n",
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              "      <td>Venture Round</td>\n",
              "      <td>2006</td>\n",
              "      <td>gaming</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>company</td>\n",
              "      <td>200000.00</td>\n",
              "      <td>39</td>\n",
              "      <td>143263.00</td>\n",
              "      <td>Dubai</td>\n",
              "      <td>seed</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>d0fd55c6-31bf-3afe-4346-a4d617c874df</td>\n",
              "      <td>Qareeb Technologies FZ-LLC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Seed Round</td>\n",
              "      <td>2019</td>\n",
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              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>company</td>\n",
              "      <td>200000.00</td>\n",
              "      <td>39</td>\n",
              "      <td>270672.00</td>\n",
              "      <td>Dubai</td>\n",
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              "      <td>Qareeb Technologies FZ-LLC</td>\n",
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              "      <td>Pre Seed Round</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>39</td>\n",
              "      <td>122572.00</td>\n",
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              "      <td>series_unknown</td>\n",
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              "      <td>Pulse Technologies</td>\n",
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              "      <td>Venture Round</td>\n",
              "      <td>2009</td>\n",
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              "      <td>14</td>\n",
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              "      <th>5</th>\n",
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              "      <td>453092.00</td>\n",
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              "      <td>358618.00</td>\n",
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              "      <th>8</th>\n",
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              "      <td>254071.00</td>\n",
              "      <td>Dubai</td>\n",
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              "      <td>3.00</td>\n",
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              "      <th>9</th>\n",
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              "      <td>Corporate Round</td>\n",
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              "      <td>16</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-88387c48-bd95-488c-a0c4-b8c5ac4e2a2e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('/content/drive/MyDrive/Upwork_Projects/Nurman_Naz/Datasets/processed.csv')"
      ],
      "metadata": {
        "id": "WvOYFdyBLgmF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df=pd.read_csv('/content/drive/MyDrive/Upwork_Projects/Nurman_Naz/Datasets/processed.csv')"
      ],
      "metadata": {
        "id": "3lkGJ_1EitTj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categories=df['category'].unique()"
      ],
      "metadata": {
        "id": "_29QETrSKO_I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['category']=='other']['post_money_valuation_usd'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLejUnDVR_9O",
        "outputId": "b6414f1f-9f54-4c94-dc6b-bd9292dbb694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2387399.3469387754"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_funding,employee_count,post_money_valuation,investor_count,age=[],[],[],[],[]\n",
        "for c in categories:\n",
        "  total_funding.append(df[df['category']==c]['total_funding_usd'].mean())\n",
        "  employee_count.append(df[df['category']==c]['employee_count'].mean())\n",
        "  post_money_valuation.append(df[df['category']==c]['post_money_valuation_usd'].mean())\n",
        "  investor_count.append(df[df['category']==c]['investor_count'].mean())\n",
        "  age.append(df[df['category']==c]['age'].mean())"
      ],
      "metadata": {
        "id": "FQUQ_Z-TI-6K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dict_category={\n",
        "    'org_category':categories,\n",
        "    'mean_funding':total_funding,\n",
        "    'mean_employee':employee_count,\n",
        "    'mean_post_money_valuation':post_money_valuation,\n",
        "    'mean_investors':investor_count,\n",
        "    'mean_age':age\n",
        "}"
      ],
      "metadata": {
        "id": "s450d41HSUYm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "category_analysis=pd.DataFrame(dict_category)"
      ],
      "metadata": {
        "id": "BThVXVjJTqHj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "category_analysis.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "MbyrX8ZkURpF",
        "outputId": "acc6aeaa-7e20-4eff-fcf8-b17ef9b37b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               org_category  mean_funding  mean_employee  \\\n",
              "0                    gaming    1514882.50        2522.00   \n",
              "1     commerce and shopping   26976191.21         275.61   \n",
              "2               advertising    1990337.30          28.19   \n",
              "3                  hardware     171630.56          83.50   \n",
              "4      consumer electronics    4219619.05          49.00   \n",
              "5    content and publishing   23432932.54          62.92   \n",
              "6        data and analytics    1074847.74          26.39   \n",
              "7             manufacturing   42668020.44        1253.56   \n",
              "8        financial services   44318765.28         106.59   \n",
              "9               real estate  110155370.47         278.05   \n",
              "10            biotechnology   30142857.14          29.00   \n",
              "11                 software    3746013.24          61.94   \n",
              "12                     apps   44431480.69          36.18   \n",
              "13                   events    2543571.95          64.50   \n",
              "14     clothing and apparel    7067000.00         439.11   \n",
              "15  community and lifestyle    8789394.83         343.80   \n",
              "16                education   20904456.63         111.14   \n",
              "17    professional services  415590000.00        2166.69   \n",
              "18        internet services    1527705.56          56.68   \n",
              "19   information technology   10819906.64          47.41   \n",
              "\n",
              "    mean_post_money_valuation  mean_investors  mean_age  \n",
              "0                        0.00            1.33      9.50  \n",
              "1                 10697205.39            2.12      7.22  \n",
              "2                    11621.62            1.46      9.14  \n",
              "3                   187500.00            1.11     12.00  \n",
              "4                  2571428.57            1.50      9.29  \n",
              "5                   419326.15            1.43      7.00  \n",
              "6                   319260.87            1.86      6.52  \n",
              "7                493188888.89            2.71     25.78  \n",
              "8                  2371394.27            2.63      5.63  \n",
              "9                 61069469.79            1.29     10.16  \n",
              "10                19428571.43            1.71      9.14  \n",
              "11                       0.00            1.55      6.18  \n",
              "12                 1010383.38            2.04      5.72  \n",
              "13                   30000.15            1.90      8.60  \n",
              "14                 4464285.71            1.64     13.04  \n",
              "15                  790652.17            1.78      8.09  \n",
              "16                 1018615.58            1.66      5.70  \n",
              "17               323076923.08            3.82      7.15  \n",
              "18                  259600.00            1.42      6.96  \n",
              "19                  511939.72            1.55      7.08  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-96b38141-5ca8-4caa-897b-d1566d2a9615\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>org_category</th>\n",
              "      <th>mean_funding</th>\n",
              "      <th>mean_employee</th>\n",
              "      <th>mean_post_money_valuation</th>\n",
              "      <th>mean_investors</th>\n",
              "      <th>mean_age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>gaming</td>\n",
              "      <td>1514882.50</td>\n",
              "      <td>2522.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.33</td>\n",
              "      <td>9.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>commerce and shopping</td>\n",
              "      <td>26976191.21</td>\n",
              "      <td>275.61</td>\n",
              "      <td>10697205.39</td>\n",
              "      <td>2.12</td>\n",
              "      <td>7.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>advertising</td>\n",
              "      <td>1990337.30</td>\n",
              "      <td>28.19</td>\n",
              "      <td>11621.62</td>\n",
              "      <td>1.46</td>\n",
              "      <td>9.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hardware</td>\n",
              "      <td>171630.56</td>\n",
              "      <td>83.50</td>\n",
              "      <td>187500.00</td>\n",
              "      <td>1.11</td>\n",
              "      <td>12.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>consumer electronics</td>\n",
              "      <td>4219619.05</td>\n",
              "      <td>49.00</td>\n",
              "      <td>2571428.57</td>\n",
              "      <td>1.50</td>\n",
              "      <td>9.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>content and publishing</td>\n",
              "      <td>23432932.54</td>\n",
              "      <td>62.92</td>\n",
              "      <td>419326.15</td>\n",
              "      <td>1.43</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>data and analytics</td>\n",
              "      <td>1074847.74</td>\n",
              "      <td>26.39</td>\n",
              "      <td>319260.87</td>\n",
              "      <td>1.86</td>\n",
              "      <td>6.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>manufacturing</td>\n",
              "      <td>42668020.44</td>\n",
              "      <td>1253.56</td>\n",
              "      <td>493188888.89</td>\n",
              "      <td>2.71</td>\n",
              "      <td>25.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>financial services</td>\n",
              "      <td>44318765.28</td>\n",
              "      <td>106.59</td>\n",
              "      <td>2371394.27</td>\n",
              "      <td>2.63</td>\n",
              "      <td>5.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>real estate</td>\n",
              "      <td>110155370.47</td>\n",
              "      <td>278.05</td>\n",
              "      <td>61069469.79</td>\n",
              "      <td>1.29</td>\n",
              "      <td>10.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>biotechnology</td>\n",
              "      <td>30142857.14</td>\n",
              "      <td>29.00</td>\n",
              "      <td>19428571.43</td>\n",
              "      <td>1.71</td>\n",
              "      <td>9.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>software</td>\n",
              "      <td>3746013.24</td>\n",
              "      <td>61.94</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>6.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>apps</td>\n",
              "      <td>44431480.69</td>\n",
              "      <td>36.18</td>\n",
              "      <td>1010383.38</td>\n",
              "      <td>2.04</td>\n",
              "      <td>5.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>events</td>\n",
              "      <td>2543571.95</td>\n",
              "      <td>64.50</td>\n",
              "      <td>30000.15</td>\n",
              "      <td>1.90</td>\n",
              "      <td>8.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>clothing and apparel</td>\n",
              "      <td>7067000.00</td>\n",
              "      <td>439.11</td>\n",
              "      <td>4464285.71</td>\n",
              "      <td>1.64</td>\n",
              "      <td>13.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>community and lifestyle</td>\n",
              "      <td>8789394.83</td>\n",
              "      <td>343.80</td>\n",
              "      <td>790652.17</td>\n",
              "      <td>1.78</td>\n",
              "      <td>8.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>education</td>\n",
              "      <td>20904456.63</td>\n",
              "      <td>111.14</td>\n",
              "      <td>1018615.58</td>\n",
              "      <td>1.66</td>\n",
              "      <td>5.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>professional services</td>\n",
              "      <td>415590000.00</td>\n",
              "      <td>2166.69</td>\n",
              "      <td>323076923.08</td>\n",
              "      <td>3.82</td>\n",
              "      <td>7.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>internet services</td>\n",
              "      <td>1527705.56</td>\n",
              "      <td>56.68</td>\n",
              "      <td>259600.00</td>\n",
              "      <td>1.42</td>\n",
              "      <td>6.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>information technology</td>\n",
              "      <td>10819906.64</td>\n",
              "      <td>47.41</td>\n",
              "      <td>511939.72</td>\n",
              "      <td>1.55</td>\n",
              "      <td>7.08</td>\n",
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          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "category_analysis.to_csv('/content/drive/MyDrive/Upwork_Projects/Nurman_Naz/Datasets/category_analysis.csv')"
      ],
      "metadata": {
        "id": "bJrM6NxQW6e9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "category_analysis=pd.read_csv('/content/drive/MyDrive/Upwork_Projects/Nurman_Naz/Datasets/category_analysis.csv')\n",
        "category_analysis.drop(columns=['Unnamed: 0'],inplace=True)"
      ],
      "metadata": {
        "id": "bWFSxNDoLC5A"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "Which 10 organization categories got the highest funding?"
      ],
      "metadata": {
        "id": "61fOb1e2j8eX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "h_f=category_analysis.sort_values(by=['mean_funding'], ascending=False)"
      ],
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        "id": "8WD4ofZdiJ-Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "h_f[['org_category','mean_funding']].head(10)"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               org_category  mean_funding\n",
              "24                   energy 2739667942.44\n",
              "17    professional services  415590000.00\n",
              "31  government and military  250000000.00\n",
              "9               real estate  110155370.47\n",
              "26           transportation  104742844.11\n",
              "23              health care   71171601.08\n",
              "32  agriculture and farming   67628911.62\n",
              "21  administrative services   48376092.85\n",
              "12                     apps   44431480.69\n",
              "8        financial services   44318765.28"
            ],
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              "      <th>31</th>\n",
              "      <td>government and military</td>\n",
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              "      <th>9</th>\n",
              "      <td>real estate</td>\n",
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              "      <th>26</th>\n",
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              "      <td>104742844.11</td>\n",
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              "      <th>32</th>\n",
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              "      <td>67628911.62</td>\n",
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              "      <td>44431480.69</td>\n",
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              "      <td>financial services</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(15,10)})\n",
        "sns.barplot(x = 'org_category',\n",
        "            y = 'mean_funding',\n",
        "            data = h_f.head(10))\n",
        "plt.xticks(rotation=45)\n",
        "plt.savefig('/content/drive/MyDrive/Upwork_Projects/Nurman_Naz/Plots/highest_funding.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "zxQbzzXDkm9V",
        "outputId": "e9128a8c-2924-4847-d213-bb296563ceba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Which type of organizations have provided the highest employment?"
      ],
      "metadata": {
        "id": "t5Mqa0yIm1CO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "h_e=category_analysis.sort_values(by=['mean_employee'], ascending=False)"
      ],
      "metadata": {
        "id": "ml5cPg97miG3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "h_e[['org_category','mean_employee']].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "0skANR03nVJD",
        "outputId": "2499884a-80ed-4b1b-8994-55abc33dc2a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               org_category  mean_employee\n",
              "24                   energy        2715.93\n",
              "0                    gaming        2522.00\n",
              "17    professional services        2166.69\n",
              "31  government and military        2019.00\n",
              "23              health care        1280.00\n",
              "7             manufacturing        1253.56\n",
              "32  agriculture and farming         696.62\n",
              "33     privacy and security         499.00\n",
              "14     clothing and apparel         439.11\n",
              "15  community and lifestyle         343.80"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6723077e-dcf8-47bd-bc8a-74f334519d1d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>org_category</th>\n",
              "      <th>mean_employee</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>energy</td>\n",
              "      <td>2715.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>gaming</td>\n",
              "      <td>2522.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>professional services</td>\n",
              "      <td>2166.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>government and military</td>\n",
              "      <td>2019.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>health care</td>\n",
              "      <td>1280.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>manufacturing</td>\n",
              "      <td>1253.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>agriculture and farming</td>\n",
              "      <td>696.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>privacy and security</td>\n",
              "      <td>499.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>clothing and apparel</td>\n",
              "      <td>439.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>community and lifestyle</td>\n",
              "      <td>343.80</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6723077e-dcf8-47bd-bc8a-74f334519d1d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6723077e-dcf8-47bd-bc8a-74f334519d1d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6723077e-dcf8-47bd-bc8a-74f334519d1d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(15,10)})\n",
        "sns.barplot(x = 'org_category',\n",
        "            y = 'mean_employee',\n",
        "            data = h_e.head(10))\n",
        "plt.xticks(rotation=45)\n",
        "plt.savefig('/content/drive/MyDrive/Upwork_Projects/Nurman_Naz/Plots/highest_employee_count.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
        },
        "id": "Tub12RBWnZqE",
        "outputId": "65faf348-873a-4767-db6e-494e39e3215e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Which companies have received a good post money valuation?"
      ],
      "metadata": {
        "id": "fT33An39n0ut"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "h_v=category_analysis.sort_values(by=['mean_post_money_valuation'], ascending=False)"
      ],
      "metadata": {
        "id": "ugMT7ShBoDHb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "h_v[['org_category','mean_post_money_valuation']].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "6f05c8a1-e7c2-4414-d65b-bfc6cde276fb",
        "id": "fuKMMwuNoDHc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               org_category  mean_post_money_valuation\n",
              "24                   energy               755913148.15\n",
              "7             manufacturing               493188888.89\n",
              "17    professional services               323076923.08\n",
              "9               real estate                61069469.79\n",
              "20  artificial intelligence                22803043.48\n",
              "21  administrative services                21596438.38\n",
              "10            biotechnology                19428571.43\n",
              "1     commerce and shopping                10697205.39\n",
              "22        food and beverage                 4854907.72\n",
              "14     clothing and apparel                 4464285.71"
            ],
            "text/html": [
              "\n",
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              "      <th>org_category</th>\n",
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              "  </thead>\n",
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              "      <th>24</th>\n",
              "      <td>energy</td>\n",
              "      <td>755913148.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>manufacturing</td>\n",
              "      <td>493188888.89</td>\n",
              "    </tr>\n",
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              "      <th>17</th>\n",
              "      <td>professional services</td>\n",
              "      <td>323076923.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>real estate</td>\n",
              "      <td>61069469.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>artificial intelligence</td>\n",
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              "      <th>21</th>\n",
              "      <td>administrative services</td>\n",
              "      <td>21596438.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>biotechnology</td>\n",
              "      <td>19428571.43</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>commerce and shopping</td>\n",
              "      <td>10697205.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>food and beverage</td>\n",
              "      <td>4854907.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>clothing and apparel</td>\n",
              "      <td>4464285.71</td>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-95730fed-da69-4d53-8ef6-368cadd7a364 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-95730fed-da69-4d53-8ef6-368cadd7a364');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(15,10)})\n",
        "sns.barplot(x = 'org_category',\n",
        "            y = 'mean_post_money_valuation',\n",
        "            data = h_v.head(10))\n",
        "plt.xticks(rotation=45)\n",
        "plt.savefig('/content/drive/MyDrive/Upwork_Projects/Nurman_Naz/Plots/mean_post_money_valuation.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "outputId": "a63e5087-8cf8-4d8a-9eff-b68d680ff3aa",
        "id": "4hlKRkzaoDHd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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xRnTt2rXShwIAACC91GHXv3//fM4BAADARtpg2L366qupVtK+fftKGQYAAICK22DYXXbZZeWuIJfLxejRoyttIAAAACpmg2E3ZsyYQs0BAADARtqkjzsAAACg+FLfPOXrr7+OW2+9NSZNmhQLFy6MJEnWPTZ27Nh8zAYAAEAKqY/Y9e3bN6ZNmxY9evSIRYsWxeWXXx6NGjWK008/PY/jAQAAUJ7UR+zGjx8fo0aNinr16sVWW20VRxxxROy9995x7rnnijsAAIAiSn3ErqysLGrVqhUREdtss00sWbIkGjZsGDNnzszbcAAAAJQv9RG7li1bxqRJk6J9+/bRrl276Nu3b2y77bbRvHnzPI4HAABAeVIfsbvmmmuiSZMmEfHPz7fbeuut46uvvooBAwbkbTgAAADKl/qIXePGjWOrrbaKiIgGDRrEtddem7ehAAAASC/1EbsOHTpE3759Y/LkyfmcBwAAgApKHXb33XdfbLPNNtGrV6/o3Llz/OEPf4gPPvggn7MBAACQQupTMffYY4/YY4894qKLLoqJEyfGiBEj4rTTTouGDRvG8OHD8zkjAAAAG5D6iN237bLLLtGiRYto3LhxzJo1q7JnAgAAoAJSH7H76quv4rnnnosRI0bElClTokOHDtG9e/c4/PDD8zkfAAAA5Ugddv/1X/8Vbdq0iS5dusStt94atWvXzudcAAAApJQ67F544YXYfvvtN/ice+65J84555xNHgoAAID0Ul9jV17URUTcddddmzQMAAAAFbdRN0/5T5IkqczVAQAAkEKlhl0ul6vM1QEAAJBCpYYdAAAAhedUTAAAgIyr1LBr165dZa4OAACAFFKHXdeuXeP++++P+fPn/8fnDB48uFKGAgAAIL3UYferX/0qJk+eHEcccUR07949hg8fHitWrMjnbAAAAKSQOuyOOuqouO2222Ls2LFx+OGHx8MPPxwdOnSISy65JF599dV8zggAAMAGVK3oC+rWrRvdunWLbbbZJu699954/vnnY/LkyVGlSpW44oor4uCDD87HnAAAAPwHqcMuSZIYN25cPP300zF27NjYd99945xzzokjjzwytt5663juuefiwgsvjPHjx+dzXgAAAP5F6rDr2LFj1KtXL7p27RoXXnhh7LDDDus9/sMf/jD+9Kc/VfqAAAAAbFjqsLvrrrti77333uBzHnzwwU0eCAAAgIpJHXZ77713TJ8+PZ599tmYP39+XHHFFTF9+vRYtWpVtGzZMp8zAgAAsAGp74r5zDPPxCmnnBJz5syJp59+OiIili1bFtddd13ehgMAAKB8qY/Y3XLLLXH//fdHy5Yt45lnnomIiJYtW8b777+ft+EAAAAoX+ojdl9++WXsvvvuERGRy+XW/XPtnwEAACiO1GG35557rjsFc62RI0fGPvvsU+lDAQAAkF7qUzEvu+yyOOuss2LYsGGxbNmyOOuss2LGjBlx33335XM+AAAAypE67Fq0aBHPPPNMvPTSS3HooYdGo0aN4tBDD41tt902n/MBAABQjtRhFxFRo0aNOPbYY/M1CwAAABshddh99tlncfPNN8d7770Xy5YtW++xsWPHVvZcAAAApJQ67Hr16hXNmjWL3r17R40aNTZ6g7fddlvceuutMXz48Nhtt902ej0AAAD8U+qw++ijj+KRRx6JKlVS30jz37z77rvx1ltvRZMmTTZ6HQAAAKwvdaXtv//+MW3atI3e0MqVK+Oqq66Kvn37bvQ6AAAA+Hepj9g1adIkunfvHkceeWRst9126z3Ws2fPcl8/aNCg+MlPfhJNmzat+JQR0aBBzY163ZaoYcNaxR5hs2J/AgDwfZc67L755ps47LDDYvXq1VFaWlqhjbz55pvxzjvvRK9evSo84FoLFnwdZWXJdz7mB+/1zZu3ZJNeb3+ub1P3JwAAbIoqVXLlHuhKHXb9+/ff6EEmTZoU06dPj8MPPzwiIkpLS+Oss86K/v37R8eOHTd6vQAAAFTwc+w++eSTGDFiRMydOze233776NKlSzRv3rzc151zzjlxzjnnrPu6c+fOcdddd7krJgAAQCVIffOUMWPGxHHHHRczZsyIOnXqxIwZM+L444+P0aNH53M+AAAAypH6iN1NN90Ud9xxRxx00EHrlk2YMCGuvvrqdadYpjVmzJgKPR8AAID/LPURu9LS0mjXrt16y9q2bVvhG6kAAABQuVKHXcuWLeO+++5bb9mQIUOiVatWlT4UAAAA6aU+FbNv375x3nnnxdChQ6NRo0bxxRdfRI0aNeKuu+7K53wAAACUI3XYtWjRIkaNGhVvvfXWurtitm7dOqpVq5bP+QAAAChHhT7uoGrVqv92nR0AAADFlTrs3n///ejXr1+8//77sWzZsoiISJIkcrlcvPPOO3kbEAAAgA1LHXYXXHBBHHXUUXH55ZfH1ltvnc+ZAAAAqIDUYTd//vzo2bNn5HK5fM4DAABABaX+uIOf/vSnMXz48HzOAgAAwEZIfcTunHPOiZNOOinuvvvuaNCgwXqPDR06tNIHAwAAIJ3UYXf++edH06ZN48gjj4zq1avncyYAAAAqIHXYvffeezFhwoQoKSnJ5zwAAABUUOpr7Nq1axfTp0/P5ywAAABshNRH7Jo2bRpnnnlmHHnkkf92jV3Pnj0rfTAAAADSSR12y5cvj0MPPTRWrVoVpaWl+ZwJAACACkgddv379y/3OSNGjIguXbps0kAAAABUTOpr7NLo06dPZa4OAACAFCo17JIkqczVAQAAkEKlhl0ul6vM1QEAAJBCpYYdAAAAhSfsAAAAMq5Sw65x48aVuToAAABSSB12BxxwwHcub9++/bo/jxgxYtMnAgAAoEJSh92qVau+c1lZWVmlDgQAAEDFlPsB5SeffHLkcrlYuXJlnHLKKes9VlpaGm3atMnbcAAAAJSv3LA78cQTI0mSePvtt+OEE05YtzyXy0WDBg3ioIMOyuuAAAAAbFi5YdetW7eIiGjdunW0aNEi7wMBAABQMamvsXvvvfdi+vTpERHx8ccfx6mnnho///nP1y0DAACgOFKH3c033xx16tSJiIgBAwbE3nvvHQcccEBceeWVeRsOAACA8pV7KuZaX375ZWy33XaxYsWKeP311+OWW26JqlWrusYOAACgyFKHXf369WPmzJnx4Ycfxt577x0lJSXxzTffRJIk+ZwPAACAcqQOux49esRxxx0XW221Vdx0000REfHKK69Ey5Yt8zYcAAAA5Usddscdd1wcc8wxERFRo0aNiIjYd99948Ybb8zPZAAAAKSSOuwiIlauXBkvvfRSzJkzJ3bYYYc49NBDo0GDBvmaDQAAgBRS3xXzzTffjCOPPDIeffTR+OCDD+LRRx+No446Kt588818zgcAAEA5Uh+x69evX1xxxRXxox/9aN2yUaNGxTXXXBOPP/54XoYDAACgfKmP2H3yySfrrrFb64c//GF8+umnlT4UAAAA6aUOu5122ilGjhy53rJnn302mjVrVulDAQAAkF7qUzEvvfTSOPfcc+PBBx+Mxo0bx6xZs2LmzJlx11135XM+AAAAypE67Pbbb7944YUXYuzYsTF37tw47LDDolOnTlG3bt18zgcAAEA5KvRxB3Xq1ImDDjpo3ccdiDoAAIDiSx12s2fPjl69esWUKVOidu3a8dVXX0Xr1q1j4MCB0aRJk3zOCAAAwAakvnlK7969Y88994xJkybFq6++GhMnToy99torLr744nzOBwAAQDlSH7F7991347777otq1apFRMS2224bvXr1igMPPDBvwwEAAFC+1Efs9t1335g6dep6y955551o06ZNpQ8FAABAeqmP2DVr1izOOeecOPTQQ2PHHXeM0tLSePnll6NLly4xaNCgdc/r2bNnXgYFAADgu6UOu5UrV8ZRRx0VERFffvlllJSUxJFHHhkrVqyI0tLSvA0IAADAhqUOu/79+5f7nBEjRmzSMLC5qV2nelQvKSn2GN8LK1aujK8Wryj2GAAAm6UKfY5defr06RNdunSpzFVCplUvKYnThzg9OSLi/jMGRYSwAwDIh9Q3T0kjSZLKXB0AAAApVGrY5XK5ylwdAAAAKVRq2AEAAFB4wg4AACDjKjXsGjduXJmrAwAAIIUK3RVzyZIlMWPGjFi6dOl6y9u3bx8RPu4AAACgGFKH3RNPPBFXXXVVbLPNNrH11luvW57L5WL06NF5GQ4AAIDypQ67m266KQYNGhSdOnXK5zwAAABUUOpr7NasWRMdO3bM5ywAAABshNRhd/bZZ8edd94ZZWVl+ZwHAACACkp9Kub9998f8+fPj3vvvTfq1q273mNjx44t9/U9evSIzz//PKpUqRLbbLNN/P73v49WrVpVeGAAAADWlzrsBg4cuEkbuv7666NWrVoREfHiiy/GpZdeGk8++eQmrRMAAIAKhN0BBxywSRtaG3UREV9//XXkcrlNWh8AAAD/VKHPsXvvvfdi8uTJsXDhwkiSZN3ynj17pnr9ZZddFuPHj48kSeLee++t2KQAAAB8p9Rh99hjj0X//v2jQ4cO8be//S0OOeSQGD9+fBx++OGpN3bttddGRMRTTz0VAwYMiMGDB6d+bYMGNVM/d0vXsGGt8p9EavZn5bEvAQDyI3XY3XvvvXHvvfdGu3btYv/994/bb789Xn755Rg1alSFN/rTn/40+vTpEwsXLox69eqles2CBV9HWVnynY/5YXF98+Yt2aTX25/r25T9aV+ub1PfmwAAW6IqVXLlHuhK/XEHCxYsiHbt2v3viqtEWVlZdOrUKV566aVyX7t06dL44osv1n09ZsyYqFOnzr/dXRMAAICKS33Ebscdd4zPP/88mjZtGs2bN4/Ro0dHvXr1olq1auW+9ptvvomePXvGN998E1WqVIk6derEXXfd5QYqAAAAlSB12HXv3j2mT58eTZs2jR49ekTPnj1j1apVcdlll5X72u222y7+/Oc/b9KgAAAAfLfUYXfcccet+3OnTp1i4sSJsWrVqth2223zMhgAAADppL7GLiJi4cKF8dRTT8XgwYOjpKQkvv766ygtLc3XbAAAAKSQOuwmTpwYRx99dAwfPjzuuOOOiIiYOXNm9O3bN1+zAQAAkELqsOvXr1/cfPPN8cc//jGqVv3nGZytW7eOqVOn5m04AAAAypc67GbNmhXt27ePiFh3N8tq1arFmjVr8jMZAAAAqaQOuxYtWsTf//739Za98sorsdtuu1X6UAAAAKSX+q6YF198cfzyl7+MQw89NJYvXx59+vSJMWPGrLveDgAAgOJIfcRu3333jb/+9a+x6667xvHHHx9NmzaNxx9/PPbZZ598zgcAAEA5Uh+xW7JkSQwbNiymTZsWy5Yti5kzZ8Zrr70WERH33Xdf3gYEAABgw1KHXc+ePWPNmjVx5JFHRvXq1fM5EwAAABWQOuzeeuuteO2116KkpCSf8wAAAFBBqa+xa9u2bXz88cf5nAUAAICNkPqI3XXXXRdnn312tG7dOho0aLDeY7/+9a8rfTAAAADSSR12N910U5SWlkbTpk3j66+/Xrd87YeVAwAAUBypw27kyJHx3HPPxfbbb5/PeQAAAKig1NfYNWvWLKpWTd2BAAAAFEjqUuvatWv06NEjTj311H+7xq59+/aVPhgAAADppA67hx56KCIibrzxxvWW53K5GD16dOVOBQAAQGqpw27MmDH5nAMAAICNlPoaOwAAAL6fhB0AAEDGCTsAAICME3YAAAAZJ+wAAAAyTtgBAABknLADAADIOGEHAACQccIOAAAg44QdAABAxgk7AACAjBN2AAAAGSfsAAAAMk7YAQAAZJywAwAAyDhhBwAAkHHCDgAAIOOEHQAAQMYJOwAAgIwTdgAAABkn7AAAADJO2AEAAGScsAMAAMg4YQcAAJBxwg4AACDjhB0AAEDGCTsAAICME3YAAAAZJ+wAAAAyTtgBAABknLADAADIOGEHAACQccIOAAAg44QdAABAxgk7AACAjBN2AAAAGSfsAAAAMk7YAQAAZJywAwAAyDhhBwAAkHHCDgAAIOOqFmIjCxcujIsuuig+/fTTKCkpiZ122imuuuqqqF+/fiE2DwAAsFkryPvbb3AAACAASURBVBG7XC4X3bt3j+eeey6GDx8ezZo1ixtuuKEQmwYAANjsFSTs6tatGwceeOC6r/fdd9+YPXt2ITYNAACw2SvIqZjfVlZWFo888kh07ty5Qq9r0KBmniba/DRsWKvYI2xW7M/KY18CAORHwcPu6quvjm222SZOPfXUCr1uwYKvo6ws+c7H/LC4vnnzlmzS6+3P9W3K/rQv17ep700AgC1RlSq5cg90FTTsrr/++pg5c2bcddddUaWKG3ICAABUhoKF3Y033hjvvPNO3HPPPVFSUlKozQIAAGz2ChJ2H330Udx9993RvHnz+NnPfhYREU2bNo3bb7+9EJsHAADYrBUk7H7wgx/EBx98UIhNAQAAbHFc6AYAAJBxwg4AACDjhB0AAEDGCTsAAICME3YAAAAZJ+wAAAAyTtgBAABknLADAADIOGEHAACQccIOAAAg44QdAABAxgk7AACAjBN2AAAAGSfsAAAAMk7YAQAAZJywAwAAyDhhBwAAkHHCDgAAIOOEHQAAQMYJOwAAgIwTdgAAABkn7AAAADJO2AEAAGScsAMAAMg4YQcAAJBxwg4AACDjhB0AAEDGCTsAAICME3YAAAAZJ+wAAAAyTtgBAABknLADAADIOGEHAACQccIOAAAg44QdAABAxgk7AACAjBN2AAAAGSfsAAAAMk7YAQAAZJywAwAAyDhhBwAAkHHCDgAAIOOEHQAAQMYJOwAAgIwTdgAAABkn7AAAADJO2AEAAGScsAMAAMg4YQcAAJBxwg4AACDjhB0AAEDGCTsAAICME3YAAAAZJ+wAAAAyTtgBAABknLADAADIOGEHAACQccIOAAAg44QdAABAxhUk7K6//vro3Llz7L777vHhhx8WYpMAAABbjIKE3eGHHx4PPfRQNGnSpBCbAwAA2KJULcRG2rVrV4jNAAAAbJFcYwcAAJBxBTliVxkaNKhZ7BEyo2HDWsUeYbNif1Ye+xIAID8yE3YLFnwdZWXJdz7mh8X1zZu3ZJNeb3+ub1P2p325vk19bwIAbImqVMmVe6DLqZgAAAAZV5Cwu+aaa+KQQw6J0tLSOOOMM+JHP/pRITYLAACwRSjIqZiXX355XH755YXYFAAAwBbHqZgAAAAZJ+wAAAAyTtgBAABknLADAADIOGEHAACQccIOAAAg44QdAABAxgk7AACAjBN2AAAAGSfsAAAAMk7YAQAAZJywAwAAyDhhBwAAkHHCDgAAIOOEHQAAQMYJOwAAgIwTdgAAABkn7AAAADJO2AEAAGScsAMAAMg4YQcAAJBxwg4AACDjqhZ7AIC06tYqiWpbVy/2GN8Lq5aviEVLVhZ7DADge0LYAZlRbevqMeoXZxR7jO+FY4cOiRB2AMD/ciomAABAxgk7AACAjBN2AAAAGSfsAAAAMk7YAQAAZJywAwAAyDhhBwAAkHHCDgAAIOOEHQAAQMYJOwAAgIwTdgAAABkn7AAAADJO2AEAAGScsAMAAMg4YQcAAJBxwg4AACDjhB0AAEDGCTsAAICME3YAAAAZJ+wAAAAyTtgBAABknLADAADIOGEHAACQccIOAAAg46oWewAAiqNO7RpRUt3/BiIiVq5YHYu/+qbYYwDARvN/dIAtVEn1qtHvsmHFHuN74dJrT9jkddSpXRIl1atXwjTZt3LFilj81cpijwGwRRF2AFAJSqpXjxsv+WWxx/heuKD/3RGx8WFXr06NqFriR5SIiNUrV8fCxY4mA+XzXRMA+F6pWlI1ptwxtthjfC+07nFosUcAMsLNUwAAADJO2AEAAGScsAMAAMg4YQcAAJBxbp4CALAZq1Nn6ygpqVbsMb4XVq5cFYsXLy/2GJAXwg4AYDNWUlIt/vCHPxR7jO+F3/3udxEh7Ng8ORUTAAAg44QdAABAxhXsVMwZM2bExRdfHIsWLYq6devG9ddfH82bNy/U5gEAYJPVq1s9qlYrKfYY3wurV62MhYtWbPTra9etEdWruTIsImLFqtXx1aJvNmkdBduTV1xxRZx88snRtWvXePrpp6NPnz4xdOjQQm0eAAA2WdVqJfG3EX2LPcb3wiFd+kbExodd9WpV44InX660ebLsxm6dNnkdBQm7BQsWxLRp02LIkCEREdGlS5e4+uqr48svv4z69eunWkeVKrkNPr5dvW03ec7NRXn7Ko2S2g0qYZLNw6buz+1qpnuPbwkq471ZYzvvzbUqY3/WqbtNJUyyeaiM/Vm7rvfnWpu6P6vV2rqSJsm+Snlv1q5dCZNsHipjf1avUbcSJtk8bOr+rLdN9UqaJPs2tC/T7OdckiRJZQ70Xd55553o3bt3jBw5ct2yY489NgYOHBh77rlnvjcPAACwWXPzFAAAgIwrSNg1atQo5syZE2vWrImIiDVr1sTcuXOjUaNGhdg8AADAZq0gYdegQYNo1apVjBgxIiIiRowYEa1atUp9fR0AAAD/WUGusYuImD59elx88cXx1VdfRe3ateP666+PXXbZpRCbBgAA2KwVLOwAAADIDzdPAQAAyDhhBwAAkHHCDgAAIOOEHQAAQMYJOwAAgIwTdgDwL9wwuvLZp2TJt9+v3rvls48qx4wZM+KRRx7Z6NcLO6Cg1n7z/+abb4o8SeH9p//xrVmzpsCT8K/W/t3MmTMnli9fHkuXLi3yRJuXJEkil8tFRMTLL78c06dPL/JExecH4e+3XC4Xr7/+eixZsiRyuZy/r29Zuy8WLVoUixcvjohY9983m2bJkiVx5ZVXxtChQzfq9VUreR7KMXny5Nhxxx2jadOm6/2PjsKbNWtWREQ0adKkyJNsOda+5//2t7/Fe++9F//93/8d9erVK/ZYBfHt/94fe+yxmDdvXsybNy9++9vfRt26dYs8HblcLsaMGRMPPPBANG3aNEpKSuKMM86I//f//l+xR9ssrH3vP/TQQ/Hoo4/GnXfeWeSJii+Xy8WECRPik08+iZNOOqnY4/C/1n6v/uyzz+LSSy+NsrKyeOKJJ6JWrVp+bov/2z9jx46NwYMHR+3ataN69eoxcODAqFatWrHHy7SysrLYZ5994uGHH46f//znUVZWFqeffnqF1uGIXYE9/vjj0b9//4jw241iWPtbpnfeeSd69uwZQ4YMiQULFhR5qi1HLpeLl19+OQYMGBBt27bdYqIu4v/+e7///vtj1KhR0bFjxxg9enTccccd657jN8LFM2nSpLjjjjvihhtuiKpVq8aMGTOiXr16/k4q0ejRo2PYsGHr4vmtt96K8ePHx/Lly4s9WtEsXrw4JkyYEKtWrYqysrJij0P883v1Cy+8EL///e/j1FNPjVq1akW3bt3WHbnb0v+ecrlcvPbaazFo0KC49tprY//994/33nsvVqxYUezRMq9KlSpRVlYW++23XzzwwANxww03xJAhQyq0jq369u3bNz/j8V2aN28e06ZNi/322y9q1Kjhtz8FtjYshg4dGrvsskuMGjUqkiSJ5s2bR82aNYs93mZv0aJFccUVV8Sll14a+++/f7z00kvx5z//OWbNmhV77bVXscfLu9LS0hg2bFjcfffdMXLkyFizZk1ce+21sWLFikiSJKpWdRJFoa39Hvz888/H0UcfHfPmzYsRI0bEgAEDomHDhjFt2rSoU6eOv5uNsHbfrv3n22+/HXXr1o1FixbFM888Ew8++GB89NFHkcvlomXLlsUetyiWLl0af/3rX6NDhw6O3H9PLF26NPr37x+/+c1v4phjjomTTjopJk+eHIMHD46uXbtG9erVo6ysbIv+2W38+PFxwgknxIIFC+Lhhx+O22+/PbbbbruYMmVK7LjjjsUeL3PWfo+cOnVqvPnmm7F48eJo165ddOzYMXr16hU1a9aM1q1bp1qXI3YF8MEHH8T7778fEf887e/zzz+PJ598MiIctSu0OXPmxHXXXRcnn3xy9OnTJ+6+++4YP358DBkyJObPn1/s8TZ7VatWjaZNm8azzz4bPXv2jJdeeinKysritddeiyVLlhR7vEq3cuXKdX9eunRpbLvttpHL5aJ3794xYcKEGDRoUFStWjX+8pe/xIgRI4o46ZZn7ZG4tdfS5XK5GDp0aAwZMiRuvPHGaNq0aYwdOzZuvfVW19tthG//0nLVqlUREdG2bdt444034qWXXor27dvHgw8+GLvtttsWt3+nT58eL730UkRE7LPPPtGhQ4cYPHjwuv1Eca1evTq+/PLLWL169bplPXv2jMWLF8cvfvGLKCsriypVtqwfn//1zIXly5fH7373u7jpppvizjvvjKZNm8Zrr70Wt9xyS8ydO7dIU2bX2ktULrnkkpg9e3aceeaZ8dhjj8U+++wTDz74YPTr1y/uu+++VOvast6ZRbBkyZIYOXJkdO/ePQYPHhxvvvlm9O7dO6ZMmRJLlixxmk+BVatWLXbeeed1v/nYZ5994owzzoi//OUv8cwzzxR5us3P2vf3J598EqWlpbHVVlvFSSedFHXr1o2f//zncdVVV8URRxwRc+fO3exOb1l7itXs2bPj8ccfj2HDhkWSJFG/fv2YMWNG9O7dO0pKSuLJJ5+Mxx57LNq2bVvskbcYa6Nj/Pjxceedd8aKFSti//33j3fffTcOPvjgqF+/frzxxhtx4403xoknnhj169cv9siZ8u2oe+SRR+KSSy6Jyy+/PD744IO4//7745Zbbon27dvHhAkT4pVXXon27dsXeeL8W/u9sKysLJ588sm477774swzz4wXX3wxmjRpEjVq1Pi351IYa/f3ypUrY82aNVGnTp045phjYujQofHBBx9ERMTChQvjZz/7WWy99dbxl7/8pZjjFkUul4vJkyfH7373u4iIOProo2OnnXaKdu3aRf369WPixInRr1+/OOWUU2L77bcv8rTZ89lnn8Xtt98ed955Z+y1116x8847R+fOnSMiok2bNvGnP/0pWrRokWpdTsXMo6lTp8aXX34Z3bp1i/322y8WLVoU999/f/ztb3+Ld999Nw4//PBo2LBhscfcrK39AWP16tVRpUqVqF69etx3330xbdq0OOKIIyLin3dnnDVrVgwfPjz23XffaNy4cZGn3nysPfX1yiuvjIULF0afPn3i3HPPjcMOOywaN24ckyZNimuvvTbOO++8aNWqVbHHrVTLli2LkSNHxpAhQ+LVV1+N3r17R7169aKsrCwWL14cjz76aLz99tvxzDPPxE033RS77LJLsUfeYqyNumuuuSZOO+202GmnnWL77bePPfbYI/70pz/FuHHjYty4cXHuuefG4Ycf7pT5Clq7rx5++OF47rnnomfPnjFixIiYPXt2HHXUUZHL5eKRRx6JP/7xjzFgwIDYddddizxx/q29Lmn06NHRo0ePOO6446K0tDRKS0vXvedq164dbdq08V4rsFwuF6NHj4577rknRo0aFfvvv3+0adMmPvnkkxg4cGDMnz8/br755vjtb38by5Yti7p168Yee+xR7LELYu33vjfffDNeeeWVeOihh6K0tDR+8pOfRN26dWPChAkxePDgeOutt6JHjx7RuXNn3y9TWrufFi5cGKtXr47Vq1fH8uXL44477og//OEP0aRJkxgxYkSsWrUq9t1332jevHm6fZuQF++//35y0kknJe3atUsmTJiwbvnKlSuT559/PrnggguS888/P1mxYkURp9wyjB8/PrnyyiuTBx54IEmSJJk/f35yxBFHJL/85S+TO++8M/nhD3+YzJw5M7nuuuuSV155pcjTbl6mTp2adO3aNfn000+TRx99NDn66KOTBQsWJEmSJJ999lly3nnnJWPGjEmSJEnKysqKOWpejBo1Kjn44IOTSy+9NPn444/XLZ81a1Yyfvz4ZNy4ccnnn39exAm3PGVlZcmyZcuSc889N3nhhReSJEmS1atXJ2vWrEmSJEkWLlyYLFmyJJkzZ86651MxZWVlyYIFC5LLL788WbJkSfLwww8n3bt3T1atWpWsWrUqmTt3bjJnzpzks88+K/aoebf2/fP2228n119/fbL77rsnt91223rPmTp1ajJo0KCkT58+ycqVK73nCmTtfp4+fXryk5/8JHnqqaeS3r17J+3bt09mzZqVJEmSPP/888lTTz2V/OMf/0jeeOON5Mc//nEyffr0Yo5dcK+88krSqVOnZOzYscnjjz+edOvWLbnwwgvXPf7FF18kCxcuTJLE98u01u6ncePGJb17904+/vjj5LDDDks6dOiQrF69OkmSJJkyZUpy1FFHJZMmTarQuh2xy4MXX3wxBg4cGGeeeWasXr06Hnjggdh7772jcePGsdVWW0WLFi1i5513jnfffXfdby+pXMn//lbj9ddfj8svvzwOPvjgeOSRR2L27NnRtm3bOPXUU2PWrFmx1VZbRY8ePWLevHkxZMiQOPXUU6NOnTrFHj/z1l5Y/vbbb8euu+4auVwu7r333rj99ttjxx13jDFjxsSee+4ZHTt2jFatWm02v+H713+PRo0aRYcOHeLDDz+MadOmRc2aNaNRo0Yxa9asaNasWbRs2TJq165dxIm3PGv/fsaMGRMdO3aMHXbYIVasWBElJSXx1ltvxerVq2PHHXeMbbfddr3ns2Hffu/ncrmoUaNGTJ48OR544IH45JNP4t57740qVarEY489FpMnT45OnTptEd9r1147c/HFF8dxxx0XLVu2jGHDhsX8+fPj4IMPjoiIHXbYIXbaaacYOnRoHHbYYW7klWerVq2KrbbaKnK5XIwbNy7uueeeOO200+LYY4+NI444ImbPnh3XXXdddO7cOdq2bRstW7aMTz75JG666aa4+uqr4wc/+EGx/xUKJkmSeOqpp6Jjx47x4x//OFq1ahWHHHJI3HTTTfHRRx/FEUccETVr1ozq1atHLpfz/TKlXC4XU6ZMiSeeeCK6desWe+21VzRv3jxeeeWVWLhwYXz00Udx++23R69evaJDhw4VWrdr7CrZmjVr4plnnomzzz47jj766Ljxxhvj+OOPj/PPPz/eeOONdc97++23Y9y4ces+2JHKlcvl4t13343Ro0fHpZdeGmeddVbceeed8dFHH8XgwYNj2bJl8Zvf/CbOPffcWLZsWVx55ZUxaNCgaNasWbFHz7TkX25IUbdu3RgyZEj069cv7r777vj/7L15WJTl9/j/mmGHkX1fXNhEkR0BBVkUFRX3vd0ss0zb7F1Wmpa5lLjlWm6Zgrti7iIg4IKsAoIoKKAIpIABiijM8/uja+ZH9X5/P1nK6DCvf7q8erw8zz33c+5zzn0WOzs7eXex8vJy+bgDZTgMWhu2J06cICYmhmvXruHu7s5rr71GQ0MDx48f56uvvmL+/PkKlrZ9IduXsnbcGhoaaGhosGDBAgB0dHTIz89n4cKFf2iYoOLv0Xrv7927l0OHDiGVSpFIJDQ0NPD2228DcPDgQaKjo4mIiGg3zSeam5tJSkrio48+IiIigqlTp7Js2TJ27tzJDz/8IH+uvLycO3fuKIUufJaprq5m+vTpcl2grq7OqVOnSEpKkj8ze/ZsQkNDGT16tHwUh7W1NVFRUe2ue6tIJEJLS4sjR45QW1sL/B6wHDhwIBcvXmTFihXy51T8fRoaGti4cSNnzpzBzc0NgMDAQJYuXcqvv/5KQ0MDn376KWFhYY9dc6u6sXuCyGrq4uLisLa2lv9Yvr6+HD9+nISEBHk07v79+4wbN041HPsJcuPGDfbs2YOHhwdisZiNGzdy/PhxbGxs6N69O+bm5ri5uREdHU15eTm+vr5oaGhw//59xowZQ+fOnRX9Cs81MuPu9OnTfP311/Tv3x9tbW2Ki4txdHREX1+fW7dusWDBAt5++228vLwULfITRXawbdu2jR07dmBlZcXChQsRi8X06tWLbt26cfv2bW7dusX777+vGnzdhsj25dq1a8nIyMDT05PIyEh++eUXNm3aRHFxMVu3buWdd97Bz89P0eI+d8j2/tatWzlw4ACjRo3C3NycLl26UFhYSHJyMrGxsZw/f54lS5a0i5o6GWKxmFOnTskzdMRiMYaGhhQVFREXF4euri6urq48ePCA0aNHq2q8nzK6urq4u7tz9+5damtrcXd3p2fPnixcuBAjIyO53da3b1/69esnbwQikUjkt/jKjOwcr6mpQSwWyztZFxYWUlRUhLOzM2VlZZw4cYIBAwZQW1tLnz59FC32c4dYLKZz586kpqaSl5dHeHg4YrEYCwsLwsPD6dmzp/yi4XGdZpVj94TIzc1lzpw5vPTSS5iZmclvJzp16kRWVhYNDQ00Nzdz/fp1goKCsLGxwcTERNFiKxWPHj2iQ4cOiMViJBIJffr0oba2lkuXLmFra4uJiQmmpqbyIlRbW1sAjIyMVKkv/xKhVZfBb7/9lunTp+Ps7EyHDh0wMjKitraWmJgYbty4wcsvv6y0DSlOnz5NdHQ0W7Zsobi4mOLiYiorK7l79y5+fn74+/sTFham6hrWRgitZgPNnTuX0aNHc/LkSXJzc3FwcGDSpEkYGhri4ODAkCFD6N27t1Luy6dF61leBQUFbNiwgR9//BFbW1u5Pg4ICMDf3x8fHx8mTpwo17vKimz/1NXV0djYiLa2NhYWFly5coWbN2/i6elJUVERmZmZ+Pn5UV9fT0BAAEZGRu0iNfVZwMDAgB9//JG5c+cSHh6Om5sbHh4ezJo1Cx0dHTw9PYHfbYPWcxiVndbB2Xnz5lFUVERSUhKDBw9GIpGQmprK+vXriYuLY/bs2TQ1NZGVlUV4eDhqamqKFv+ZRra2BQUF1NTUUFdXh4uLCx4eHpw6dYr09HTCwsL+8Ow/ReXYPQEuX77M119/zdSpU3F1dcXS0hJdXV2+/PJLSkpKWL9+PZ9++ik6OjpIpVJVRPgp0NzcjEQiwcTEhFdeeYXs7GzCw8Pp1auXvJuTqakp5ubmmJmZYWFhoWiRlYKGhgY0NTURiUQ8evSIrVu3MmrUKPr168fDhw9RU1PDxsaGnj17EhkZSb9+/XB2dlaag/Lhw4eIxWJEIhHV1dVoa2szcuRI4uLiOHbsGLt27eL27dv88MMPmJmZ4eLiohp03YbI6jyPHDnC4MGDGT58OIMGDeLUqVNkZWXRsWNH/P396dKli3yorjLsy7agoKCA/Px8eaZDbW0tFy5cICIiAjU1NTQ0NAC4fv069vb2mJmZoaurq0CJnz6tDeO5c+dy7Ngxzpw5Q2BgICKRiPj4eLZu3UpsbCyzZ8+mrq6O69evExoaqqpPesr8+cwJCgqivr6eJUuW0KdPHzw9Penevbu8FlIikfyhZrQ9IAvOLl26lPnz53P16lViY2PJz89n8uTJ9O/fH39/f1544QVu3rzJsmXLmDNnjipQ+TcQiUQkJCTw9ddfU1dXR2xsLJqamvTq1QtnZ2cOHjxIeno6/fr1+9f7rX0kuT9FpFIpd+/e5ebNm/IBw7q6uowbN46ff/6ZUaNGERMTQ11dHTExMQwYMEDBEisn6urqnDx5kpiYGFavXs2VK1eYN28eAF988QWGhob89NNP8nx5Ff+e2tpatmzZQk1NDfB73VJLSwuXL1+mubkZTU1NAM6cOUNhYSE6OjryWU3KcFDW19dz8uRJysrK2Lx5M1u3bsXKygpTU1OKi4uJjIwEfq9H6NOnD2FhYSqnrg2RzUW8cOECJ06c4PLly/z2229IJBLmzZtHXV0dP/74Y7sbjv2kKCwsxM3NjYqKChoaGtDR0eHRo0eUlJTIa0JiY2NZuHAhv/32m9LPZpPdXmZmZrJ48WJmzJhBVFQUDQ0NrFu3jqFDh7J27VrmzJnDzz//TG1tLdu3b2fSpEnyRh4qnh6yJjarVq3i66+/pqamhpkzZzJ06FDeeustrl69SkhICGfPnsXa2rpd/R6yb7OxsZH4+Hi+/fZbKisrSUtLY+3atRQVFfHuu+8C4OjoyMOHD4mNjWXZsmU4OzsrUvTnhvPnz7NmzRo2b96Mra0tt2/fZtu2bRw8eBAXFxc+++wzJkyY8ET+LdWN3b/gwYMHCIJAp06dcHV15eTJk9y8eVM+bNXU1BRra2sqKipYt24dX3zxBV27dlWw1MrJ5cuXWb9+PQMGDKB79+4EBwfz448/UlxcTHBwMGFhYbi4uKjqF54gjY2NODg4IAgCCQkJdO3alTt37lBcXIy5uTmWlpZcvnyZL774Aj8/P6VrTKOlpcWJEydYsmQJV65c4aOPPsLU1JRHjx6RnJxMSUkJaWlpHD16lLlz56r2XhvROhVOW1sbb29vDAwMOH36NGZmZpiZmdGhQwdCQ0Oxt7dX1Tk/JjIHxsXFhd9++43Zs2ejpqaGv7+/PNhTUFBARkYGe/fuZcGCBUptKN+6dYsOHTrI3y83NxdjY2NGjx6NRCJh6NChrFixgkePHuHj44OFhQW3b99m/fr1zJkzR2UYP2Vk+iAvL4/PPvuMwMBAcnJyOHDgAN27d2fYsGGUlJSwdOlSJkyYgI6ODmpqakqTVfJ3kM3xq6qqYsSIEairqxMVFcWSJUtwdnYmNzeX4uJi3NzcMDc3RyKREBgYqMp8egyys7MZPHgwN27c4KeffmLx4sXcvn2bHTt2oKurS0hIyBNbT5Vj9w9JSEggKiqKgwcPUlVVxbBhw+jYsSOHDx+msLCQoKAg+bPa2tr069dP6WsLFEV5eTnff/899+7dY/r06QB06NCBsLAwli1bRlFREWFhYRgbGytYUuVBKpWiq6uLvr4+P//8M+fPn0dXV5eBAweSmppKXFwc+/fv5+DBg3z44YcEBwcrWuQnRusD38DAgJSUFExNTenVqxfa2tpoa2vTpUsXbt++Ld+TDg4OCpa6fdA6FW7lypVkZWVx7do1JkyYQG1tLUeOHEEikWBpaYlEIsHMzEzRIj93yPZ+XFwcHh4e1NbWkpaWxqNHjxg/fjzm5uaIxWLEYjHTGqorngAAIABJREFUp0/H3t5ewRI/Xd5//33Wr1/Piy++CEBmZiY///wzo0ePlmctPHjwACMjI7p16wb83i24T58+qmBPGyASiUhNTSU6OpqpU6cyZMgQhg4dSmFhIdu2bWP8+PGEhITIDWtZrVh7ceoALl26xHfffUdwcDD29vbU1taydetWBg4cyG+//caxY8f49ttvcXJykgd2ZKnWKv47fw4MODs7Y2xszI8//sjUqVPx8PCgpKQEExMTevXqJS8FeBKoHLt/QHJyMitWrGDevHmUl5cTHx/PmDFjsLOzw9LSkgMHDuDj4yNv5a6pqYm2traCpVZO6uvr0dfXp6qqiry8PEQiEa6ursDvXaz69++PtbW16gB9wohEIs6dO8fx48d57bXXqKio4Pz58+jp6fHyyy/j6emJm5sbw4cPx9vbW2min63fIzU1FS0tLSZNmkRxcTEnTpzAxsYGGxsbfv31VxwdHRkxYgSmpqYKlrr9INuXS5cuZe7cuRw9epQbN24wcOBAfH19+fXXXzl06BDh4eFKX+/1NJFKpcyfP5+mpiZefPFFSktLuXDhAi0tLQQHB+Pt7Y2Pjw+GhoaKFvWpM3z4cI4cOcLOnTsZO3Ysrq6uXL58mejoaNzd3bl69Srr169nyJAh2NraynWIlpaWokVvN8THx7Nx40Y8PDxwd3cHoE+fPhw7dgxfX1/09fXbbeC3pKSE1atX07FjR1566SUA9PX1qaysZO3atRw+fJjXX38dHx8foH05vP8GkUhEcnIyv/zyizyjqUOHDiQnJ3P27FkMDQ1Zt24d06ZNw8PD44n+2yrH7jERBIFjx47x9ttvc+PGDQ4fPszKlSsxMjLi5s2buLq6Eh4ejpWVlaJFVXpKSkpYvnw5xsbGDB48mAcPHpCTk0NDQ4M8MtqhQwesra2VxrF4lqivr2fDhg307dsXV1dXKisrSU9P58GDB/Ts2RMrKyulmlMHfxxpsHbtWkaOHImpqSlBQUHk5OSQlJREWloa69evZ/z48apuq22IrKZu27ZtTJo0ifr6euLi4li8eDGGhobcuHGDsLAw/Pz8nmh0tD3w3/TnvXv35HVJPj4+lJeXk5CQgIaGhnyAs7J89/+L5uZmxGIxo0ePZvfu3ezatYtx48bRp08f8vLy2Lt3L5mZmbz99tvylvDKvibPAn/erx4eHmhqarJy5Up69+6NhYUFGRkZ8uHQ+vr6CpRWsVRWVlJYWEhOTg6dO3eWl0wEBAQQEBBAZGQkPXv2VNlQf5PWqb8zZ86kX79+JCcnk5WVhYGBAW5ubly9epUTJ07w7rvvyku3niQqx+4xkRXgbty4kczMTFasWIG1tTVJSUls2LCBwMBAVcviNkJWz5WXl4eRkRHh4eFUVVVx9uxZGhoa5Dd3oDpMnwayIfB6enq4ublha2tLWVkZubm5uLu7K61Tc/ToUbZv387mzZuxtLQkJyeH2tpaRo4cyaNHj5BKpcyYMUM1p66NaW5uRk1NjZycHM6cOUN8fDyLFy/Gzs6O+Ph49u3bh7+/v2rMzGPS2qArLS2lqakJiUSChYUFy5YtQ19fHxcXF7y9veUzrVp3FFRGZGvS0NDA3bt3kUgkjB07ltjYWHbt2sWECRMICwujf//+DB06lK5du6oM4zZElo69c+dOfvnlF5ydneXdBmfOnElZWRlXr17lhRdeULp5qv8Xsn147do1+UiSkJAQKisryc/PR09PDxsbG8RiMSYmJkoXnH3aiEQiUlJSiIuL46WXXmLIkCGMGjWKzMxMkpOTmTx5MuHh4fKA+NPQCyrH7m9SV1fHo0eP0NTURE9Pj5SUFPr27UtISAhpaWksXLiQyZMnq5qjtAG3bt1CR0dH3pymrKyM9PR0bG1tCQ4O5vbt2/j4+KhS4J4CpaWlHDp0CHd3d3R1dbl//z4rV65k0KBBmJub07lzZ3x8fJSqIUVrxSsIApmZmXTp0kVes7Vhwways7MxNDQkIiICHx8flfPQxhQXFxMfHy8fAr969Wrmzp2Lu7s7eXl5fPPNN0yYMEHVqOIxab33T5w4wf79+9m9ezf6+vq4u7tjYmLCtWvX8PDwQENDAw8PD6UN6LRGJBKRmJjIV199xf79+8nKyqJr1668/vrrxMbGsnHjRl588UW0tLTkdXYqw/jp03pW2BdffEF4eDi3bt0iOjoaBwcHhg0bhra2Nvv27ePll18mIiKC5ubmdjVqQub0zp8/n5qaGpYvX054eDhOTk6UlpaSlpaGvr6+qifEv2DXrl1s2rQJb2/vP6T+bty4kZ49e2JkZIS2tvZT23cqx+5vcOrUKT7//HPOnTtHZWUlgwYNQhAEjh8/zv79+0lMTOS9994jLCxMFZV7CrRe0/LyclavXk1ZWRnu7u6YmppiamrK0aNHuXDhAp06dWLQoEGqpghPgZSUFJKSkkhNTSUmJgYdHR1cXV3lA1y7dOmCRCJRqhvr1ntPKpXKZ9Zt3LiR8vJyBgwYwJtvvklpaSkSiQQXFxfV999GyH6bjIwMli9fzrFjxzAxMWHYsGFoaGiwdetWEhMTOXbsGNOmTaNfv34q/fwYtF6rmJgYTp48yZQpUzA1NWXz5s0UFBSQlJREcXExffv2pUOHDgqWuO3IyspiwYIFzJ49m7Fjx3LhwgUyMzPp168fo0aNYv/+/Tg6OqpKMtoYWaOUDRs2MGXKFAYPHkz//v25c+cOa9asYcyYMfj7+6Opqcm8efMICgrCysqqXemEnJwcFi9ezNq1aykrK6OkpIRRo0Zha2uLhYUF165dw9vbWxWcfAxkulL23969eyOVSlm3bh3h4eEYGxuTl5fHkSNHGDNmzB+66D4NVEOV/g+uXbtGbGwsU6ZMQUdHh3nz5iGVSnnzzTcZNmwYt27dQk9PDwsLC5XR8BRobGzkzp072NnZcfnyZerq6nBycuLq1avExMQwfvx4nJycCAgI4OrVq0qfAqQoMjMzWbRoEd988w3vvvsuW7ZsIScnhxUrVtDQ0ICvry99+/ZVtJhPlNbf844dO8jLy0NdXZ3hw4ezbds21NXVEYvFJCYmcuHCBcaPH69gidsXsuL05cuX88EHH3D48GGOHz+OVCplypQp9O/fH7H491GtnTp1Uunnx0S2VpmZmVy4cIEFCxZgYGBA165d8fHxkRvLxcXFPHz4UMHSti3V1dUEBQXJmx589tlnTJw4kU2bNvH666+zZ88eBUvYfqmsrCQuLg4XFxdCQ0MBmDZtGjk5OVRUVGBvb8+rr76KWCxuV8EIGTdv3mTcuHEUFRVx/Phxli1bhrGxMadPnyYgIIAPPvhA1ezvMZHdgqalpVFdXc20adOYMWMGjx49IjIykhEjRiASiZgxY0abBHtUN3b/D65cucKkSZMYMWIEo0aNonPnznh5ebF06VIqKysJCQnByMhInnqiMhqePAUFBcTExJCTk8PSpUt57bXXCAwM5Ndff6WgoIBLly4hlUrZuXMn06ZN+0NdnYonQ2FhITExMURGRsoPSk9PT4KCgnBxcaGhoYEhQ4YoXVvz1o1STpw4weTJk9m/fz+3bt1i4MCBiEQitm7dysaNG/n2229xdHRUsMTKz/379xEEATU1NaRSKd9//z2hoaEMHTpUXmO7detWJBKJfOaSrDOjSj8/HlKplPLycmbOnMndu3fp3bu3PIpvYGCAlZUVQ4cOZfTo0e2uGc3FixfZu3evvIuglpYWLS0t6OnpPbW6GRX/nT+nyru4uMjrP11cXOjSpQuZmZns2LGDkSNHyvWBh4eHvH6sPVBSUoKhoSF37txh06ZNnDlzhtWrV2NjY8P58+dZv349AQEBqpu6x6B16u9//vMfIiMjycvLIyUlBQMDAyZMmEBzczPbt2/n008/JTQ0VN5w6Wmicuz+By0tLWhoaJCfn09cXByvvPIKABYWFri5ubFy5UrCwsIwMDBQKfCniIWFBfHx8Wzbto033niD0NBQxGIxjo6OSKVSCgoK+OWXX3j77befSneh9oxsXs2RI0eIj4/n4cOH9OzZ8w/RPFtbW0JCQnByclI6Y0YQBKqrqzlw4ADLli0jISGByspKoqKiaG5u5t69e5iZmTF06FClc2qfRWpra1m2bBne3t5oaWkhFotJS0vDwMBAfnPi5eXFsWPHqKmpwdHRUVVn+5i0/oZFIhH6+vo4OzuTnp6Ojo4OdnZ26OjoAL+fkWKxWP5nZUW2JkVFRRQWFmJnZ0e3bt1ISUlh48aN+Pr6kpuby7p16xg+fDi2trZKpQefZWS/TUpKCjExMezduxcbGxvCwsIwNTVl5syZ5OfnU15ezosvvoi3t7eiRW5zpFIpjY2NjB07FmNjYwICAkhOTsbT05MOHTpQUVHBN998w9SpU/H09FS0uM8VsqyRgwcP8tJLLzFo0CCGDBnC9evX2b17N2PHjiUgIIDGxkbmzJnDsGHD2iSYoHLs/gvx8fFs2bKFY8eO8eqrr1JeXs7PP//MqFGjEIlEWFlZMWbMGCwsLFQK/CkhU9g1NTWoqalhbm5OYWGhvGmKRCLB3NycIUOGMGDAALp37650joWikK1jXV0d2tra8shmQUEBUqkUW1tb+QwmkUiktANdRSIRmpqapKenExMTQ3FxMT/88ANqamrs3LmTvLw8eXBHxdNFEAR0dHRwcnKiqalJ3sCmoaGB7777Di8vLywtLSksLCQzM5P79+9z8+ZNQkJCFC36c0Nr/Xn48GESExOprKzEx8cHe3t7YmJiALC2tkZXV/epR52fFUQiEQkJCcyaNYszZ86QlJSEi4sLr776KhcvXiQxMZHU1FRmzJhBYGCgosVtV8i6lC9ZsoQBAwZw7949tmzZgrW1NUOGDMHS0pJ9+/YxZMgQhg0bRktLS7tqlALIh4kbGBiQkZFBREQEVlZW8tmrhYWFvPbaa6oa5H9IQkIC69evx9HRkZ49ewLg5+fH/v37cXV1xdTUlMDAQB49eoSzs3ObOHaqGrs/cfr0aVavXk1UVBSfffYZW7duZcGCBXz22WeMGDGC2NhYRCJRu8zNbitkyiUhIYETJ07wzjvvMHDgQFavXs327dvR1dXl3r17JCYmMnPmTFU73ieIbO1Pnz7N9u3bcXBwQEdHhxkzZlBdXc358+dpamoiMjJSKWf/yBqkyJB1wq2pqWH27NmIxWJiY2OJjo5mxYoVCpS0/VBRUcHKlSv5+OOPsbGxYc2aNSQmJqKmpsbgwYOpra3l888/p3v37vKbk6KiIvLy8v7ye6r437ROPT527BiRkZEsX76cO3fu8Oqrr/Lo0SO+++471NTUGDduXLtZ17KyMg4fPsy6deuwt7fn008/Zd26dbz99tssXLgQqVTK/fv3kUgkKsNYAcTHxzN16lQGDRrEsGHDiI6OZs6cOezfv58xY8agrq7Op59+ipWVVbsL9FRVVWFhYQGAk5MT+/fvp6ysDF9fX3r06IGGhgaNjY2qvfsY/HmdXnvtNQCioqLw8/PDy8uLgoICbt++jZ6envz5Dz74oM1kVN3YtUIQBLZt28asWbO4evUqGRkZfPXVV5iamuLr60t+fj7m5uaqTldPGZFIxLlz5/juu+94++235cPG/fz8uHnzJomJiRw8eJAJEyaoauqeMLK1X7JkCbNnzyY5OZni4mKGDh2Kp6cnFRUVpKWlERAQoFRtzTMyMjAxMUFDQ0OeggqgoaGBtbW1vAPg0aNHSUlJISoqSlVT10ZoaWmxZs0aLl26RGBgIIGBgdTW1nLw4EEMDQ0ZNmwYvXr1wtvbmwkTJlBWVsaKFSt49913Vd1xH5P8/Hx2797Nhg0byM3N5e7du3z66ac0Nzdjb29P586dcXd3bxe31IIgUFFRwcyZM2lpaWHQoEFIJBKCg4NJTk4mMTEROzs7LC0t0dDQaHc3Qc8Czc3N7Nq1Cx0dHXx9fZFKpbi7u5OdnY2npydGRka4uLjQsWPHNrsteVaoqKhg7ty5HD16FDc3N5ycnKisrGT37t0MHDhQns6uGsfx92md+nvq1ClSUlLw9/fH09MTkUjEJ598QlFRESUlJYwdOxYfHx+FrKvKsWuFVCrl8OHDnDt3jjNnzrBo0SLs7Ow4ePAgsbGxzJ07Fzs7O0WL2S6IiYkhLCyM8PBwHj58iCAIiMVi/Pz88PHxYejQoXh5eamiTE8QqVRKS0sLO3fu5NVXX+Xhw4ccPnyYqKgoDAwMuH79OqGhoXh4eChdcGPOnDmsXLmSiRMn/sG5EwQBIyMjeSTO09OTF154QTV8vA24fv06sbGx+Pj4MHr0aGJiYjhz5gzBwcH06tWLqqoqjh07hp6eHj169MDS0pK6ujqWL1/Ol19+iYuLi6Jf4Znnz/qzpaWFuro6Tp06xYULF1i3bh0aGhrs3buXBw8e4O/vr5Q39a2RrYksM+fhw4dkZ2djaWmJubk5enp6BAcHk5SUhK+vL2ZmZqozqI2RzRXW1tbGwMCADRs2YGJigrOzM5mZmezbt4/BgwfLHbmuXbu2C6dOtnelUil6enr4+vpy8eJFzp07x969e3Fzc+PmzZv4+Pioss7+AbL5lUuXLqV///7ykq3Ro0fTs2dPJBIJP/30E1OnTmXAgAFt0ijlv6Fy7FohFouRSqXs2bOHSZMm4e/vT3p6OlFRUbz44ouqCH0bIQgCe/bsQVNTEz8/PwRBQF1dnYyMDEpKSnBxcZEbF6oD9ckh6zZYVFTE0aNHiYuLY+nSpdjY2JCUlMS+ffvo2bOnUnbNGjZsmNyQHTNmjNy5kyllWW2npaUlenp6Cpa2fVBeXs6MGTPkeiAyMpJdu3bJnbvevXtTWlrK0aNH6devHzo6OkgkEsLDw7G2tla0+M88rZ26S5cuoaamRlNTE8uWLaOiooKYmBjU1NSIjY1l06ZNTJgwQalv6mTBHJFIxOXLl8nOzsbOzg5PT0+kUin79+/H0NAQMzMz9PT06N+/v+pGuI2orq4Gfs+giIuLY8GCBWzfvh1LS0tCQkIwNDTk66+/5sqVK+zYsYOPP/4YLy8vBUvdtsi+5+TkZKKjoyksLMTCwoIJEybQrVs3Wlpa2LZtG+np6QwbNgxzc3NFi/zcUVxczPfff8/SpUupqqqiuLiYlpYWoqOjGT9+PJ6enujq6vL5558THBysuAC4oOIPNDQ0CJs3bxZCQ0OFmTNnCkOHDhUSEhIEQRAEqVSqWOGUFNm6Njc3Cw8fPhQEQRASExOF/v37CydPnhQEQRAyMzOFgQMHChkZGQqTUxmRrX1aWpqwf/9+QRAEYc+ePUJISIgQFxcnCIIg5ObmCpGRkcKpU6cUJufTQPbusj0nCIIwefJkISIiQmhsbBQEQRBaWloUIlt7p7m5WRAEQcjOzhZcXV2FtWvXCoIgCE1NTcJLL70kvPfee0J9fb0gCIJQWlqqMDmVgc2bNwuvvPKKUFZWJgiCIJw5c0bw9/cX5syZI3zxxRfC0KFDhStXrihYyqfLrVu3hNjYWEEqlQpJSUlCv379hJEjRwovvPCCkJ2dLQiCIOzcuVMYPXq0cOjQIeHRo0cqe6CNaG5uFl566SXhs88+Ey5fviy88MILQlpamrBt2zahb9++wuHDhwVBEISysjKhoKBAuHr1qiAI7dNeS0pKEoYOHSpcuHBB6NevnzB79mzh0aNH8v9fVlam0pf/goqKCiE/P1/IysoSIiMjhcrKSuHatWtC165dhcjISPlzP/30k1BUVKQwOUWCIAiKcSmfba5evSofeeDg4KBocZSWlpYW1NTUiI+P5+TJk1RXV/Puu+/i7u7OgQMH+Pbbb+nVqxdFRUW8//77hIWFKVpkpeP06dMsWrSIuXPn4u/vD8DatWtJS0ujpaWFBw8eMGXKFKXqmtX6PW7fvo1UKpUXmU+ePJlbt26xf/9+tLW1VQ04FIRs3bOzs3nppZeYNm0ab7/9Ng8fPmTixIlYWFjw/fffy7uyqnh8du3axb59+1i3bh2GhobcuHEDExMT7ty5w4ULFxAEgYCAAKUvQTh27Bhr165l/Pjx5ObmMnnyZBwdHfn666+pqKjgrbfewsPDg+joaFxdXeXjNVS0DRUVFUyfPh19fX169+7NG2+8AcCBAwdYu3YtU6dOZeTIkQqWUnEIgsD9+/f59NNPef/996mpqWHRokWsWbMGCwsLqqur/5Jpoyxn+dNGZqO2Zs+ePVRVVTFt2jTOnTtHXl4eXl5e+Pr6KkjKP6Jy7FQohLt376KtrY22tjaJiYmsXLmSxYsXs2jRIvLy8vj2228JCQnhxo0bSKVSpFIpXbp0USmjJ0xxcTHvvvsuy5cvp2vXrmRlZZGfn8+IESNoamqivr4esViMnZ2dUq791q1bSUhIQEtLiy5duvDJJ58A8NZbb5GXl0d8fLx8tIOKp49sj128eJHy8nLMzMzo2bMneXl5TJgwgRkzZjBlyhQePnxIfn6+au7Sv2T16tV06tQJfX19+X4Xi8V89dVX7a5G8ciRI2zduhVtbW2+/PJLunTpAsD8+fMpLi5m+vTp7XIOmiL5cwBu+vTpaGlpsWrVKnmN2N69e1m5ciW7d+9ul+mFsjVqaWlh8eLFGBgYcO7cOebNm4eDgwNHjx7l9u3bvPjii6og2N+ksrKSnTt38t577wF/7ZZ98uRJpk+fznvvvce2bduIiooiICAAmTulaDtJVWOnos0pKipiwYIFuLm5yfO+P/vsM65du8bFixcZNGgQixYtwsHBAS8vLwwNDVUjDZ4SjY2NVFRUUFVVRWJiIomJiRQUFHD27FmGDh2KsbGxvK5G2dY+JiaGEydOsHTpUrKysoiJiaGsrIz+/fszdOhQLl68iIeHh1LXFT1ryOZSffXVV3Tq1IlZs2ZhZGRE3759CQoK4sMPP0QsFhMQEIClpaWixX3uycnJ4cSJE6SlpRESEsLIkSO5e/cuXbp0aRfrKzOKKysr8fLywtjYmDNnzmBiYoKlpSU6OjoEBweTnZ2Nm5tbu3QcFIXQqmYsJycHLy8vQkJC2L17N6Wlpfj4+KCpqUn37t0ZPHiwPOOivSBbn4cPH6Kuro5YLOb06dNs2LCBPXv2YGNjQ35+PgsWLGDw4MHyQIWK/zeCIHDt2jV+/vlnSktLCQwMlDekkdlADg4O2NvbU1NTwyuvvELv3r0BnpnOuCrHTkWb0tTUxCeffEJoaCg9e/bk5s2b9OrVC6lUyoIFC1i0aBEDBgzg+PHjHD58mJEjR6Kjo6NosZWW5uZmrl27xtWrV+nfvz+vvvoqHTt2pLS0lNDQUKVNQayqqiIlJYU5c+awd+9eSkpKWLVqFQsXLuTatWv079+fQYMGqZy6NqasrIwFCxawYsUKdHR0yMvL4/3330dPTw8LCwsCAwMxMDCgc+fOihZVKfD29sbDw4OJEyfSrVs3cnNz2bNnD2PHjm033S8TEhJYvnw5PXr0wMfHB21tbfbt24eOjg7m5ubo6uoSGhqqcuraGJFIRHx8PN9++y0RERHY2dmhp6dHv379+Omnn8jLyyMgIABNTU309PSeCYO6Lbh37558vurp06dZt24dKSkpiMVixo8fz9WrV9m2bRu5ubns2LFDVcLymIhEIiwtLTEzM+PAgQP8+uuv+Pn5yW9FZTaRk5MTvr6+dOrUScES/xWVY6eiTVFXV+fBgwccPnyY1atX89prr2Fra8v169cpLS1l/PjxpKen09zczAcffPBMfjTPI+Xl5VRWVv4hz14QBHR1dfH29iYiIoIuXbpQUFDAggULGDdunFJ1gf1zGqlEIsHDw4Oqqiqio6P5/vvvMTMz4/r165w7d47IyEh0dXXbjbHwrNDS0kJ9fT1NTU2sWbOGqKgobGxsOHToEE1NTXh4eNC5c2elTAt+WvyvtZK14jY0NEQQBHbs2MHGjRuJiopqF9F9kUhEamoq3333HR9//LF8JmrXrl3R1NTkp59+QiKR4OzsrEphUwBVVVXMnj2bVatW4ebmRkZGBqdOncLW1paRI0fyww8/EBgYiImJSbvRBXV1dcyaNQttbW2qq6v56quveOGFF6ioqKCwsJDy8nK++OILDA0NcXd3Z/DgwfK6eRV/n4SEBHbu3Imuri6pqancunWLoKAgxGLxH5y7Z3XfqStaABXtjw4dOpCbm4unp6e8fsna2prExEQ+/PBDzp8/z8KFC+nRo4eCJVUedu3axaFDh1i9erW8dkamlNTV1REEgbS0NL7//numTZumtI1S4uLi+O2337C0tJSnAhcVFQG/19hoaWnxyy+/YGhoqEiR2wWNjY1kZWXRu3dvsrKyKC0tpV+/fuzZs4empiaSkpJQU1MjJyeHVatWMX/+fPnfVYZ92Ra03vtXrlxBS0sLIyMj9PX1UVf//49/TU1NDA0N+f7779tVMO3SpUuMGTMGb29vHj58iFgsRl1dncjISMRiMdbW1mhoaChazHbJ/fv3aWlp4cKFC2zYsIF79+5RWVlJRUUF//nPf9i7d698uHZ7QV9fnx49erB79266dOnC5MmTiYiIoG/fvsTFxXHw4EFGjBhBRESEokV9bikpKWHJkiWsXLkSKysrcnNzWbNmDd9//z3Tp09/LoI8qhs7FW1KTU0NUqmU0NBQ1NXV2b9/P507d6Zz584MHz4cIyMjxo8fr4oyPWF69epFRUUFW7duxd3d/S/zl0QiERKJhN69eyvd4HfZe2zdupW9e/diYGDAli1bMDY2JjAwkIyMDDZu3Mi5c+f46KOPlL4D4LNCXV0d+/fv54cffuDkyZOMGTMGOzs77O3tOXv2LLdv3+bq1ausWbOGmTNnEhgYqGiRnztke3/z5s1s27aNlJQU0tLS6Ny5s/z2XvatOzs7t7uAxqFDhygpKWHgwIGoqakhFotJT08nOzubwYMHt4s6w2eJ1nVMRkZG1NTUkJOvyFRPAAAgAElEQVSTw+DBg3nrrbcwMDDgzJkz9O3bFw0NDaUtFfhvyNbG19eX+vp6jhw5AoCPjw96eno4OTkRHR2Ns7MzNjY2Cpb2+eXWrVukpKTw+uuvo62tjYmJCTdv3iQ6Opra2trn4hxSOXYq2oySkhJWrlyJiYkJ/fv3x8TEhNLSUlJSUujUqRNdunTB3t6+3RVBP21kqQNBQUFcuXKF7du3/8W5EwQBbW1tuWGnLE6djPT0dPbu3cuWLVu4dOkSdXV1zJgxA4DIyEj8/f154YUX6Nixo4IlbT/o6upSXl4ubyH/+uuvA7/f3vv4+JCRkYGWlhZjx46lT58+ShVsaEtiY2M5efIkmzdv5sKFC9y7d48XX3xRrhfay5rK9k95eTl1dXXo6+vj4OBAdHQ0v/32G97e3mRkZPD5558zZMgQbG1tFS1yu+DGjRvs2rULHx8feZMK2W/l5+fHgAED6NKlC2lpaSxbtow33ngDJyenduXUyZCti7u7O2pqamRmZmJgYECHDh24c+cOu3fvZuTIkX8ZbaDif/Pnc0VHR4fU1FSam5vldZ13797FyMiIgQMHPhfBHpVjp6LN0NLSIisriytXriASifDx8cHa2prCwkKSkpIICQlBXV293RgaTxuZwqqpqaGurg6JREJISIi8uNrT0xNTU1NA+Ry51ty+fRtNTU3U1dWJi4vjwoULrFu3Dk1NTWJjY9HU1MTe3l7VpKeNaH2QGhsb4+bmRklJCUlJSYSHh6OmpoZEIiEiIgI/Pz/5Daoy79EnyZ8NlezsbEaMGMHBgwfJy8tj1apVqKmpkZGRgZmZ2XORWvQkEIlEnDp1ioULF5KRkUF8fDwuLi707t2btWvXcvbsWY4fP85HH31EUFCQosVtN9y4cYPPPvsMQRDo2bMnIpHoL23jCwsL+eKLL3jvvfcICwtrN0Ge8vJyzp49i6Ojo/x9Ze/eo0cPfvvtN2JiYjh16hTZ2dlMmjQJPz8/BUv9fCESiTh37hxJSUncuHGDHj16cO/ePdLS0khOTqahoYEVK1YwZcqU52bcicqxU/HUuXXrFg0NDRgZGeHr60tBQQHZ2dno6Ojg6emJnZ0dAQEBWFhYtAtl3Ra07vg2Z84czp8/T0JCAhEREQQHB3P9+nXWrVuHj4/PX9Iyn3dap/Ps2LGDX375BVNTU5YuXUpTUxNbtmxBTU2Nffv2sWnTJkaPHi2fiaTi6SMSicjIyCA/Px+RSESfPn3o0qULCQkJnDt3DhMTE/7zn//g7++Pvr6+Sic8JrL1OnfuHHZ2dsTHxxMVFYW2tjarV69GTU2N7du3s337diIiItrNnMa0tDRWrVrF2rVruX37Nrm5ubzyyit06dKFsWPHEhgYyKBBg+jRo0e7cRwUjVQqxcLCgm7duvHdd9+hrq6Ol5eX3LmTtY83NTUlJCQEDw+PdvPbSKVSzpw5w9KlSzExMcHZ2fkvzp2npyfNzc0UFBTw5Zdf4uPj027W598iW6fMzEw+//xzdHR0SEtLIzc3l6lTp2JoaEhFRQUlJSVMmjRJPtLgeUDl2Kl4agiCwK+//sqiRYu4e/cuNjY2GBkZ4enpycGDBzl16hQmJib4+vpibGysaHGVCpFIJD8UVq5cSUtLC5s3byYrK4thw4bRp08fSkpK6Ny5M9bW1ooW94kiO9ROnz5Nfn4+U6ZMwdXVFalUSnx8PI2NjcTHx/PLL7+wdOnSdtUsQpHIDtILFy7w0Ucf0djYyLFjx6ivryc8PJzu3buTmJhIbGws7777Lh4eHioD5R9y9+5dFi5ciJ2dHf379yc2NhYTExO6devGkSNH2L17N/Pnz8fKykrRoj5VWgd5MjMzCQ0N5fr16+zZs4eoqCjMzMzIysrC3NycDh06IJFIANXtcFshEomIi4vj5MmTGBkZceDAAR4+fChvLy+VSuXPycZvtJffRiQS4eTkJO/QamZmhr29/V+cO3d3d3r16iUvI2gv6/NvEYlEXLx4ke3bt/Pll18yZswYHBwcOHHiBNnZ2UycOJGgoCCCgoJwcHB4rhxmlWOn4onTOtImkUh49OgR6enp3Lt3D1NTU4yNjdHQ0OD69evyIdgqniz3798nKSmJKVOmUFRUxO7du4mJiWHz5s0kJSUxZMgQwsLClM6pg9/3X21tLSNHjqSxsZEJEyagrq6Ot7c3JiYmPHjwAG1tbd59910cHBwULW67QXaQHjhwgDlz5jBx4kRMTEzYsWMH9+7dIywsjCFDhtC3b1/Vrclj0tqBgd+/gUuXLlFTU0NgYCAREREcOHCA3NxcSktLmT17Nk5OTgqU+Oly//59+ayvnJwcLCwsSEtLY82aNZSUlLBixQqsrKxITk5myZIlhIaGqm7tFUBpaSmzZs3i008/ZcyYMQQEBLB8+XJaWlrw9vZ+ZgY+tzUy3Xf69GlSUlKoq6sjOTkZfX19unbt+hfnTtlnTj4Nmpqa2L9/P9u2bWPAgAHY2dlhaGiIs7Mzhw4d4uzZs/Tv31++B5+nfahy7FQ8UWSKJj09nZMnT6KmpkZwcDDq6uokJSVRUlJCWVkZu3btYsaMGfLZQSr+Pa0NYQ0NDezt7dHW1mb58uV8+OGHODo6UlNTQ3JyMoGBgUqXgilDJBKho6NDWFgYP/30E9ra2vj6+gK/z6jy9vbGx8en3XUAVASVlZUkJSXh5OREY2Mjq1ev5sSJE0yePBk9PT3Mzc2xtrZm48aN/Pbbb/j6+qpuTf4BsrWqra0Ffm8AYGxszNKlS3F1dcXR0ZERI0YwYMAA+vbtq/TDttPT01m8eDEikYj58+cTEhJCr169OHz4MNra2gwaNIjz58+zdOlSpk+fjpubm6JFbpeUlZWRmprKO++8g6amJtbW1jQ0NLB8+XLU1dXleru9IRKJKCgoYNasWXz++ef07dsXKysrfv75Z4yNjXFwcHjunI1ngdY2krq6Ot27d+f+/fscOHAALy8vzM3NMTQ0xMXFBQ8PD8zNzZ/LNVY5diqeKLJC1Llz51JfX096ejqVlZWMHDkSY2NjysvLOXPmDK+//vpzlbP8rCNTWNnZ2SQmJiKRSDA0NEQkEhEdHU3fvn0pLy8nISGBxYsX4+zsrGiRnzrm5ub06tWL//znP2hpaT03hc/KglQqJT09nU6dOqGurk6HDh3o2rUr+fn5pKSk0L9/f7S1tTE3N8fOzg5HR0elTw180hQUFHDs2DE8PDxIT0/nm2++4dKlS5ibm9OjRw8ePHhAS0sL3bp1kw8kV1NTey6NlcfBzs6Offv2sX37dr766iu8vLwQi8VEREQQGxtLSkoKFy9e5K233iI0NFR1O9xG/HmdjYyMOH78OPX19bi7uyMSiaiursbCwgJ/f/92PXrm4sWLlJeXM2nSJExNTbGxsSE3N5cdO3Zgbm7eLs7wJ0nrS4fMzEwqKipwcHAgICCA8vJydu7cSffu3bGwsMDc3Py5Dn6pHDsVT5Ti4mJ+/PFH5s+fz8svv0xLS4s8/adfv34EBwczcOBAnJycVIfpE0QkEpGSksLs2bO5ffs2CQkJqKmp4eHhQXl5ORs3biQuLo6XX36Znj17KlrcNsPCwoKgoCA++OAD9PX18fDwULRI7QaRSETHjh3R0tLik08+4c6dO4SEhODt7U1aWhonT54kLCwMHR0dOnXqpJRpwU8TqVRKWVkZfn5+VFVV4erqiq2tLdXV1URFRdHU1MTZs2fJy8tj6NCh8kHbyq5zZWmp165dQ1dXl+PHjxMeHo5EIkFTU5NRo0YxZMgQQkJC6Nq1K6D8a/KsIAv8nj9/nuLiYrp37y5vpnTs2DHEYjHffvstM2bMwNfXt13ZCP/tXRMSEjAxMaFTp05oa2tTUVGBvr6+vKO4ir+PSCQiMTGRRYsWYWJiwr59+yguLiYoKAg/Pz+uXr1KTEwMQ4YMQUND47nedyrHTsUTQRAE6uvr2bRpE+fOncPLy4vOnTvj4uJCY2MjFy5coLS0FC8vL/lH8zx/OM8a169f5+eff2bu3LlMmjSJu3fvcuHCBZqampg0aRIDBgxg+PDheHp6tqvDEn537sLCwrC3t1fVc7YxYrGYBw8eIJVKSU1Npba2lqCgIHr06EF8fDwnTpxg8ODBKn3wmLS0tKCmpiZPXZs/fz6FhYWMHDkSf39/unbtSnNzMzk5OVy+fJkxY8bIU1yVFZleq66uRiwW06dPHyIjI7ly5QqrV69m4sSJXL58mU2bNhEUFIS6unq7nIWmCFpnlHz44YeYmJiwf/9+bt26xeuvv461tTWXLl2ioqKCl19+mYCAAKB9OdwikYisrCyKioqora2lW7duFBcXU1hYSGFhIS0tLaxevZr3338fT09PRYv73JGdnc3SpUv54YcfuHfvHqmpqQiCwMWLFwkODiYgIEBpurOrHDsVTwSRSISWlhZmZmbU19dTWlqKsbExFhYWdO3alQcPHuDv7//c5iw/q0ilUu7evcvixYspLCzEx8cHOzs7PD09qaqqIiEhgcbGRry9veXNAdrj+pubm6ucujZENvwaQFtbW55SlZiYSH19Pb1798bDwwNPT0+VTnhMamtruXPnDgYGBiQlJfHw4UM6d+5MTk4OWVlZ9O7dGzs7O7p3786QIUMYM2bMczFU998iazYxe/Zsjh8/zp49e/Dx8WH48OHk5+fzzTffkJSUxLBhw3B0dFQ5dW2IrBvuqVOneP/99xk9ejQBAQGsWrWKmzdvMmrUKMLDw+nduzeOjo7tKvgoe9fU1FQ++OAD6uvrOXbsGBoaGrz44ovcvXuX1NRUsrKymDx5stzpVfF43L9/X57dsHLlSlasWIG6ujrbt2/n2rVrREREYGRkpGgxnwgqx07FP0amkHJycsjIyEAsFuPk5IStrS0FBQUUFhYikUiwsrLCxcVFPgxbxZND1ijE0tKS0tJSGhsbMTExwcTEBE9PT27fvo23t/dznS+u4vmgoqKC6dOnM2zYMNTU1P7QqVFLSwsrKyvU1NQ4evQoDQ0NBAUFKW0Dn6eJrEV3cnIyu3fv5p133sHR0RENDQ2ys7O5dOkS/v7+iEQiNDQ0lP6mTrbPcnJymDdvHl988QWTJ08mOzubw4cPExERQUREBE5OTgwfPpyAgIB25Tg8CwiCwA8//MC2bdsYN24cFhYWGBsb07t3b5YsWcK1a9fo27cvampqQPsKPsr2bmxsLLNnz2b8+PEYGhqyadMmNDU1GT16NIMHDyY0NBQXFxfV3v2HGBkZYWlpyZ49e/Dz8yM4OJiqqiokEgnjxo1TqrNI5dip+MfIcpbnz5+Pmpoa27Zto7Gxkd69e9OpUycyMjIoLCzE19cXTU1NlTJ6QrR2qJOSkqiursbFxQVXV1fi4uKoqalBX18fU1NTvLy8lEphqXh26dChA5s3b+bAgQOMHj0asVj8B+dOW1sbS0tLNDQ0cHV1VQUb/iG2trbExsZy4sQJPvzwQ3r06IFIJMLGxgZdXV2Sk5O5fv06/v7+ihb1qVJWVoaWlpa8drCoqAg1NTXGjRuHhoYG4eHh7Nmzh5s3b9K7d286duwoDy6qzqKnT2sHRCQSERYWxrVr19i5cyfDhw9HU1MTIyMjgoOD6dixIzY2NgqWWDHcu3ePVatWkZCQwNSpU9HR0cHCwgJLS0vWrVtHU1OTqoTlX/BnR/jatWts3boVQRBYt24dr732mtI1VlM5dir+MVlZWSxcuJANGzagpaXFiRMnaGlpobq6mj59+tC5c2c8PT2xsrJSKaMnhExJJSQk8NVXXyESiUhLS+PUqVOEhYXRo0cPDh8+zN27d3Fzc0NTU1PRIqtoB8hSLydOnEhsbCzbt29n7Nix/9W5c3JyahepgU8S2XcvW0sdHR0MDAzIyspCT08PCwsLtLW10dTUxMrKipCQEKW/qVu3bh36+vpYWloiEokoLCxk7dq1DBw4EAMDAwAaGxvR1dVVNU1qY1p3IMzPz+fq1as4OjoycOBAMjMz2bx5M4MGDUJLSwtjY2NsbGza7U2Uuro6Dg4O5OTkkJqaysCBA9HS0pKPgrG3t1fZUP8A2W2cSCT6w94yMzNDV1eXjIwM3nrrLYKCghQs6ZNHJAiCoGghVDyfXL9+ncbGRmpqali2bBlr1qxh165d7Ny5k5dffpk33nhDnlqh4t/x8OFDuZNWXV3Nxx9/zEcffYSrqyslJSUcOnSI+vp6Zs2axZkzZzA2NqZbt24KllpFe2XixIk0NTWxZ88euXOnqmn6Z7Q2SoqKihCLxdjb2wOwcuVKcnJyePPNNykoKKCqqooZM2ago6OjSJHbjPLycl5++WUOHjyIRCJh8eLFpKam8sknn9DS0sLChQuZNWuWarSOAkhMTCQqKopRo0axb98+goKC+OSTTwCYNm0a169f55dffkEsFrcrp0X2Pefl5SGVStHQ0KBbt25cv36dqKgodHR0WLx4sUpv/gNka1tcXMz69esJCQlhyJAhAH9ZS5lNpYwBBdWNnYq/jewDkEXnjYyMMDU1JTo6Gl9fXwIDA3n06BHV1dVMnDhRVVP3hLhz5w67du2iW7duaGhooKamxpEjR+jatSudOnXC0NCQuro6zpw5w+DBg+nUqZMq/VJFm9A6Ml9YWEhBQQFOTk6MGTOGAwcOsHv3bnlapop/hszo2LJli3xsSXJyMt7e3vTr14+ysjJSUlI4c+YM06dPb1ezAPX19UlISGDjxo2MHDmSvn37UlVVxb59+7h8+TJvvvkmwcHBihaz3ZGTk8OyZctYu3Yt1dXVXLp0iZs3b3LlyhW5sS2r/VY2o/r/QpZx88033/DgwQP27duHVColMDCQ7t27c/z4cRISEhg0aJD8eRV/D5FIRFJSEuvWrePy5cvcunULABcXlz/YroA8oKCM66ty7FT8bWQ1dcuXL+fy5ctoaWlhbW1Nbm6u/CPaunUrU6dOxd3dXdHiKg0lJSU4OzsjCAJlZWVYWlpy8eJFmpqasLCwQF9fn8bGRs6ePUtwcHC7idarUCwypy4xMZEFCxbg4ODAokWLaGxsxN/fnzFjxrB9+3Z27NjBxIkTFS3uc83u3bs5fPgw27Zto6SkhOPHj1NcXEzP/6+9O4+rOW8bOP45p5Vo37SoJJJKqUSbNrIryz2M15jVPZYZjHEPYx3LMPaxL48bEzPMGJkxGO6JZImIqFRGWROKZEQ5bc8f99N5zL3MDIMj53r/M6+cU+c6v/kt3+u7XN+AACIiIvD396dfv344ODhoOtRnqm6T9aKiIoqLizE1NSU2NpaTJ0+ydOlS+vTpQ2hoKDExMfTo0UPrKiy+KG7evEloaChFRUUsWbKEpUuXYmVlxdKlSyksLCQyMlJrOx9TU1NZvHgxa9eupbi4mLS0NC5duoRKpSIkJARvb2+8vb21Mun9szIzM5kyZQpz5syhT58+3L17l6ysLKqrq/+tEu7LfGwlsRN/WG5uLvPnzyckJISHDx+yefNmWrZsiY+PD9euXePMmTO8+eabhIaGajrUl4qVlRVGRkYsWLCA06dP4+HhQfPmzUlISCA1NZWTJ0+ybt063n33XVq3bq3pcMVLrm6dl0Kh4OzZs8yfP5/ly5dz584d8vLyOHDgAKWlpYSEhDBgwADc3d21ahTpaXg0Gbl+/Tq3bt1i6NChfPPNN2RlZbFx40ZWrVql3jO0SZMmGBoaajjqZ+fKlSuUl5djbGzM3r17mTBhAvv27WP//v24uLgwePBgTp06xezZsxkwYACNGjXSygqLLwobGxusrKzYvn070dHRBAQEcOnSJZycnOjcubNWb679888/06lTJwoKCli3bh1z5syhtLSUL7/8Ul1kRgpLPZnc3Fxu3rzJq6++irGxMS4uLuzYsYOMjAwaN26snsL+spPETvymugbG3bt3ycvLw8PDg7/85S+4urpSXV3Nhg0b8Pf3JzY2lpiYGOkhfYrqjmNBQQEmJiYYGxtz+fJl0tPTCQ0NJTw8HBMTE3R0dBgwYADBwcFy7MUzVVhYyOrVq2nXrh06Ojpcv36diIgIioqKWLRoEZs2baJZs2bMnTuXX375hdDQUEnqHtOja0G2bNlCVlYWnTt3pra2lvj4eMaPH4+NjQ23bt3i+vXr9OzZk4YNG2o46mcrMzOTfv36ERQUxA8//MDo0aMZNmwYWVlZHD16lODgYHr16kVaWhr29vYv/chlfaBQKDh16hQrV67EwsKChQsX8s477+Dv7691z6lHv6+LiwsWFhasX7+eN954A19fX65evYqlpSVhYWFSWOox/Ot5dOvWLVJSUvD19cXIyIjGjRujUqkoKiqipKSEoKAgraj7IImd+E0KhYLExEQ++OADzpw5w/nz54mNjcXIyAhnZ2fKysr4+9//TkxMDA0aNHhp5yw/b49Wv1y8eDEeHh54eXnRsGFDcnJyOH36NG5ubgQEBNCmTRt141mOvXiWGjZsiLm5OdXV1VRXV+Ps7IyVlRXff/893bt3p1WrVly+fBlHR0fat2+Pk5OTpkOud+qu4UOHDnHkyBHeeOMNLC0tuXfvHrt27aJ58+YcPnyYjIwMZs6cqRW9+02bNsXT05O3334bDw8PXn31VXR0dAgKCiI+Pp6CggKCg4Pp0aMHDg4OWpc4vEgerYLr7e2NSqXizJkzDBkyRF3ERhv+39y+fZvz58+rq7Y+SkdHh0OHDpGUlISxsTErV65k6NCh+Pj4aCja+qfuGj9z5gxZWVno6Ojg4eHBiRMn+Mc//gH8s8DfF198wcCBA9m3bx9hYWEvfbVgkMRO/I4rV66QkJDAG2+8QUxMDAkJCeTn5xMeHk6DBg1o1qwZnTp1wsbGRitu1s+LQqHg8OHDLFq0iClTpuDu7s7Dhw+xtbXFzc2NlJQUcnNz8fPzQ1dXV469eOaqqqrQ1dXFysqKkSNHkpiYSGhoKA0bNiQxMZE1a9bQtGlTZs2axfvvv09AQIA0sJ/Q1atX1TMj4uLiqKysxNjYmEuXLnHkyBFSU1P5+OOPcXZ21nSoz03d9jnz5s2jZ8+emJiYqEc2q6qqCAgIUL9Xzrlnr+7aPnXqFPn5+eTm5uLq6vqrY6+rq0u7du0IDw/Xqtk8KpWK9evXc/DgQSwtLf/j1FNLS0sKCwv56aefGDFihFRufUx1baTJkydz/vx5Tp8+TU1NDUOHDuXKlStkZ2eTkpLCxIkTMTIyIjk5mdjY2Jd6ynod2e5A/FclJSWEhIQwePBgxo8fD/yzB2TUqFF4eHjw2WefaTjCl9u8efOwsrKiU6dOJCcn89NPP1FSUsL69eu5fPkyjRo1ws3NTdNhCi1Q1yCr64Fu0KAB7777Lg0bNmT27Nk0atSImTNncv/+faKiooiOjtZ0yPXK3bt3KS0txcnJiaNHj+Lg4EBKSgozZ85k1apVBAcHq99bVlaGQqHAyMhIgxFrzoEDB/j444/58MMPsbGxYdKkSUyfPp2OHTtqOjStUzejZNCgQUyfPp2JEycyYMAA4N+nyWmbS5cusXXrVsrKyujfvz+enp7Av5fdv3v3LiYmJlp/vB5XXl4eixYtYvz48Tg6OhIfH09mZiZhYWH07NkTgIqKCg4fPsySJUuYO3cu7u7uGo76+ZARO/FfNWjQAE9PTxYsWEDPnj0xNjbGzMyMdu3a8fe//53AwEDMzc3lZvSU1N3Yr127pu6dT0lJ4ZtvvsHNzY1u3bpx//59rKys8PPzw8LCQtMhCy2hUChITk5m8uTJ+Pn50aRJE7p37863337L4cOHCQoKonPnzoSFheHm5iaNlMdQXV3N6dOnSUxMZPfu3ezfv5/Y2Fj8/PwwMzNj+vTptGjRAicnJ2prazEwMFDvaamNnJ2dcXd3Z/To0VhZWfH+++8TGBgo59xzdvXqVT777DPWrFlDUVERhYWFDB8+HCMjI61eklE3FTUvL4+kpCROnTpFUVERFhYW2NnZoVAoqKmpAf55XzUwMNDq4/VHFRUVkZWVhb29PXfu3GHlypVkZGTQtWtXzM3NcXFxobS0lEOHDlFSUoK3tzcKhYKcnBwGDhyoNUkdSGInfoezszMtW7Zk6NChdO3alcaNG2Nubk6/fv3+49xx8WTqGiXJycl89tlnhISE4OnpiYeHB7GxsXTs2JFffvmFdevW0b17d60tFS2er7p9f7KyspgxYwaffPIJvr6+6mmZPXr0YOPGjRw+fJiYmBh1wiH3hT9OqVSir6/PF198QWpqKm+//Ta+vr4AeHl5YWxszJgxY/D19aVp06YajvbF0LRpU7y9vXFwcCAwMBCQc+55u3nzJsXFxTx8+JD4+HjmzZuHvb09u3btoqysTGuLJtXt6zllyhSmTJmCl5cXt2/fJjs7G2tra/U2BnXnq5y3f8x3331HixYtMDAwwMTEBCMjIwoLCyksLFTv3VuX3LVr1w5LS0uUSiUtW7bUuj2VJbETv8vFxYXmzZszaNAgevfuTePGjdHT09N0WC8VhULBkSNHmDt3LpMnT1avVbC1tcXExEQ9WjJu3LhfrSUR4lm4evUqDRs2RFdXF4Dz58+jq6tLv379UKlUKJVK9Qavffr0wdnZWatLmD+JR0eY6nruTU1NuXXrFlVVVbi4uADQrFkz3N3dcXFxwczMTJMhv1CcnJzUo5jSOH726o5zfn4+5ubm6Ojo8PXXX5OUlMSyZcto2rQpaWlpLFiwgM6dO2NjY6PpkDVm//796OnpqdfJmpmZsXXrVs6dO4e1tbXcK5+Al5cX1dXVzJw5E4COHTtiampKVlYWFy9exMHBAWtra7y8vLSioNRvkcRO/CF1I3dlZWU4OjpqOpyXSm1tLdXV1axevZpOnToRGhrKt99+y7Rp00hOTiYqKor09HR69uwpewSKZ06lUrF161YMDQ3VjbOsrCzWr19Pp06dMDMzQ6lUkpaWxjI0ZxoAACAASURBVLZt2wgICNDa3vkn9Wgy8s0335CamsqgQYNo3rw5P//8M1lZWRgbG5Obm8vu3bt55ZVXtK7X+Y+SpO75UCgUHDx4kPHjxxMYGIiNjQ23b99GV1eX3Nxc7ty5w6JFi/jggw9o3769psPVqIKCAs6ePUtQUBB6enrY2tpy8eJFHj58SGhoKObm5poOsV74184vpVJJZmYmOTk56OjoEBYWRoMGDUhNTeXixYv4+/tLMTkksROPwcXFBUdHR+khfcrqblh37txh3bp1JCYmYmtry8CBA0lMTMTLy4vw8HBJqMUzl5uby9SpU/nb3/6Gnp4egwYNom/fvri7u1NYWMiePXuwsrLi8uXLzJw5k9jYWFxdXTUddr2kUCjYtGkT27Zt4/3331fvVdmyZUvy8vLYvXs3P/zwA2PHjpWp10Lj0tPTmTFjBjNmzMDLy0s9za1hw4ZcvnyZyspK+vXrR8eOHbWqjVD3XS9cuEBJSQn379/Hw8ODr7/+mmvXrmFgYMCNGzfU13mrVq00HXK98GiRmby8PG7cuIGdnR2BgYGcO3eOtLQ0DAwMCAkJwcjIiLZt20p19v8jVTGF0IBH92ApKCjA3t4ea2trbt26hYmJCU5OTly5coX333+fhQsXSuNZPHMPHjxg1KhR9OrVi8DAQGpqavjkk08oLS1l48aN3Lt3j/Xr13P06FGsra3p378/ERERWtWI+7MuXLhAs2bNgH9WzRszZgyff/45lpaW/Pjjjxw5coTY2FjCwsIoKChAV1dXNiwWL4Svv/6agoICPvzwQ6qrq6mpqfnVkgxtvg/s37+f5cuXY2FhQXl5OT169KBbt27MmDGDiooKdWGZyMhITYdaLxQXF3Py5Em6dOnCkSNHmDFjBjo6OrRr144BAwbg6urKqlWrOHfuHHFxcXJc/4UkdkJoyMGDB5kzZw79+/dn0aJFTJgwgVdeeQWAI0eOMHPmTD788EMpHS+ei5KSEiZMmICLiwupqanMmzePpk2bMmbMGIqLi4mPj0dfX5979+6pKzNqc2Pucd2+fZv4+HjefPNNTE1NqaioYNq0ady5cwczMzMMDQ0xMzMjJyeHRYsWacV+S+LFd+DAASoqKqipqeHHH3/kk08+UVdkTkpKIi8vjyFDhmjtveDy5cuMHDmShQsXYmVlxfnz55k9ezbDhg0jPDyc6upqSktLsba21tpj9Lg2bNhAamoqHTt2JCkpiYkTJ6Kjo8OqVavQ09PjlVdewdXVlWXLltGlSxetqnj5Ryh//y1CiKfh+vXr5ObmUlNTw9WrV1mxYgUrV66kVatWODs7ExUVBfyzgV1QUMCECROIjo5G+l7Es3T9+nXu3buHubk5Pj4+bNq0CX9/f1xdXdHT02P+/PnY2toSGxuLSqWicePGUv3yCRgbGzN06FCuXr3Kp59+iqGhId27d8ff359hw4YxdepU3Nzc1FOzhdC0vLw8li9fjqOjI82aNaOyspKkpCQyMjI4e/YsS5YsoXnz5oD23gtKS0tp3Lgxrq6uGBsb4+3tTVRUFBkZGejo6KCvr68u5qGtx+hxvfHGG/j5+ZGSkkJNTQ22trbY29vz1ltvUVVVxRdffEFeXh6jR4+WpO4/kDV2QjwHly5d4vXXX6dXr15YWlpSU1PD3bt3efjwIStXrmTBggXqUtEGBgaEhobi7OwMyMNAPDtVVVV89dVXmJqaYmVlRXl5OS1btmTnzp0oFApat26Nnp4eERERnDhxAjs7O6no9pjqeul1dHTQ0dHhzJkznDhxgkuXLtG3b1/8/PwwMTHhu+++Y/369UycOFGrKwqKF0NWVhYrV66kRYsW9OnTR128Jy0tjZ07d3Ls2DHeeecdoqKitGok6l+/q4GBAcnJyejo6ODk5IS+vj55eXlcvnyZ8PBwrTkuf1ZZWRnl5eUYGhpy7tw5goKCuHv3Lrm5uRgYGODo6Ii1tTVNmzbl1KlTspfvb5DETojn4MCBA+r1Mrt378bb25tp06aRlJTE7t27MTc3JyMjg5kzZxIaGoqDg4OmQxZaQKlU0qZNG5RKJWPGjGHQoEEEBQXh4ODA4sWLMTQ0VCd3Xbt2laTuMT3aCCwtLaVBgwY0b94cExMTDh8+THZ2Nh06dCA/P59vv/2W8ePH07JlSw1HLQTcu3ePPXv2UFxcjLe3N+bm5rRo0QJ/f3969uxJdHQ0np6eWpnUpaenk5WVRWZmJr6+vhQXF3PmzBmOHTtGZWUlK1asYMiQIbLv5GM4c+YMH330EQqFgqlTp9KpUyfCw8O5desWp0+fpqamBnt7e2xtbQkODpbOr98giZ0Qz0FlZSU7duzg+++/p2vXrnh5eeHq6kpKSgo3btwgLy+PFStWMHbsWIKDgzUdrnjJqVQqysvL0dfXp6ioiNLSUrKzs/nhhx8ICQnBw8MDZ2dnZs6ciaGhIW3atNGaxtvT8miDd+PGjcybN4/s7GwqKiqIjIzEyMiIEydOkJ6eTq9evQgNDZVCKUJj6s7Xq1evUlFRgYODA2FhYSQlJXHr1i3s7e0xNTXFwMAAAwMDGjZsCGjXjBKFQkFSUhKzZs1CT0+PhIQEjh49ytixYzEwMODChQtcvXqVwYMHy9ZEj8ne3p5Dhw6xfv16pkyZQmBgIAD+/v5cunSJo0ePAuDm5oaenp5WnXePSxI7IZ6TnTt3YmlpSatWrbCwsMDd3Z2AgADS0tJo1KgRffv2JTQ0VKt6QMXzV1VVRUpKChcvXuTMmTNs2rSJzp07ExAQwLlz50hISCA0NJRWrVrh6uqKjY2NjCA/gbprODExkT179vD222+jUqk4ePAglZWVxMTEoKenR3Z2Nm3btsXY2FjDEQttplAo2L9/P3PmzCE7O5tdu3bRvn17goKC+P7777l8+TLOzs6YmJhoOtTnKj8/n4yMDOzt7SkrK2P69OlMmTKFvn37MmjQINauXcvZs2d5++23iYiIICgoSKpYP6bq6mqUSiWlpaUYGRmxceNGIiMj1fv9tWnThps3b+Lr64u1tbW0j36HJHZCPCOPJmgGBgbExMRgYWHB3r17qaiowM7ODicnJyIjI/Hz81PvUyc3LfEsKZVKrl+/zpIlS9i/fz9vvvkmvr6+NGrUiBYtWnD+/Hk2bdpEeHg4Hh4eODg4SGfDYygqKsLIyAiAkydPsmjRIoYNG6aeYq1QKDhw4AD37t2jZ8+eBAUFSVInNO7kyZMsWbKElStXkpeXR15eHr169cLOzg5PT0+2b99OSEiIVm2uffPmTV599VW6du2Kk5MTBgYGfPnll/Tp00ed4Pr7+3PgwAGioqLQ1dVFR0dHw1HXH3XPlZs3b/LgwQPat29PTEwMv/zyC1OnTiUuLo6cnBw2bdrE8OHDZfrlHySJnRDPQN0NKyUlhe3bt3PkyBFcXFwICAigsrKSY8eOcffuXRwdHdVTWoR41mpqalAoFDRs2JArV65QU1NDs2bNMDY2xtTUFBMTE5o2bcq1a9dwcHBQP0glqftjrly5QkJCAq1bt0ZfX5/Lly9z/PhxcnNziYqKwszMDBsbGx4+fEhaWhpBQUE0atRI02ELwfHjxwkMDOTGjRts375dXQ03LS2N1q1bExMTo67uqC1+/vlnysvLsbOzY9asWfTu3ZuMjAyOHTtGQEAAhoaGZGZmcvToUbp166auFix+X10bKTk5mQkTJpCcnMzmzZuxs7NjwIAB3L59m4ULF3L48GF69Ogha48fgyR2QjwDdUnd9OnT6dSpE6mpqeTk5KBSqejRowe3bt0iLS2NDh060LhxY02HK7RAbW0tSqWS48ePM2XKFGbPno2lpSX79u3j/v37tGrVisLCQnJzcxk4cKB6BFn8cdXV1fj6+nL58mVOnDhBZGQkbm5u5OXlcfLkSfz9/TE1NcXBwYGoqCi59sULIzMzk6+++oqcnBzmzJmDo6MjBw8eZNmyZQQHB2NmZqbpEJ87CwsLVqxYwc6dO3nrrbdwd3fH3t6e3NxclixZQnV1NStXrmTUqFG0aNFC0+HWKwqFgtzcXKZNm8ann37KsGHDKCoqIikpiTZt2tC9e3fatGlDXFwcAQEBMmvkMUhiJ8QzUFtby+LFi4mLiyM2NpaYmBgKCgpISkqiR48eeHt74+PjI1UGxXOjUCjIyMhg3bp1DBkyBFdXV5o1a0ZNTQ0pKSkcOXKERYsW0adPH1xcXDQdbr1S1+ho0KABtbW1fP3115w+fRodHR2CgoIwNTUlIyODgwcPqqdeGhgYaDpsoaXqztf8/HzKysowMTHBzs6Or7/+mpYtW+Lv78/Zs2eZO3cuw4cPx8vLS9Mha8zRo0dRKpWYmppiZ2eHq6sr7du3R0dHByMjI3r37k1wcLAkHn/AtWvXKC4uVk/nvX79OsXFxbz66qsAtGvXjt27d5OVlUVUVBQ2NjaYmpoCMmvkcUhiJ8RTlp6eTklJCRcvXqRJkya4u7ujo6ND27Zt2bBhA76+vlhYWEhvvXiuVCoVP/30E/Hx8XTo0EG9saubmxuWlpaYmZkRFxenrkYm/phHG3SVlZUYGBjg7u5OUVERJ06cACA0NBQjIyMuXryIr6+veg2eEJqgUCjYt28fkydP5scff6SwsBBPT0969+7Nd999R2pqKqdOneKvf/0rERERWpu0KJVKunTpQnR0NBs3buTatWs4OTlhYWFBmzZt1GuQQRKPP2L16tUsWbKEwMBALCwsuHbtGsuWLSMyMlI9IqxUKqmsrKRdu3Yajrb+ksROiKeg7sF3/vx5Jk+eTFxcHEVFRWzYsEE95So/P5+9e/cSFxcnSZ14Lh5tkOno6ODh4YGOjg5bt27Fzc0Ne3t7AOzs7GjZsqVUv3wCdcd3y5YtrFy5klu3btGkSRM6dOjA1atXSU9P5+HDh0RGRtKhQwetqyooXhx194OqqipWrFjB1KlTiYqKIiUlhZycHHx8fBgwYADdu3cnNDQUd3d3rU3q6tSNxHt7e7Nz504uXLiAk5OTeiRJ/HHBwcFcuXKFjRs34uPjg5eXFxUVFUyYMAFHR0d+/vlnVqxYQVxcHM7OzpoOt96SxE6Ip0ChUHD48GHWr19P3759CQgIoG3btly8eJE1a9aQnp7Oli1bGDFiBG3atNF0uEIL1DXIjh8/zsGDB7l27Rr29vaEhIRQVlbGV199hb29vayle0KPNnj37t3L5s2biYmJITMzk8zMTJo2bUpYWBjnzp0jLy+Pdu3aSaEkoVEKhYITJ05w9OhR7ty5Q2xsLNbW1uoiKRkZGVhYWGBra4uBgQEKhUKrk7pHmZmZ0bp1a77//ntCQ0O1qjron1V3r1SpVERERKgrL/v4+NCtWzeMjIw4dOgQly5d4q233qJjx45a36HwZyhqa2trNR2EEC+D5ORk3n33XQYMGMCj/SXHjx/HwMAAPT09PDw85IYlnpsDBw6wcOFCevTowf79+3FxcWHcuHGYmpqybNkyEhMT2bBhAyYmJnJOPoZHr+Hi4mLOnj2LlZUVrVu3JiMjgx9//JHy8nL69euHq6srFRUVWll8QrxYUlNTmTJlCm5ubuTn59OvXz8GDRqEoaEhGRkZxMfHM2zYMNmH7TeUl5fToEEDTYdRb9TdK5OSkkhMTGTs2LGYmZkxffp0Tp8+zWeffaYuPFNdXY2Ojo60kf4kGbET4gnV3XwqKyuprq7G1dUVX19fZs+ejb29Pa1atQLA3t4eW1tbrKysAJmLL56PzMxMFixYwKpVq6ioqODo0aOYmJhw+PBh2rdvT2hoKMHBwdjY2Mg5+RgebXR88cUXTJo0icOHD1NQUECXLl2wsbHBzMyMs2fPkp+fT0hIiGxpIDQuNzeXtWvXMmvWLAYNGoSenh45OTlcv35dPQ07KCgIW1tbTYf6QtPV1ZX75WNQKBQcOnSIhQsX8s4779C8eXMAOnbsSF5eHqtWrSIgIABLS0uUSqX6d8STk8ROiCekUChITExk1apVfPXVV5iZmREWFoaPjw9jx47F0tKS1q1bazpMocX8/PwoLi7m888/Z8mSJTRq1IjNmzeTnZ1N586dZRTpCTy6/1JKSgqTJk2iefPmXLlyhZycHNq3b4+1tTVNmjQhLCxM1tMKjSsrKyMhIYEdO3bQrl07XFxccHNz45dffiEtLY0bN27g4+ODnp6eNKp/hxyfx7d+/Xr69+9PWFgYlZWV6q13wsLCuHTpEs7OzlIh/CmSxE6Ix1TXsLtw4QKffPIJb7/9Ng0aNGDv3r2UlZXRvXt3XF1dGTduHP3798fIyEgeBuK5a9SoETY2NuzcuZNmzZoRHR1NUVER+vr6vP7661q32fCflZOTQ+PGjdHT0yMzM5MPP/yQ8PBwYmJiaNKkCQ0aNCA9PZ2TJ08SGhqKpaWlrKkTGvPoyLK+vj6Ojo6oVCqOHTuGnZ0dDg4O6uQuMDAQKysreU6Jp666upr4+HgsLS1p06YNtbW16Orqkp6eTnl5ObGxsZLUPWWS2AnxmOo2H1+0aBH9+/ena9eu+Pv7o6enx9KlSwkPD8ff35+BAwfKw1Jo3I0bN1i/fj337t1jzZo1vP7667Rt21bTYdUrDx48YMGCBSQkJNClSxfMzMy4du0au3btokOHDtjb29OkSRMA8vPz8fX1lXU4QqPqnlMJCQmUlpbi5uZGmzZtKCgo4MCBA1hZWeHo6Iinp6d6mYAQT5tSqURPT489e/ZgbW2Nk5MTp06d4uOPPyY8PBwbGxtNh/jSkcROiCfwyy+/sGDBAnR1denatSs1NTW0aNGCM2fO4OTkhKOjI4aGhigUClkILDTKwcEBMzMzcnNzeeuttwgNDdV0SPVKbW0t+vr6eHp6kp6ezr59++jUqRORkZGUlJTw5Zdf4unpSZMmTXBwcCA0NFS2NBAad+LECWbNmoWxsTHnzp3jxIkThIaG4unpSV5eHklJSYSFhaGvry/PJ/FM2dnZcf/+febOncv58+fZsmUL48aNkz1TnxGpiinEE8rOzuYvf/kL77//PnFxcRQWFvLee++xdu1a9ebPQrwoampqUCqV0tHwhBISEsjKyuKnn36iXbt2zJo1C6VSybJly/jHP/7B4sWL1dXdhNCEumu7urqaHTt20LRpU/z8/Dh+/Dg7d+4EYMyYMVRVVXHv3j1cXFw0HLHQJufOnUOlUmFgYECLFi3kWfSMSGInxJ+QkZHB4MGDadq0KZGRkbRp04aIiAhNhyXEv5GH6JNLSEhg3bp1LFy4kKysLFJSUlCpVMyfPx+ANWvWEBsbKxu8C41LTExk48aNlJWVER0dzbBhwwBIS0tj69atKJVKZsyYga6uroYjFUI8C3JlC/EneHt7s3nzZgYOHEhtbS0RERHU9ZVII1q8SOR8fDK1tbVkZmby2muv0aJFC5ydnXFzc2P8+PF88MEHLFq0iPfee0/TYQpBfn4+W7dupWfPnty6dYu9e/fi4OBAz5498ff3p6amBlNTU0nqhHiJydUtxJ/UqlUrNm7cSP/+/TE3N+f111/XdEhCiCf0ryObCoUCe3t7UlNT6dixI7a2tnh5eeHh4UFFRQV37tyRAgBC47Kzsxk1ahQjRowgNjaW0tJSbGxs2LRpE5WVlfTp04d27dppOkwhxDOm1HQAQrwMvLy82LZtGyEhIZoORQjxhB5N6i5evMi9e/dQqVSEhoaiq6vLjh07yM3NZd++fRQXFzNt2jRJ6sQLwdbWloYNGxIfHw+AqakpUVFR9O3bl/j4eG7evElNTY2GoxRCPGuyxk4IIYTWezSpi4+PZ8+ePbi5uWFgYMDYsWNJT09nz549ZGdno1QqmTp1qhRJEhpTd74+ePCA2tpajIyMuHv3Lu+++y4mJiasXr0a+GcF5/LycumAEEJLSGInhBBC/J/Nmzezd+9eli5dysSJE7lw4QJubm7MnDkTIyMjbt26hZ6enmxpIDSmLqlLTk5mw4YNqFQq3N3dGTNmDA8fPmTUqFEolUq++OILTYcqhHjOZCqmEEIIAdy/f5/8/HzmzJnDtm3buHfvHtOmTePy5ct8+OGHlJSUYGlpKUmd0CiFQsGJEyeYN28e77zzDpMnTyYnJ4c5c+Zgbm7OkiVLqKioIDMzU9OhCiGeMymeIoQQQgBGRkZ89NFHXLt2jYMHD7J+/XoAnJ2dMTAwQKVSaThCIf7pypUrxMbGEhwcDMDq1avp1asX27dvJy4ujk2bNqGnp6fhKIUQz5uM2AkhhBD/R19fH11dXQoLCzl27Ji6UMrf/vY3bG1tNR2e0FJ1q2bu3bun/u+OHTvUrzdu3Ji+fftiZGQEIEmdEFpKEjshhBDiEba2tvTq1YvFixezbNkyJk6ciLm5uabDElqqbk1dUlIS8+bNo6SkhNdeew0nJydGjBhBSUkJaWlp/Pjjj3KeCqHlpHiKEEII8S+qqqq4c+cOCoUCS0tLTYcjtNyhQ4eYP38+EyZMIDAwEIDr168za9Ys7ty5Q1VVFe+++y4REREajlQIoUmS2AkhhBBCvMA++eQTQkJCiI6ORqVSoa+vr36trKyMqqoqTE1Nf7VthxBC+0jxFCGEEEKIF1R1dTWXLl2iWbNmACiV/1xFc/r0aQwNDX+1n6IkdUJoN1ljJ4QQQgjxgtLR0aF///4kJydz9OhRdHV1OXXqFOPGjZNKrUKIX5EROyGEEEKIF1jHjh0pLi7mo48+IiQkhIyMDD7++GO8vb01HZoQ4gUia+yEEEIIIeqBc+fOoVKpMDAwoEWLFrKmTgjxK5LYCSGEEEIIIUQ9J2vshBBCCCGEEKKek8ROCCGEEEIIIeo5SeyEEEIIIYQQop6TxE4IIYQQQggh6jlJ7IQQQgghhBCinpPETgghhBBCCCHqOUnshBBCiN+RmppKWFiYpsMQQggh/itJ7IQQQtQ71dXVmg7huamqqtJ0CEIIIeoBSeyEEEK8MPLz83nttdfw9/ene/fu7Nu3D4Dx48czdepUhgwZgo+PD6mpqZw9e5bY2Fh8fX0ZOXIko0ePZtGiRb/7GYmJifTu3Zu2bdsSHR3NwYMHAdi2bRtdu3bF19eXqKgotmzZAsCDBw8YMmQIRUVF+Pr64uvry82bN6mpqWHNmjVER0cTGBjIqFGjKC0tVX/Od999R0REBIGBgSxfvpzIyEhSUlIAUKlUfPrpp4SEhBASEsKnn36KSqUC/n90cM2aNQQHB/Pxxx/To0cP9u/fr/7blZWVBAYGkp2d/XQOvBBCiHpPEjshhBAvhMrKSoYOHUpwcDApKSlMmjSJsWPHcuHCBQB27tzJ0KFDOXXqFN7e3rz33nvExcVx/PhxevToQWJi4u9+RkZGBuPGjeOjjz4iLS2NL7/8Ent7ewAsLCxYvXo1p06dYvbs2cyePZuzZ8/SsGFD/ud//gdra2vS09NJT0/HxsaGjRs3kpiYyKZNmzh06BAmJiZMnz4dgLy8PKZNm8a8efM4dOgQZWVl3Lx5Ux3HypUrOXPmDN9//z07duwgMzOTFStWqF+/desWd+/eJSkpiRkzZtC7d2927Nihfj05ORlra2s8PDyeyrEXQghR/0liJ4QQ4oVw5swZHjx4wF//+lf09fXp0KEDERER7Nq1C4CoqCj8/PxQKpXk5ORQVVXF4MGD0dPTo3Pnznh5ef3uZ3z77bf07duX4OBglEolNjY2uLq6AhAeHk7Tpk1RKBS0a9eO4OBg0tLS/uvf2rJlCx988AG2trbo6+vz3nvvsXfvXqqqqtizZw8RERH4+/ujr6/PyJEjUSgU6t/94YcfGDFiBBYWFpibmzNixIhfJW5KpZKRI0eir6+PoaEhvXr1Ijk5mbKyMgB27NhBr169nug4CyGEeDnpajoAIYQQAqCoqAhbW1uUyv/vc7Szs1OPdDVp0uRX77WxsflVsvTo6//N9evX6dix4398LTk5meXLl3Pp0iVqamqoqKigRYsW//VvFRYWMmLEiF/Fq1QquX37tvq71GnQoAGmpqa/it/Ozu5X37OoqEj9s5mZGQYGBuqfbWxsaNu2LXv37qVTp04cPHiQiRMn/u73FUIIoT0ksRNCCPFCsLa25saNG9TU1KiTpevXr+Ps7My1a9d+9V4rKytu3rxJbW2tOrm7fv06jo6Ov/kZTZo04cqVK//27yqVipEjRzJnzhyioqLQ09Nj+PDh1NbWAvwqgaxja2vLrFmz8PPz+4/f5eLFi+qfKyoqfrX+ztramsLCQtzc3NSxW1tbq1//T58XFxfH1q1bqa6uxsfHBxsbm9/8rkIIIbSLTMUUQgjxQvD29sbQ0JC1a9dSWVlJamoq+/fvp1u3bv/2Xh8fH3R0dNi0aRNVVVUkJiaSmZn5u5/Rr18/EhISOHr0KDU1Ndy8eZP8/HxUKhUqlQpzc3N0dXVJTk7myJEj6t+zsLCgtLSUe/fuqf9t4MCBfP755+qks6SkRL3OLyYmhv3793Pq1ClUKhVLly5VJ4kA3bt3Z+XKlZSUlFBSUsLy5cvp2bPnb8YeHR1NdnY28fHxxMbG/u53FUIIoV1kxE4IIcQLQV9fn1WrVjFt2jRWr16NjY0Nc+fOVa+B+9f3Ll26lEmTJrFw4UJCQ0MJDw9HX1//Nz/D29ub2bNnM2vWLAoKCrC0tGTKlCm4uroyadIkRo8ejUqlIiIigsjISPXvubq60r17d6Kjo6murmbXrl0MHjyY2tpa3nrrLYqKirCwsKBbt25ER0fj5ubG5MmTGTNmDOXl5QwePBhzc3N1fMOHD+f+/fvqdXJdunRh+PDhvxm7oaEhnTt3ZteuXXTq1OlxD68QQoiXnKL20S5EIYQQop7q378/AwYMoG/fvpoO5d/cv3+fgIAA9u7d+7vTRX/LsmXLuHTpEvPnz3+K0QkhhHgZyFRMIYQQ9dLx48cpLi6mqqqK7du3c+7cOUJDQzUdltr+/fspuLfKKQAAANpJREFULy/nwYMHzJkzhxYtWuDg4PDEf6+0tJRt27bxyiuvPMUohRBCvCxkKqYQQoh66eLFi4wePZry8nIcHBxYsmQJ1tbWrFq1itWrV//b+/38/Fi7du1zi2/fvn189NFH1NbW4unpycKFC/9jUZQ/4ptvvmHWrFn06tWLgICApxypEEKIl4FMxRRCCCGEEEKIek6mYgohhBBCCCFEPSeJnRBCCCGEEELUc5LYCSGEEEIIIUQ9J4mdEEIIIYQQQtRzktgJIYQQQgghRD0niZ0QQgghhBBC1HP/C9TE1i0OGtcOAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We study the correlation of the different parameters with one another.We find how the post money valuation has a good positive correlation with total funding raised,employee count,age of company and investors."
      ],
      "metadata": {
        "id": "prN2MO77pzir"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets see how the funding received by the company changes with the  age of the company."
      ],
      "metadata": {
        "id": "SvQZETOrNa6C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=\"mean_age\", y=\"mean_funding\", data=category_analysis)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "ykNsZmKYNl-Y",
        "outputId": "5b461fd4-2277-4069-d17a-46fd41117ce9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1f2797e90>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "lets check how the newer companies are performing in accordance to the older companies-\n",
        "1.take two constants 90th percentile age and 10th percentile age\n",
        "2.check the total employee count and total funding received in both the groups\n",
        "3.compare and analyze"
      ],
      "metadata": {
        "id": "xdjyzJ7h5-Sc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "PufVR16M6ech",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "a47b4ef8-c2bb-4822-eac6-600a03b9478a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     roles  total_funding_usd  employee_count      rank   city  \\\n",
              "0  company         5250000.00           10000 333629.00  Dubai   \n",
              "1  company          200000.00              39 143263.00  Dubai   \n",
              "2  company          200000.00              39 270672.00  Dubai   \n",
              "3  company               0.00              39 122572.00  Dubai   \n",
              "4  company               0.00               0 339418.00  Dubai   \n",
              "\n",
              "  investment_type announced_on  raised_amount_usd  post_money_valuation_usd  \\\n",
              "0  series_unknown   2006-01-01         5250000.00                      0.00   \n",
              "1            seed   2019-01-01                NaN                      0.00   \n",
              "2        pre_seed   2017-08-01          200000.00                      0.00   \n",
              "3  series_unknown   2009-02-18                NaN                      0.00   \n",
              "4  series_unknown   2009-02-18                NaN                      0.00   \n",
              "\n",
              "   investor_count                              org_uuid  \\\n",
              "0            1.00  e542dd76-1ee1-5227-2f27-4fb9ecd65fc2   \n",
              "1            1.00  d0fd55c6-31bf-3afe-4346-a4d617c874df   \n",
              "2             NaN  d0fd55c6-31bf-3afe-4346-a4d617c874df   \n",
              "3            1.00  2f0a2474-bd62-744e-9868-74ced3e3f1a7   \n",
              "4            1.00  8d302824-3ea2-a8d4-c14f-e723bcb7196d   \n",
              "\n",
              "                     org_name lead_investor_uuids           round  Year  \\\n",
              "0                     Maktoob                 NaN   Venture Round  2006   \n",
              "1  Qareeb Technologies FZ-LLC                 NaN      Seed Round  2019   \n",
              "2  Qareeb Technologies FZ-LLC                 NaN  Pre Seed Round  2017   \n",
              "3     Conservus International                 NaN   Venture Round  2009   \n",
              "4          Pulse Technologies                 NaN   Venture Round  2009   \n",
              "\n",
              "                category  age  \n",
              "0                 gaming   22  \n",
              "1  commerce and shopping    5  \n",
              "2  commerce and shopping    5  \n",
              "3            advertising   18  \n",
              "4               hardware   14  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-973d7a52-3f9d-4db6-8f97-fa82fd6a9feb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>roles</th>\n",
              "      <th>total_funding_usd</th>\n",
              "      <th>employee_count</th>\n",
              "      <th>rank</th>\n",
              "      <th>city</th>\n",
              "      <th>investment_type</th>\n",
              "      <th>announced_on</th>\n",
              "      <th>raised_amount_usd</th>\n",
              "      <th>post_money_valuation_usd</th>\n",
              "      <th>investor_count</th>\n",
              "      <th>org_uuid</th>\n",
              "      <th>org_name</th>\n",
              "      <th>lead_investor_uuids</th>\n",
              "      <th>round</th>\n",
              "      <th>Year</th>\n",
              "      <th>category</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>company</td>\n",
              "      <td>5250000.00</td>\n",
              "      <td>10000</td>\n",
              "      <td>333629.00</td>\n",
              "      <td>Dubai</td>\n",
              "      <td>series_unknown</td>\n",
              "      <td>2006-01-01</td>\n",
              "      <td>5250000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>e542dd76-1ee1-5227-2f27-4fb9ecd65fc2</td>\n",
              "      <td>Maktoob</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Venture Round</td>\n",
              "      <td>2006</td>\n",
              "      <td>gaming</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>company</td>\n",
              "      <td>200000.00</td>\n",
              "      <td>39</td>\n",
              "      <td>143263.00</td>\n",
              "      <td>Dubai</td>\n",
              "      <td>seed</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>d0fd55c6-31bf-3afe-4346-a4d617c874df</td>\n",
              "      <td>Qareeb Technologies FZ-LLC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Seed Round</td>\n",
              "      <td>2019</td>\n",
              "      <td>commerce and shopping</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>company</td>\n",
              "      <td>200000.00</td>\n",
              "      <td>39</td>\n",
              "      <td>270672.00</td>\n",
              "      <td>Dubai</td>\n",
              "      <td>pre_seed</td>\n",
              "      <td>2017-08-01</td>\n",
              "      <td>200000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>d0fd55c6-31bf-3afe-4346-a4d617c874df</td>\n",
              "      <td>Qareeb Technologies FZ-LLC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pre Seed Round</td>\n",
              "      <td>2017</td>\n",
              "      <td>commerce and shopping</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>company</td>\n",
              "      <td>0.00</td>\n",
              "      <td>39</td>\n",
              "      <td>122572.00</td>\n",
              "      <td>Dubai</td>\n",
              "      <td>series_unknown</td>\n",
              "      <td>2009-02-18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2f0a2474-bd62-744e-9868-74ced3e3f1a7</td>\n",
              "      <td>Conservus International</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Venture Round</td>\n",
              "      <td>2009</td>\n",
              "      <td>advertising</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>company</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>339418.00</td>\n",
              "      <td>Dubai</td>\n",
              "      <td>series_unknown</td>\n",
              "      <td>2009-02-18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>8d302824-3ea2-a8d4-c14f-e723bcb7196d</td>\n",
              "      <td>Pulse Technologies</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Venture Round</td>\n",
              "      <td>2009</td>\n",
              "      <td>hardware</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-973d7a52-3f9d-4db6-8f97-fa82fd6a9feb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-973d7a52-3f9d-4db6-8f97-fa82fd6a9feb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-973d7a52-3f9d-4db6-8f97-fa82fd6a9feb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "HjK4OiFTWyle"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lower_age_quantile,upper_age_quantile=df['age'].quantile(0.25),df['age'].quantile(0.75)\n"
      ],
      "metadata": {
        "id": "mKGkRQ47WAmc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['age']<=lower_age_quantile]['total_funding_usd'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1xCYdOsX_T8",
        "outputId": "b3568821-720a-4d19-bf48-1004c2582ab1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13517517.005988024"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['age']>=upper_age_quantile]['total_funding_usd'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-xQoqWLYSiA",
        "outputId": "2d01a054-579a-4edb-c7c5-311cfc133572"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "284136566.77393615"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ipo1=pd.merge(df_organization[org_columns],df_ipo[['org_name','org_uuid','city','went_public_on','share_price_usd','valuation_price_usd','money_raised_usd','stock_symbol']], how='inner',left_on='uuid', right_on='org_uuid')"
      ],
      "metadata": {
        "id": "S2-zfxUM2_cr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ipo1['category_groups_list'] = df_ipo1['category_groups_list'].fillna('other')\n",
        "df_ipo1['category']=df_ipo1['category_groups_list'].apply(compute_category)"
      ],
      "metadata": {
        "id": "om5M0iL63SGJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ipo1.fillna(0,inplace=True)"
      ],
      "metadata": {
        "id": "xsV96DJZ3BBY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df_ipo1['share_price_usd']) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "UKIRmbJ84xlx",
        "outputId": "14dc6379-ebc2-4312-d03b-160a8dfd3ee9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1f25cf050>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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v3+Ltt/eXehQAAOAs5jY/AACABDEFAACQIKYAAAASxBQAAECCmAIAAEgQUwAAAAliCgAAIEFMAQAAJIgpAACABDEFAACQIKYAAAASxBQAAECCmAIAAEgQUwAAAAliCgAAIEFMAQAAJIgpAACABDEFAACQIKYAAAASxBQAAECCmAIAAEgQUwAAAAliCgAAIEFMAQAAJIgpAACABDEFAACQIKYAAAASxBQAAECCmAIAAEgQUwAAAAliCgAAIEFMAQAAJIgpAACABDEFAACQIKYAAAASxBQAAECCmAIAAEgQUwAAAAliCgAAIEFMAQAAJIgpAACABDEFAACQIKYAAAASxBQAAECCmAIAAEgQUwAAAAliCgAAIEFMAQAAJIgpAACABDEFAACQIKYAAAASqk71gkKh4qOYo8M661x0TtYLHWG90F7WCh1hvdAR1kvncKr/DhXFYrH4Ec0CAADQZbjNDwAAIEFMAQAAJIgpAACABDEFAACQIKYAAAASxBQAAECCmAIAAEgQUwAAAAliCgAAIKGq1AN0xJYtW6KhoSH27dsXvXr1itmzZ8cll1xS6rHopMaMGRPV1dXRrVu3iIiYMmVK1NfXl3gqOovZs2fHCy+8ENu2bYulS5fGkCFDIsJ5hradaL04z/BBe/fujWnTpsXWrVujuro6Bg0aFDNmzIg+ffrEH//4x2hsbIz33nsvLrjggvj+978fffv2LfXIlNDJ1svQoUNjyJAhUSj889rHww8/HEOHDi3xxHxIsYxMnjy5uGTJkmKxWCwuWbKkOHny5BJPRGd29dVXF//yl7+Uegw6qTVr1hSbmpo+tE6cZ2jLidaL8wwftHfv3uJrr73W+njWrFnFb3/728Xm5ubiNddcU1yzZk2xWCwW582bV2xoaCjVmHQSJ1ovxWKxOGTIkOKBAwdKNRrtVDa3+e3evTs2bNgQ48ePj4iI8ePHx4YNG2LPnj0lngwoR5dffnnU1NQc95zzDCfS1nqBtvTq1StGjhzZ+viyyy6LpqameOONN6Jbt25x+eWXR0TEpEmT4vnnny/VmHQSJ1ovlI+yuc1v+/btMWDAgKisrIyIiMrKyujfv39s3749+vTpU+Lp6KymTJkSxWIxRowYEffcc0/07Nmz1CPRiTnPkOE8w4m0tLTEz3/+8xgzZkxs3749Bg4c2LqtT58+0dLS0npLMfzrennf5MmTo7m5Oa666qq4++67o7q6uoQT0payuTIFHbVw4cJ49tlnY9GiRVEsFmPGjBmlHgnoYpxnOJkHH3wwevToEbfddlupR6EMfHC9vPTSS7F48eJYuHBhvPnmmzFv3rwST0hbyiamampqYseOHdHc3BwREc3NzbFz5063XXBC76+N6urquPXWW2PdunUlnojOznmGjnKe4URmz54df/vb3+LRRx+NQqEQNTU1x92+tWfPnigUCq5KEREfXi8R///8cu6558bNN9/s/NJJlU1M9e3bN4YNGxbLli2LiIhly5bFsGHD3HpDmw4ePBj79++PiIhisRi/+tWvYtiwYSWeis7OeYaOcJ7hRH74wx/GG2+8EfPmzWu9LetTn/pUHD58ONauXRsREU899VRce+21pRyTTqKt9fLOO+/E4cOHIyLi2LFj8cILLzi/dFIVxWKxWOoh2mvTpk3R0NAQ7777bvTs2TNmz54dH//4x0s9Fp3QW2+9FXfffXc0NzdHS0tLDB48OO67777o379/qUejk3jooYdixYoVsWvXrujdu3f06tUrli9f7jxDm9paL/Pnz3ee4UM2btwY48ePj0suuSS6d+8eEREXXnhhzJs3L9atWxf333//cV+N3q9fvxJPTCmdaL185StficbGxqioqIhjx45FbW1tTJ8+PT72sY+VeGI+qKxiCgAAoLMom9v8AAAAOhMxBQAAkCCmAAAAEsQUAABAgpgCAABIEFMAZ6m5c+fGlClTSj1GhzU2Nsa8efNKPcZJ/f3vf4+hQ4fGsWPHSj0KAP+Lqko9AAB0xIwZM0o9AgBEhCtTAJwBH9UVmObm5o/k9wBAe4gpgLPAggULor6+Pmpra+MLX/hCrFq1KiIijh49GtOmTYva2tq4/vrr409/+tNx77nmmmuitrY2xo0bF7/+9a9bty1evDgmTZoUM2fOjJEjR8bcuXPjyJEjMXv27Pjc5z4XV155ZTQ2Nsbhw4dPOtfq1avjqquuivnz58fIkSNjzJgx8eyzz7Zub2hoiPvvvz/uvPPOuOyyy2L16tXR0NAQc+bMaX3Niy++GBMnTozhw4fHNddcE6+88kpEROzfvz+mT58edXV1UV9fH3PmzDlljH3w1scP3q63ePHiGDt2bNTW1h43a3Nzc8yePTtGjhwZY8eOjZdffvmkvweArkFMAXRxmzdvjoULF8YzzzwTr7/+evzkJz+JCy64ICIifvOb38T1118fa9eujTFjxsSDDz7Y+r6LLrooFi5cGH/4wx/iG9/4RkydOjV27tzZun39+vVx0UUXxcqVK+NrX/taPPLII7Fly5ZYsmRJrFixInbu3Nmuv23atWtX7N27N1599dWYNWtWNDY2xubNm1u3L1u2LL761a/GunXrYsSIEce9d/369XHvvffGtGnTYu3atbFw4cLWf1tDQ0NUVVXFihUrYsmSJbFy5cr4xS9+kT6OBw8ejIceeih+/OMfx+uvvx5PPfVUDBs2LCIinn766fjtb38bS5YsiUWLFsXzzz+f/j0AlA8xBdDFVVZWxpEjR2LTpk1x9OjRuPDCC+Piiy+OiIgRI0bE6NGjo7KyMiZOnBh//vOfW9933XXXxYABA6JQKMS4ceNi0KBBsX79+tbt/fv3j8mTJ0dVVVV069Ytnn766Zg+fXr06tUrzj333Ljrrrti+fLl7ZrxW9/6VlRXV8cVV1wRo0ePjueee65129ixY2PEiBFRKBSiW7dux73vmWeeiS9+8YsxatSoKBQKMWDAgBg8eHDs2rUrXn755Zg+fXr06NEj+vbtG7fffnu75zmRQqEQGzdujMOHD0f//v3j0ksvjYiI5557Lr785S9HTU1N9OrVK+66667T+j0AlAdfQAHQxQ0aNCimT58ec+fOjTfffDPq6uqioaEhIiL69evX+rru3bvHe++9F8eOHYuqqqpYsmRJ/PSnP41t27ZFxD+vzOzdu7f19eeff37rz3v27IlDhw7FTTfd1PpcsViMlpaWU87Xs2fP6NGjR+vjgQMHHncFrKam5oTv3b59e4wePfpDzzc1NcWxY8eirq6u9bmWlpaT7utUevToEXPmzInHH388vvOd78Tw4cPj3nvvjcGDB8fOnTuP2/fAgQPTvweA8iGmAM4CEyZMiAkTJsSBAweisbExHnnkkdarU23Ztm1b3HffffHEE09EbW1t65Wrf1VRUdH6c+/evaN79+6xfPnyGDBgQIdme/fdd+PgwYOtQbV9+/bWKz6nUlNTE1u3bv3Q8+eff35UV1fHa6+9FlVV7f+oO+ecc477O69du3Ydt72+vj7q6+vj8OHD8eijj8Z3v/vdePLJJ+O8886L7du3t77uX38GoOtymx9AF7d58+ZYtWpVHDlyJKqrq6Nbt25RKJz89H/o0KGoqKiIPn36RETEokWLYuPGjSd8faFQiJtvvjlmzpwZu3fvjoiIHTt2xKuvvtquGd//Aou1a9fGSy+9FNdee2273velL30pFi9eHKtWrYqWlpbYsWNHbNq0Kfr37x+jRo2KWbNmxYEDB6KlpSW2bt0av//970+6v2HDhsWaNWuiqakp9u/fH4899ljrtl27dsWLL74YBw8ejOrq6ujRo0frcbzuuuviZz/7WfzjH/+Id955JxYsWNCu+QEob2IKoIs7cuRI/OAHP4iRI0dGXV1d7NmzJ+65556TvucTn/hE3HHHHTFp0qS48sor469//WsMHz78pO+ZOnVqDBo0KG655ZYYPnx43H777bFly5ZTztevX7/o2bNn1NfXx5QpU+KBBx6IwYMHt+vf9ulPfzq+973vxcyZM2PEiBFx2223RVNTU0REPPzww3H06NEYN25cfOYzn4lvfvOb8fbbb590f6NGjYpx48bFDTfcEDfddFNcffXVrdtaWlriiSeeiPr6+rjiiitizZo18cADD0RExC233BJ1dXUxceLEuPHGG+Pzn/98u+YHoLxVFIvFYqmHAODstHr16pg6dWrr15kDQDlxZQoAACDBF1AA8L9q/vz5x/3t0ftGjBgRd95550c+T2NjYyxduvRDz0+YMCFmzJjxkc8DQPlymx8AAECC2/wAAAASxBQAAECCmAIAAEgQUwAAAAliCgAAIEFMAQAAJPw/c3fPQHzbvM4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df_ipo1['money_raised_usd'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "Ljh2OL1l5IaU",
        "outputId": "283ad098-034f-4942-de11-f7d8c9edeb52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1f257a250>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gk = df_ipo1.groupby(['category'])"
      ],
      "metadata": {
        "id": "jEEGLG586AeV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipo_analysis=gk.mean()"
      ],
      "metadata": {
        "id": "tp_iX1wy6INy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipo_analysis.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "0uVb6fmp7nJb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipo_analysis.sort_values(by=['money_raised_usd'], ascending=False).head(8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "oqDNbUi88aSW",
        "outputId": "d2094998-d01b-4db2-f51f-63a03e316568"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         total_funding_usd  share_price_usd  \\\n",
              "category                                                      \n",
              "commerce and shopping          10000000.00             0.00   \n",
              "internet services                     0.00             7.50   \n",
              "energy                       3862273495.29             0.14   \n",
              "manufacturing                  23335714.29             0.07   \n",
              "food and beverage                     0.00             3.33   \n",
              "information technology        310000000.00             6.50   \n",
              "administrative services               0.00             0.00   \n",
              "financial services             28205128.21             0.76   \n",
              "\n",
              "                         valuation_price_usd  money_raised_usd  \n",
              "category                                                        \n",
              "commerce and shopping           948675770.33      478689609.00  \n",
              "internet services               888000000.00      139500000.00  \n",
              "energy                                  0.00      105428571.43  \n",
              "manufacturing                           0.00       77785599.57  \n",
              "food and beverage                       0.00       30000000.00  \n",
              "information technology           97500000.00       18700000.00  \n",
              "administrative services                 0.00       12415000.00  \n",
              "financial services               40539511.08        5063302.21  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a7d21676-60c9-45a1-80b4-5aa913151ad8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_funding_usd</th>\n",
              "      <th>share_price_usd</th>\n",
              "      <th>valuation_price_usd</th>\n",
              "      <th>money_raised_usd</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>commerce and shopping</th>\n",
              "      <td>10000000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>948675770.33</td>\n",
              "      <td>478689609.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>internet services</th>\n",
              "      <td>0.00</td>\n",
              "      <td>7.50</td>\n",
              "      <td>888000000.00</td>\n",
              "      <td>139500000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>energy</th>\n",
              "      <td>3862273495.29</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>105428571.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturing</th>\n",
              "      <td>23335714.29</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>77785599.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>food and beverage</th>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>30000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>information technology</th>\n",
              "      <td>310000000.00</td>\n",
              "      <td>6.50</td>\n",
              "      <td>97500000.00</td>\n",
              "      <td>18700000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>administrative services</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>12415000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>financial services</th>\n",
              "      <td>28205128.21</td>\n",
              "      <td>0.76</td>\n",
              "      <td>40539511.08</td>\n",
              "      <td>5063302.21</td>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grp_y=df.groupby(by=['Year']).mean()"
      ],
      "metadata": {
        "id": "OQfCZ2YIIOaL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grp_y.fillna(0).sort_values(by='total_funding_usd',ascending=False).round(2).drop(columns=['age','rank','raised_amount_usd'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "TXdoXG2gJLzb",
        "outputId": "f7c0e293-aaf5-4bbb-fbf6-f8a52352f5fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      total_funding_usd  employee_count  post_money_valuation_usd  \\\n",
              "Year                                                                \n",
              "2020       250410490.23          421.04              107819915.73   \n",
              "2008       200378472.50         5019.50                      0.00   \n",
              "2019       155542028.62          228.31                 767294.41   \n",
              "2009        78928639.54           81.38                      0.00   \n",
              "2014        53380535.71          828.25               13327916.67   \n",
              "2013        50434769.22          276.30                 504054.05   \n",
              "2015        41603842.72          282.41                 116883.72   \n",
              "2012        40706594.96          692.00                      0.00   \n",
              "2017        38142464.30          386.66               10535860.26   \n",
              "2016        36973385.50          239.20               22181922.02   \n",
              "2011        36535294.17          461.58                      0.00   \n",
              "2021        36419592.20           99.11               12498978.05   \n",
              "2018        34964750.81          130.77               10911114.14   \n",
              "2022        30051819.36          164.35               64827965.21   \n",
              "2010        19611666.67          112.33                5478333.33   \n",
              "2004        15000000.00           49.00                      0.00   \n",
              "1999        15000000.00           39.00                      0.00   \n",
              "2000        14000000.00          177.00                      0.00   \n",
              "2006         5250000.00        10000.00                      0.00   \n",
              "2007         2695935.50          269.00                      0.00   \n",
              "\n",
              "      investor_count  \n",
              "Year                  \n",
              "2020            1.83  \n",
              "2008            3.50  \n",
              "2019            2.16  \n",
              "2009            1.00  \n",
              "2014            1.33  \n",
              "2013            1.54  \n",
              "2015            1.72  \n",
              "2012            1.44  \n",
              "2017            2.04  \n",
              "2016            2.00  \n",
              "2011            1.00  \n",
              "2021            2.57  \n",
              "2018            1.80  \n",
              "2022            2.69  \n",
              "2010            2.33  \n",
              "2004            4.00  \n",
              "1999            0.00  \n",
              "2000            1.00  \n",
              "2006            1.00  \n",
              "2007            1.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09c304f1-60a3-49f2-9d6f-1b3e4d6dee1a\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_funding_usd</th>\n",
              "      <th>employee_count</th>\n",
              "      <th>post_money_valuation_usd</th>\n",
              "      <th>investor_count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>250410490.23</td>\n",
              "      <td>421.04</td>\n",
              "      <td>107819915.73</td>\n",
              "      <td>1.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>200378472.50</td>\n",
              "      <td>5019.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>155542028.62</td>\n",
              "      <td>228.31</td>\n",
              "      <td>767294.41</td>\n",
              "      <td>2.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>78928639.54</td>\n",
              "      <td>81.38</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
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              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>53380535.71</td>\n",
              "      <td>828.25</td>\n",
              "      <td>13327916.67</td>\n",
              "      <td>1.33</td>\n",
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              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>50434769.22</td>\n",
              "      <td>276.30</td>\n",
              "      <td>504054.05</td>\n",
              "      <td>1.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>41603842.72</td>\n",
              "      <td>282.41</td>\n",
              "      <td>116883.72</td>\n",
              "      <td>1.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>40706594.96</td>\n",
              "      <td>692.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>38142464.30</td>\n",
              "      <td>386.66</td>\n",
              "      <td>10535860.26</td>\n",
              "      <td>2.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>36973385.50</td>\n",
              "      <td>239.20</td>\n",
              "      <td>22181922.02</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>36535294.17</td>\n",
              "      <td>461.58</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>36419592.20</td>\n",
              "      <td>99.11</td>\n",
              "      <td>12498978.05</td>\n",
              "      <td>2.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>34964750.81</td>\n",
              "      <td>130.77</td>\n",
              "      <td>10911114.14</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022</th>\n",
              "      <td>30051819.36</td>\n",
              "      <td>164.35</td>\n",
              "      <td>64827965.21</td>\n",
              "      <td>2.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>19611666.67</td>\n",
              "      <td>112.33</td>\n",
              "      <td>5478333.33</td>\n",
              "      <td>2.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>15000000.00</td>\n",
              "      <td>49.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>15000000.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>14000000.00</td>\n",
              "      <td>177.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
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              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>5250000.00</td>\n",
              "      <td>10000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
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              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>2695935.50</td>\n",
              "      <td>269.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <script>\n",
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              "          document.querySelector('#df-09c304f1-60a3-49f2-9d6f-1b3e4d6dee1a button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-09c304f1-60a3-49f2-9d6f-1b3e4d6dee1a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=grp_y.index,y=grp_y['total_funding_usd'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "PreWcC6tUNLs",
        "outputId": "8fd0d01e-11d2-4010-a1c4-084fc9dc38d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1f24d4510>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=grp_y.index,y=grp_y['employee_count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "xHDpMnU5UwsI",
        "outputId": "5bd93e7f-3235-402d-b1c2-f82fcc653944"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff1f23f6b90>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grp_y_c=df.groupby(by=['Year','category']).mean()"
      ],
      "metadata": {
        "id": "bTCBjhBx2f9q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grp_y_c['total_funding_usd'].nlargest(30).sort_values(ascending=False).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_22OsQEy4gS6",
        "outputId": "4e367bbb-a96b-4b66-fdf4-ba850f6e87f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year  category               \n",
              "2020  energy                    11466497935.50\n",
              "2019  energy                     4261277514.50\n",
              "2009  education                  1000000000.00\n",
              "2013  professional services       771700000.00\n",
              "2014  professional services       771700000.00\n",
              "2015  professional services       771700000.00\n",
              "2017  professional services       771700000.00\n",
              "2018  professional services       771700000.00\n",
              "2019  government and military     500000000.00\n",
              "2022  energy                      500000000.00\n",
              "2015  health care                 413715000.00\n",
              "2008  health care                 400756945.00\n",
              "2016  professional services       386000000.00\n",
              "2022  manufacturing               326700000.00\n",
              "2017  energy                      299689400.60\n",
              "2013  administrative services     280000000.00\n",
              "2017  real estate                 236327041.67\n",
              "2019  real estate                 235497041.67\n",
              "2014  real estate                 230000000.00\n",
              "2022  transportation              203456762.00\n",
              "2011  energy                      200000000.00\n",
              "2017  transportation              185175000.00\n",
              "      agriculture and farming     153947340.00\n",
              "2021  agriculture and farming     153947340.00\n",
              "2013  real estate                 142000000.00\n",
              "2019  transportation              137163733.83\n",
              "2014  health care                 135955928.50\n",
              "2015  transportation              135925000.00\n",
              "      information technology      132152999.60\n",
              "2012  commerce and shopping       117912250.00\n",
              "Name: total_funding_usd, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grp_y_c['employee_count'].nlargest(30).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3JY1g1t7T9u",
        "outputId": "a64634fc-051d-4f7d-8510-003ce0ec8149"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year  category               \n",
              "2006  gaming                    10000.00\n",
              "2008  health care               10000.00\n",
              "2014  manufacturing             10000.00\n",
              "2020  energy                     7624.75\n",
              "2017  transportation             5074.50\n",
              "2021  energy                     5004.50\n",
              "2012  agriculture and farming    4999.00\n",
              "2013  professional services      3999.00\n",
              "2014  professional services      3999.00\n",
              "2015  professional services      3999.00\n",
              "2016  energy                     3999.00\n",
              "2017  professional services      3999.00\n",
              "2018  professional services      3999.00\n",
              "2019  government and military    3999.00\n",
              "2022  energy                     3999.00\n",
              "2020  clothing and apparel       3339.33\n",
              "2014  health care                2584.25\n",
              "2015  health care                2519.00\n",
              "2017  community and lifestyle    2514.25\n",
              "2019  energy                     2350.83\n",
              "2011  commerce and shopping      2124.00\n",
              "2016  professional services      2019.00\n",
              "2019  health care                2004.00\n",
              "2020  real estate                2004.00\n",
              "2014  energy                     1832.67\n",
              "2012  commerce and shopping      1545.25\n",
              "2020  health care                1464.86\n",
              "2022  community and lifestyle    1359.00\n",
              "2018  health care                1335.38\n",
              "2017  health care                1331.62\n",
              "Name: employee_count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grp_y_c.head(100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "yM6bt04FAkVt",
        "outputId": "342c45ee-b6ee-4ff5-f6e8-c4797b2e74ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             total_funding_usd  employee_count      rank  \\\n",
              "Year category                                                              \n",
              "1999 advertising                   15000000.00           39.00 367953.00   \n",
              "2000 advertising                   15000000.00           39.00 435249.00   \n",
              "     clothing and apparel          25000000.00          249.00 226384.00   \n",
              "     hardware                             0.00          499.00 197635.00   \n",
              "     information technology        15000000.00           49.00 346050.50   \n",
              "...                                        ...             ...       ...   \n",
              "2015 data and analytics               10000.00           19.00 321626.00   \n",
              "     education                        22500.00            9.00 344389.50   \n",
              "     events                         5400000.00           39.00 429688.00   \n",
              "     financial services             5720000.00           27.00 219610.60   \n",
              "     food and beverage                    0.00            9.00 330235.00   \n",
              "\n",
              "                             raised_amount_usd  post_money_valuation_usd  \\\n",
              "Year category                                                              \n",
              "1999 advertising                           NaN                      0.00   \n",
              "2000 advertising                   15000000.00                      0.00   \n",
              "     clothing and apparel          25000000.00                      0.00   \n",
              "     hardware                              NaN                      0.00   \n",
              "     information technology         7500000.00                      0.00   \n",
              "...                                        ...                       ...   \n",
              "2015 data and analytics               30000.00                      0.00   \n",
              "     education                        22500.00                      0.00   \n",
              "     events                         1300000.00                      0.00   \n",
              "     financial services             1800000.00                      0.00   \n",
              "     food and beverage                     NaN                1440000.00   \n",
              "\n",
              "                             investor_count    age  \n",
              "Year category                                       \n",
              "1999 advertising                        NaN  25.00  \n",
              "2000 advertising                       1.00  25.00  \n",
              "     clothing and apparel              1.00 132.00  \n",
              "     hardware                          1.00  25.00  \n",
              "     information technology            1.00  26.00  \n",
              "...                                     ...    ...  \n",
              "2015 data and analytics                1.50   8.00  \n",
              "     education                         1.00   7.00  \n",
              "     events                            1.00  10.00  \n",
              "     financial services                1.00   8.80  \n",
              "     food and beverage                 1.00   7.50  \n",
              "\n",
              "[100 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6ae59af6-eb04-45bb-80a4-4f792528910f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>total_funding_usd</th>\n",
              "      <th>employee_count</th>\n",
              "      <th>rank</th>\n",
              "      <th>raised_amount_usd</th>\n",
              "      <th>post_money_valuation_usd</th>\n",
              "      <th>investor_count</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <th>category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <th>advertising</th>\n",
              "      <td>15000000.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>367953.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">2000</th>\n",
              "      <th>advertising</th>\n",
              "      <td>15000000.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>435249.00</td>\n",
              "      <td>15000000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>25.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clothing and apparel</th>\n",
              "      <td>25000000.00</td>\n",
              "      <td>249.00</td>\n",
              "      <td>226384.00</td>\n",
              "      <td>25000000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>132.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hardware</th>\n",
              "      <td>0.00</td>\n",
              "      <td>499.00</td>\n",
              "      <td>197635.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>25.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>information technology</th>\n",
              "      <td>15000000.00</td>\n",
              "      <td>49.00</td>\n",
              "      <td>346050.50</td>\n",
              "      <td>7500000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>26.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2015</th>\n",
              "      <th>data and analytics</th>\n",
              "      <td>10000.00</td>\n",
              "      <td>19.00</td>\n",
              "      <td>321626.00</td>\n",
              "      <td>30000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.50</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <td>22500.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>344389.50</td>\n",
              "      <td>22500.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>events</th>\n",
              "      <td>5400000.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>429688.00</td>\n",
              "      <td>1300000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>financial services</th>\n",
              "      <td>5720000.00</td>\n",
              "      <td>27.00</td>\n",
              "      <td>219610.60</td>\n",
              "      <td>1800000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>8.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>food and beverage</th>\n",
              "      <td>0.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>330235.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1440000.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>7.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 7 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2010 2014 2021\n",
        "categorty 2021- category 2010"
      ],
      "metadata": {
        "id": "LVYEy9Y1F_6G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#mean from 2010-2015\n",
        "\n",
        "df_10_15=df[(df['Year']>=2010)&(df['Year']<=2015)]\n",
        "grp_c_15=df_10_15.groupby(by=['category']).mean()\n",
        "\n",
        "\n",
        "df_16_22=df[(df['Year']>=2016)&(df['Year']<=2022)]\n",
        "grp_c_22=df_16_22.groupby(by=['category']).mean()\n"
      ],
      "metadata": {
        "id": "ZwFVGdqVLARh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "JrO0-3FMa1ts"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grp_c_15.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8y4uwjinNgOo",
        "outputId": "555ee83d-273e-4c29-f12a-3a9c68b194dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['administrative services', 'advertising', 'agriculture and farming',\n",
              "       'apps', 'artificial intelligence', 'biotechnology',\n",
              "       'clothing and apparel', 'commerce and shopping',\n",
              "       'community and lifestyle', 'consumer electronics', 'consumer goods',\n",
              "       'content and publishing', 'data and analytics', 'education', 'energy',\n",
              "       'events', 'financial services', 'food and beverage', 'gaming',\n",
              "       'hardware', 'health care', 'information technology',\n",
              "       'internet services', 'manufacturing', 'media and entertainment',\n",
              "       'other', 'professional services', 'real estate', 'software',\n",
              "       'transportation'],\n",
              "      dtype='object', name='category')"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "common = grp_c_15.index.intersection(grp_c_22.index)"
      ],
      "metadata": {
        "id": "PUblxbK3N3q-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grp_c_15.loc[common]"
      ],
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          "height": 1000
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        "id": "dAN5K2K7iLBT",
        "outputId": "61c1ccf3-7ed6-4670-c788-a83f2475f31a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         total_funding_usd  employee_count      rank  \\\n",
              "category                                                               \n",
              "administrative services        98204740.83          199.00 345909.67   \n",
              "advertising                     1501322.50           29.83 252005.00   \n",
              "agriculture and farming        15000000.00         4999.00 185613.00   \n",
              "apps                            4976785.71           50.50 254773.21   \n",
              "artificial intelligence               0.00           39.00 475296.00   \n",
              "biotechnology                  69666666.67           35.67 364300.67   \n",
              "clothing and apparel           11317777.78          182.33 312469.78   \n",
              "commerce and shopping          43423995.66          684.34 293504.39   \n",
              "community and lifestyle         5498400.00           22.00 215609.90   \n",
              "consumer electronics            1488750.00           45.25 227465.12   \n",
              "consumer goods                    30000.00            9.00 286506.50   \n",
              "content and publishing           225000.00           31.75 132703.00   \n",
              "data and analytics                10000.00           19.00 321626.00   \n",
              "education                        169000.00           27.00 296289.00   \n",
              "energy                         81666666.67         1005.83 302194.17   \n",
              "events                          3417203.25           66.50 296674.50   \n",
              "financial services             20612759.93           63.29 233809.93   \n",
              "food and beverage                     0.00            9.00 329803.67   \n",
              "gaming                           150000.00           49.00 111347.00   \n",
              "hardware                         238333.33            9.00 344227.00   \n",
              "health care                   160583511.44         1733.56 352034.78   \n",
              "information technology         95150646.86          107.57 340083.57   \n",
              "internet services                502443.00           16.20 266362.50   \n",
              "manufacturing                  10731092.00         5074.50 365179.50   \n",
              "media and entertainment           30000.00            9.00 429179.00   \n",
              "other                            712166.67           22.50 222795.67   \n",
              "professional services         771700000.00         3999.00 227484.67   \n",
              "real estate                   124333333.33          145.67 362909.67   \n",
              "software                        1000000.00            9.00 205939.00   \n",
              "transportation                 91116666.67           75.67 350718.33   \n",
              "\n",
              "                         raised_amount_usd  post_money_valuation_usd  \\\n",
              "category                                                               \n",
              "administrative services          514222.50                  38333.33   \n",
              "advertising                     1409166.67                  16666.67   \n",
              "agriculture and farming                NaN                      0.00   \n",
              "apps                             455500.00                 142857.14   \n",
              "artificial intelligence                NaN                      0.00   \n",
              "biotechnology                  49666666.67               45333333.33   \n",
              "clothing and apparel            8965000.00                      0.00   \n",
              "commerce and shopping           5015541.67                8147500.00   \n",
              "community and lifestyle         2259142.86                3287000.00   \n",
              "consumer electronics             411428.57                      0.00   \n",
              "consumer goods                    30000.00                      0.00   \n",
              "content and publishing           166666.67                      0.00   \n",
              "data and analytics                30000.00                      0.00   \n",
              "education                        148333.33                      0.00   \n",
              "energy                         72500000.00                      0.00   \n",
              "events                           502524.33                      0.00   \n",
              "financial services             24137863.90                      0.00   \n",
              "food and beverage                      NaN                 960000.00   \n",
              "gaming                           150000.00                      0.00   \n",
              "hardware                         357500.00                      0.00   \n",
              "health care                   111812376.12                      0.00   \n",
              "information technology        103799166.67                 157857.14   \n",
              "internet services                432061.43                 124000.00   \n",
              "manufacturing                  21462184.00                      0.00   \n",
              "media and entertainment           30000.00                      0.00   \n",
              "other                            292600.00                 180333.33   \n",
              "professional services          23900000.00                      0.00   \n",
              "real estate                    77666666.67                6083333.33   \n",
              "software                        1000000.00                      0.00   \n",
              "transportation                   130000.00                 103333.33   \n",
              "\n",
              "                         investor_count    Year   age  \n",
              "category                                               \n",
              "administrative services            2.17 2014.33 10.67  \n",
              "advertising                        1.10 2013.42 10.92  \n",
              "agriculture and farming            1.00 2012.00 55.00  \n",
              "apps                               1.27 2014.00  9.00  \n",
              "artificial intelligence             NaN 2013.00  9.00  \n",
              "biotechnology                      2.00 2012.33 14.00  \n",
              "clothing and apparel               2.11 2013.00 10.44  \n",
              "commerce and shopping              1.71 2013.85 11.35  \n",
              "community and lifestyle            1.00 2012.80 10.70  \n",
              "consumer electronics               1.83 2013.88  8.88  \n",
              "consumer goods                     1.00 2014.50  8.00  \n",
              "content and publishing             1.00 2013.25 10.00  \n",
              "data and analytics                 1.50 2015.00  8.00  \n",
              "education                          1.00 2013.60 10.60  \n",
              "energy                             2.00 2012.33 18.83  \n",
              "events                             1.67 2013.25  9.75  \n",
              "financial services                 1.00 2013.64 10.00  \n",
              "food and beverage                  1.00 2014.00  8.33  \n",
              "gaming                             1.00 2012.00 10.00  \n",
              "hardware                           1.00 2013.67  9.00  \n",
              "health care                        1.83 2013.78 16.44  \n",
              "information technology             1.00 2014.29 14.57  \n",
              "internet services                  1.33 2014.20  8.20  \n",
              "manufacturing                      1.00 2013.00 24.00  \n",
              "media and entertainment            1.00 2015.00  8.00  \n",
              "other                              1.00 2014.00  7.83  \n",
              "professional services              4.67 2014.00 10.00  \n",
              "real estate                        1.50 2014.00 14.67  \n",
              "software                            NaN 2012.00 10.00  \n",
              "transportation                     1.50 2014.67  7.67  "
            ],
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              "      <th>administrative services</th>\n",
              "      <td>98204740.83</td>\n",
              "      <td>199.00</td>\n",
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              "      <td>514222.50</td>\n",
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              "      <td>1409166.67</td>\n",
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              "      <th>agriculture and farming</th>\n",
              "      <td>15000000.00</td>\n",
              "      <td>4999.00</td>\n",
              "      <td>185613.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2012.00</td>\n",
              "      <td>55.00</td>\n",
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              "      <td>4976785.71</td>\n",
              "      <td>50.50</td>\n",
              "      <td>254773.21</td>\n",
              "      <td>455500.00</td>\n",
              "      <td>142857.14</td>\n",
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              "      <td>475296.00</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>35.67</td>\n",
              "      <td>364300.67</td>\n",
              "      <td>49666666.67</td>\n",
              "      <td>45333333.33</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2012.33</td>\n",
              "      <td>14.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clothing and apparel</th>\n",
              "      <td>11317777.78</td>\n",
              "      <td>182.33</td>\n",
              "      <td>312469.78</td>\n",
              "      <td>8965000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.11</td>\n",
              "      <td>2013.00</td>\n",
              "      <td>10.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>commerce and shopping</th>\n",
              "      <td>43423995.66</td>\n",
              "      <td>684.34</td>\n",
              "      <td>293504.39</td>\n",
              "      <td>5015541.67</td>\n",
              "      <td>8147500.00</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2013.85</td>\n",
              "      <td>11.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>community and lifestyle</th>\n",
              "      <td>5498400.00</td>\n",
              "      <td>22.00</td>\n",
              "      <td>215609.90</td>\n",
              "      <td>2259142.86</td>\n",
              "      <td>3287000.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2012.80</td>\n",
              "      <td>10.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>consumer electronics</th>\n",
              "      <td>1488750.00</td>\n",
              "      <td>45.25</td>\n",
              "      <td>227465.12</td>\n",
              "      <td>411428.57</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.83</td>\n",
              "      <td>2013.88</td>\n",
              "      <td>8.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>consumer goods</th>\n",
              "      <td>30000.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>286506.50</td>\n",
              "      <td>30000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2014.50</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>content and publishing</th>\n",
              "      <td>225000.00</td>\n",
              "      <td>31.75</td>\n",
              "      <td>132703.00</td>\n",
              "      <td>166666.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2013.25</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data and analytics</th>\n",
              "      <td>10000.00</td>\n",
              "      <td>19.00</td>\n",
              "      <td>321626.00</td>\n",
              "      <td>30000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.50</td>\n",
              "      <td>2015.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <td>169000.00</td>\n",
              "      <td>27.00</td>\n",
              "      <td>296289.00</td>\n",
              "      <td>148333.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2013.60</td>\n",
              "      <td>10.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>energy</th>\n",
              "      <td>81666666.67</td>\n",
              "      <td>1005.83</td>\n",
              "      <td>302194.17</td>\n",
              "      <td>72500000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2012.33</td>\n",
              "      <td>18.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>events</th>\n",
              "      <td>3417203.25</td>\n",
              "      <td>66.50</td>\n",
              "      <td>296674.50</td>\n",
              "      <td>502524.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.67</td>\n",
              "      <td>2013.25</td>\n",
              "      <td>9.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>financial services</th>\n",
              "      <td>20612759.93</td>\n",
              "      <td>63.29</td>\n",
              "      <td>233809.93</td>\n",
              "      <td>24137863.90</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2013.64</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>food and beverage</th>\n",
              "      <td>0.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>329803.67</td>\n",
              "      <td>NaN</td>\n",
              "      <td>960000.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2014.00</td>\n",
              "      <td>8.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gaming</th>\n",
              "      <td>150000.00</td>\n",
              "      <td>49.00</td>\n",
              "      <td>111347.00</td>\n",
              "      <td>150000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2012.00</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hardware</th>\n",
              "      <td>238333.33</td>\n",
              "      <td>9.00</td>\n",
              "      <td>344227.00</td>\n",
              "      <td>357500.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2013.67</td>\n",
              "      <td>9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>health care</th>\n",
              "      <td>160583511.44</td>\n",
              "      <td>1733.56</td>\n",
              "      <td>352034.78</td>\n",
              "      <td>111812376.12</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.83</td>\n",
              "      <td>2013.78</td>\n",
              "      <td>16.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>information technology</th>\n",
              "      <td>95150646.86</td>\n",
              "      <td>107.57</td>\n",
              "      <td>340083.57</td>\n",
              "      <td>103799166.67</td>\n",
              "      <td>157857.14</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2014.29</td>\n",
              "      <td>14.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>internet services</th>\n",
              "      <td>502443.00</td>\n",
              "      <td>16.20</td>\n",
              "      <td>266362.50</td>\n",
              "      <td>432061.43</td>\n",
              "      <td>124000.00</td>\n",
              "      <td>1.33</td>\n",
              "      <td>2014.20</td>\n",
              "      <td>8.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturing</th>\n",
              "      <td>10731092.00</td>\n",
              "      <td>5074.50</td>\n",
              "      <td>365179.50</td>\n",
              "      <td>21462184.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2013.00</td>\n",
              "      <td>24.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>media and entertainment</th>\n",
              "      <td>30000.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>429179.00</td>\n",
              "      <td>30000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2015.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>other</th>\n",
              "      <td>712166.67</td>\n",
              "      <td>22.50</td>\n",
              "      <td>222795.67</td>\n",
              "      <td>292600.00</td>\n",
              "      <td>180333.33</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2014.00</td>\n",
              "      <td>7.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>professional services</th>\n",
              "      <td>771700000.00</td>\n",
              "      <td>3999.00</td>\n",
              "      <td>227484.67</td>\n",
              "      <td>23900000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4.67</td>\n",
              "      <td>2014.00</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>real estate</th>\n",
              "      <td>124333333.33</td>\n",
              "      <td>145.67</td>\n",
              "      <td>362909.67</td>\n",
              "      <td>77666666.67</td>\n",
              "      <td>6083333.33</td>\n",
              "      <td>1.50</td>\n",
              "      <td>2014.00</td>\n",
              "      <td>14.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>software</th>\n",
              "      <td>1000000.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>205939.00</td>\n",
              "      <td>1000000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012.00</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transportation</th>\n",
              "      <td>91116666.67</td>\n",
              "      <td>75.67</td>\n",
              "      <td>350718.33</td>\n",
              "      <td>130000.00</td>\n",
              "      <td>103333.33</td>\n",
              "      <td>1.50</td>\n",
              "      <td>2014.67</td>\n",
              "      <td>7.67</td>\n",
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          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "growth=grp_c_22.loc[common]-grp_c_15.loc[common]"
      ],
      "metadata": {
        "id": "TPhhXI95i89m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "growth.sort_values(by='total_funding_usd',ascending=False).drop(columns=['rank','age','Year','raised_amount_usd']).round(2).fillna(1).head(19)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "nvq7v4M5jruN",
        "outputId": "55ed6676-bc35-43ba-e37c-f2a6701c8839"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         total_funding_usd  employee_count  \\\n",
              "category                                                     \n",
              "energy                       3592385055.63         2356.97   \n",
              "agriculture and farming        55260357.20        -4517.50   \n",
              "manufacturing                  49693908.00        -4892.17   \n",
              "apps                           43945473.26          -15.95   \n",
              "artificial intelligence        38212133.27           -1.98   \n",
              "content and publishing         37553515.38           49.75   \n",
              "financial services             25264146.55           46.16   \n",
              "transportation                 14794135.50          287.08   \n",
              "food and beverage               9009595.50           76.95   \n",
              "community and lifestyle         4205160.06          411.19   \n",
              "consumer electronics            3736416.67          -10.42   \n",
              "education                       3570392.71           84.78   \n",
              "software                        2917639.06           56.25   \n",
              "internet services               1708770.93           67.47   \n",
              "other                           1675149.08            6.80   \n",
              "data and analytics              1295954.47            9.41   \n",
              "media and entertainment          993977.00           19.29   \n",
              "consumer goods                   450000.00           30.00   \n",
              "gaming                           179765.00          -29.50   \n",
              "\n",
              "                         post_money_valuation_usd  investor_count  \n",
              "category                                                           \n",
              "energy                              1020482750.00            1.09  \n",
              "agriculture and farming                      0.00            0.05  \n",
              "manufacturing                        739783333.33            3.00  \n",
              "apps                                    966269.06            0.88  \n",
              "artificial intelligence               23309777.78            1.00  \n",
              "content and publishing                  681405.00            0.60  \n",
              "financial services                     2527260.56            1.70  \n",
              "transportation                          639523.81            0.80  \n",
              "food and beverage                      4426031.50            0.56  \n",
              "community and lifestyle               -3189777.78            1.05  \n",
              "consumer electronics                   4500000.00           -0.67  \n",
              "education                              1138452.71            0.78  \n",
              "software                                     0.00            1.00  \n",
              "internet services                       226000.00            0.13  \n",
              "other                                  2515028.71            0.75  \n",
              "data and analytics                      431941.18            0.50  \n",
              "media and entertainment                      0.00            1.00  \n",
              "consumer goods                               0.00            0.00  \n",
              "gaming                                       0.00            1.00  "
            ],
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              "      <th></th>\n",
              "      <th>total_funding_usd</th>\n",
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              "      <th>energy</th>\n",
              "      <td>3592385055.63</td>\n",
              "      <td>2356.97</td>\n",
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              "      <td>1.09</td>\n",
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              "      <th>agriculture and farming</th>\n",
              "      <td>55260357.20</td>\n",
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              "      <td>-1.98</td>\n",
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              "      <td>1.00</td>\n",
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              "      <td>49.75</td>\n",
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              "      <td>46.16</td>\n",
              "      <td>2527260.56</td>\n",
              "      <td>1.70</td>\n",
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              "      <th>transportation</th>\n",
              "      <td>14794135.50</td>\n",
              "      <td>287.08</td>\n",
              "      <td>639523.81</td>\n",
              "      <td>0.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>food and beverage</th>\n",
              "      <td>9009595.50</td>\n",
              "      <td>76.95</td>\n",
              "      <td>4426031.50</td>\n",
              "      <td>0.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>community and lifestyle</th>\n",
              "      <td>4205160.06</td>\n",
              "      <td>411.19</td>\n",
              "      <td>-3189777.78</td>\n",
              "      <td>1.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>consumer electronics</th>\n",
              "      <td>3736416.67</td>\n",
              "      <td>-10.42</td>\n",
              "      <td>4500000.00</td>\n",
              "      <td>-0.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <td>3570392.71</td>\n",
              "      <td>84.78</td>\n",
              "      <td>1138452.71</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>software</th>\n",
              "      <td>2917639.06</td>\n",
              "      <td>56.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>internet services</th>\n",
              "      <td>1708770.93</td>\n",
              "      <td>67.47</td>\n",
              "      <td>226000.00</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>other</th>\n",
              "      <td>1675149.08</td>\n",
              "      <td>6.80</td>\n",
              "      <td>2515028.71</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data and analytics</th>\n",
              "      <td>1295954.47</td>\n",
              "      <td>9.41</td>\n",
              "      <td>431941.18</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>media and entertainment</th>\n",
              "      <td>993977.00</td>\n",
              "      <td>19.29</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>consumer goods</th>\n",
              "      <td>450000.00</td>\n",
              "      <td>30.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gaming</th>\n",
              "      <td>179765.00</td>\n",
              "      <td>-29.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
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              "  </tbody>\n",
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              "  </svg>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-595d942a-6102-4c28-88b1-85ac04e36cff button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-595d942a-6102-4c28-88b1-85ac04e36cff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "growth.sort_values(by='total_funding_usd',ascending=True).drop(columns=['rank','age','Year','raised_amount_usd']).round(2).head(11)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "B2H7outNozMN",
        "outputId": "16f40192-d8b4-498f-9254-428a5c2d3c51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         total_funding_usd  employee_count  \\\n",
              "category                                                     \n",
              "professional services        -462943000.00        -2382.00   \n",
              "health care                  -117038767.22         -740.22   \n",
              "information technology        -92772697.97          -66.06   \n",
              "biotechnology                 -69166666.67          -11.67   \n",
              "administrative services       -54895968.12          126.78   \n",
              "commerce and shopping         -20639327.92         -514.91   \n",
              "real estate                   -16836330.90          157.21   \n",
              "clothing and apparel           -7372444.44          395.72   \n",
              "events                         -1092039.12           -2.50   \n",
              "advertising                     -336476.59           -4.02   \n",
              "hardware                         -53688.88           64.64   \n",
              "\n",
              "                         post_money_valuation_usd  investor_count  \n",
              "category                                                           \n",
              "professional services                420000000.00           -1.17  \n",
              "health care                             694444.44            0.57  \n",
              "information technology                  419681.67            0.67  \n",
              "biotechnology                        -45333333.33           -0.50  \n",
              "administrative services               23750454.72            0.35  \n",
              "commerce and shopping                  3316697.46            0.50  \n",
              "real estate                           65296037.04           -0.25  \n",
              "clothing and apparel                   6944444.44           -0.71  \n",
              "events                                   37500.19            0.33  \n",
              "advertising                              -6212.12            0.69  \n",
              "hardware                                272727.27            0.20  "
            ],
            "text/html": [
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              "      <th>total_funding_usd</th>\n",
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              "      <td>157.21</td>\n",
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              "      <td>-0.25</td>\n",
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              "      <th>clothing and apparel</th>\n",
              "      <td>-7372444.44</td>\n",
              "      <td>395.72</td>\n",
              "      <td>6944444.44</td>\n",
              "      <td>-0.71</td>\n",
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              "    <tr>\n",
              "      <th>events</th>\n",
              "      <td>-1092039.12</td>\n",
              "      <td>-2.50</td>\n",
              "      <td>37500.19</td>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>advertising</th>\n",
              "      <td>-336476.59</td>\n",
              "      <td>-4.02</td>\n",
              "      <td>-6212.12</td>\n",
              "      <td>0.69</td>\n",
              "    </tr>\n",
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              "      <th>hardware</th>\n",
              "      <td>-53688.88</td>\n",
              "      <td>64.64</td>\n",
              "      <td>272727.27</td>\n",
              "      <td>0.20</td>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4aeb216-2517-470d-b985-dcd730bd244f')\"\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grp_c_15.drop(columns=['rank','age','Year','raised_amount_usd']).corr()\n",
        "sns.heatmap(data =grp_c_15.drop(columns=['rank','age','Year','raised_amount_usd']).corr())\n",
        "plt.savefig('/content/drive/MyDrive/Upwork_Projects/Nurman_Naz/Plots/correlation_heatmap1.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "qGjsJmO8x0VA",
        "outputId": "eb9cda0c-fe52-438e-b0d7-add71dc90ea4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grp_c_22.drop(columns=['rank','age','Year','raised_amount_usd']).corr()\n",
        "sns.heatmap(data =grp_c_22.drop(columns=['rank','age','Year','raised_amount_usd']).corr())\n",
        "plt.savefig('/content/drive/MyDrive/Upwork_Projects/Nurman_Naz/Plots/correlation_heatmap2.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "6oYDiF-KPoq0",
        "outputId": "a472438a-3241-4e09-c804-b9439782bf11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Track on time period"
      ],
      "metadata": {
        "id": "IS5PghsSq8rd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLECqymjq-4j",
        "outputId": "3c83ef68-3209-4f1f-be0d-82cfa37759d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['roles', 'total_funding_usd', 'employee_count', 'rank', 'city',\n",
              "       'investment_type', 'announced_on', 'raised_amount_usd',\n",
              "       'post_money_valuation_usd', 'investor_count', 'org_uuid', 'org_name',\n",
              "       'lead_investor_uuids', 'round', 'Year', 'category', 'age'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def year1(y):\n",
        "  if(y>2016):\n",
        "    return '2016-2022'\n",
        "  elif(y>2010 & y<= 2016):\n",
        "    return '2010-2015'\n",
        "  else:\n",
        "    return '2000-2010'\n"
      ],
      "metadata": {
        "id": "B-bH4dg4rfvx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Year_Category']=df['Year'].apply(year1)"
      ],
      "metadata": {
        "id": "bB4ClRCArYX5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Year_Category'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qyh6wLuisTBQ",
        "outputId": "c36b23d5-fa14-43e4-c184-d794bcea5ad9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2010-2015', '2016-2022', '2000-2010'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_c_yc=df.groupby(by=['Year_Category','category']).mean()"
      ],
      "metadata": {
        "id": "QTZ-k6VJsA4B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_c_yc.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "drLyOWNZuOoN",
        "outputId": "133848e8-271a-42e3-fd7d-dfd41f203b89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['total_funding_usd', 'employee_count', 'rank', 'raised_amount_usd',\n",
              "       'post_money_valuation_usd', 'investor_count', 'Year', 'age'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_c_yc.nlargest(15,[\"total_funding_usd\"])['total_funding_usd']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPRe6GmZsZao",
        "outputId": "12638f40-89cd-41cb-d88a-57173361b24a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year_Category  category               \n",
              "2016-2022      energy                    3867248823.47\n",
              "2010-2015      professional services      617420000.00\n",
              "2016-2022      government and military    500000000.00\n",
              "2000-2010      health care                400756945.00\n",
              "2016-2022      professional services      289446250.00\n",
              "2010-2015      education                  145052142.86\n",
              "               health care                131624465.00\n",
              "2016-2022      real estate                112532288.50\n",
              "               transportation             112282668.97\n",
              "2010-2015      real estate                103500000.00\n",
              "               energy                      79717800.00\n",
              "               biotechnology               74500000.00\n",
              "2016-2022      agriculture and farming     69292211.33\n",
              "               manufacturing               60425000.00\n",
              "2000-2010      biotechnology               60000000.00\n",
              "Name: total_funding_usd, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_c_yc.nlargest(15,[\"post_money_valuation_usd\"])['post_money_valuation_usd']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qfK7X54yg3ZT",
        "outputId": "2038e78f-ccc4-4575-d073-32aec95a9ede"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year_Category  category               \n",
              "2016-2022      energy                    1074192368.42\n",
              "               manufacturing              739783333.33\n",
              "               professional services      400000000.00\n",
              "2010-2015      professional services      200000000.00\n",
              "               biotechnology               68000000.00\n",
              "2016-2022      real estate                 66933566.14\n",
              "2010-2015      real estate                 44650000.00\n",
              "2016-2022      administrative services     25991453.61\n",
              "               artificial intelligence     24974761.90\n",
              "2010-2015      commerce and shopping       17765263.16\n",
              "2000-2010      community and lifestyle     16435000.00\n",
              "2016-2022      clothing and apparel         7812500.00\n",
              "               commerce and shopping        7426075.28\n",
              "               food and beverage            5642509.19\n",
              "               consumer electronics         4909090.91\n",
              "Name: post_money_valuation_usd, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_c_yc.nlargest(15,[\"employee_count\"])['employee_count']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehKhaye833OW",
        "outputId": "182c4776-7241-46d1-c88e-3d642d8f5b31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year_Category  category               \n",
              "2000-2010      health care               10000.00\n",
              "2016-2022      government and military    3999.00\n",
              "2010-2015      manufacturing              3396.00\n",
              "               gaming                     3362.67\n",
              "2016-2022      energy                     3329.32\n",
              "2010-2015      professional services      3207.00\n",
              "               agriculture and farming    1692.33\n",
              "               energy                     1665.83\n",
              "2016-2022      professional services      1516.50\n",
              "2010-2015      health care                1425.45\n",
              "2016-2022      health care                 993.33\n",
              "               clothing and apparel        649.19\n",
              "2010-2015      commerce and shopping       557.45\n",
              "2016-2022      agriculture and farming     530.67\n",
              "2000-2010      hardware                    499.00\n",
              "Name: employee_count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['round'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XIpi0fJH5b2K",
        "outputId": "6c25353f-9fe7-45d3-afdd-5ceedb6b1f76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Venture Round', 'Seed Round', 'Pre Seed Round', 'Series A',\n",
              "       'Corporate Round', 'Debt Financing', 'Private Equity Round',\n",
              "       'Series B', 'Series C', 'Secondary Market', 'Angel Round',\n",
              "       'Convertible Note', 'Funding Round', 'Post', 'Series D',\n",
              "       'Series E', 'Series F', 'Grant', 'Equity Crowdfunding',\n",
              "       'Non Equity Assistance', 'Product Crowdfunding',\n",
              "       'Initial Coin Offering'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTOB3E9C6W3q",
        "outputId": "5f7f0a97-2d7d-4d19-a1f1-27416e12d2a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['roles', 'total_funding_usd', 'employee_count', 'rank', 'city',\n",
              "       'investment_type', 'announced_on', 'raised_amount_usd',\n",
              "       'post_money_valuation_usd', 'investor_count', 'org_uuid', 'org_name',\n",
              "       'lead_investor_uuids', 'round', 'Year', 'category', 'age',\n",
              "       'Year_Category'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "7cev_2bL8xSw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        " \n",
        "# read the csv data\n",
        "df1=df[df['category']=='commerce and shopping']\n",
        "data = df1[['announced_on','raised_amount_usd']].set_index('announced_on') \n",
        "# plot the auto correlation\n",
        "data=data.fillna(0)"
      ],
      "metadata": {
        "id": "IzUCFoN06S8B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "324068ba-538b-41f3-a1c4-4a0fb9ccd242"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_acf(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6NI2Jgxr7DVh",
        "outputId": "d717569b-0f77-4c6a-e018-a844aef6dec8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.graphics.tsaplots import plot_pacf\n",
        "plot_pacf(data, lags=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "n8pEFDnb7iR6",
        "outputId": "86780b58-db2e-4cf7-b4e9-81c3d7dc1b43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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NAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcMINAACgcI19OWjJkiW54IILsnr16gwbNiyzZs3Knnvuuckx7e3t+cQnPpG2trZ0dnbmsMMOy7/+67+msbFPvwQAAAAvoE9n3GbMmJGpU6fmtttuy9SpUzN9+vTnHXPNNddkr732ys0335ybbropv/3tb7NgwYJtPjAAAMCOptdwa29vz6JFi9La2pokaW1tzaJFi7Jq1apNjqurq8v69etTqVTS0dGRjRs3ZvTo0dtnagAAgB1Ir9cxtrW1ZfTo0WloaEiSNDQ0ZNSoUWlra8uIESN6jjv77LNz7rnn5sgjj8xTTz2V0047La997Wu3apjm5p23cvztq6np2Ydn5MghNZ6E/s4OUS07RLXsENWyQ1TLDlVnm70A7dZbb82+++6b6667LuvXr8+0adNy66235rjjjuvz52hvX5dKpXtbjVS1jo7ONDU1ZuXKtbUehX5s5Mghdoiq2CGqZYeolh2iWnaod/X1dVs8kdXrpZItLS1Zvnx5urq6kiRdXV1ZsWJFWlpaNjluzpw5OfHEE1NfX58hQ4ZkwoQJufvuu6scHwAAgF7Drbm5OePHj8/8+fOTJPPnz8/48eM3uUwySXbbbbf8+Mc/TpJ0dHTkZz/7WfbZZ5/tMDIAAMCOpU/vKnnRRRdlzpw5mThxYubMmZOZM2cmSaZNm5b7778/SXLhhRfmV7/6VSZNmpSTTjope+65Z0499dTtNzkAAMAOok+vcdtrr70yd+7c591+7bXX9vx4jz32yDe+8Y1tNxkAAABJ+njGDQAAgNoRbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIUTbgAAAIXrU7gtWbIkU6ZMycSJEzNlypQ8/PDDmz3ulltuyaRJk9La2ppJkybl8ccf35azAgAA7JAa+3LQjBkzMnXq1EyePDk33nhjpk+fnm9961ubHHP//ffnyiuvzHXXXZeRI0dm7dq1aWpq2i5DAwAA7Eh6PePW3t6eRYsWpbW1NUnS2tqaRYsWZdWqVZsc981vfjNnnnlmRo4cmSQZMmRIBg4cuB1GBgAA2LH0Gm5tbW0ZPXp0GhoakiQNDQ0ZNWpU2traNjnuoYceytKlS3Paaafl5JNPzlVXXZXu7u7tMzUAAMAOpE+XSvZFV1dXfv/73+cb3/hGOjo6ctZZZ2Xs2LE56aST+vw5mpt33lbjbBNNTc8+PCNHDqnxJPR3dohq2SGqZYeolh2iWnaoOr2GW0tLS5YvX56urq40NDSkq6srK1asSEtLyybHjR07Nscdd1yamprS1NSUY445Jvfdd99WhVt7+7pUKuWcpevo6ExTU2NWrlxb61Hox0aOHGKHqIodolp2iGrZIaplh3pXX1+3xRNZvV4q2dzcnPHjx2f+/PlJkvnz52f8+PEZMWLEJse1trbmrrvuSnd3dzZu3Jif//zn2W+//aocHwAAgD79cwAXXXRR5syZk4kTJ2bOnDmZOXNmkmTatGm5//77kyQnnHBCmpub89a3vjUnnXRS9t577/zDP/zD9pscAABgB1HXXdA7iJR2qeS73/32NDU15uqrv1HrUejHXBpAtewQ1bJDVMsOUS071LuqL5UEAACgtoQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4foUbkuWLMmUKVMyceLETJkyJQ8//PALHrt48eIceOCBmTVr1raaEQAAYIfWp3CbMWNGpk6dmttuuy1Tp07N9OnTN3tcV1dXZsyYkWOPPXabDgkAALAj6zXc2tvbs2jRorS2tiZJWltbs2jRoqxatep5x37lK1/J0UcfnT333HObDwoAALCj6jXc2traMnr06DQ0NCRJGhoaMmrUqLS1tW1y3AMPPJC77rorZ5xxxnYZFAAAYEfVuC0+ycaNG/PJT34yn/rUp3oC78Vobt55W4yzzTQ1PfvwjBw5pMaT0N/ZIaplh6iWHaJadohq2aHq9BpuLS0tWb58ebq6utLQ0JCurq6sWLEiLS0tPcesXLkyjzzySN7znvckSdasWZPu7u6sW7cul1xySZ+HaW9fl0ql+0V8GdtHR0dnmpoas3Ll2lqPQj82cuQQO0RV7BDVskNUyw5RLTvUu/r6ui2eyOo13JqbmzN+/PjMnz8/kydPzvz58zN+/PiMGDGi55ixY8fm7rvv7vn4iiuuyIYNG/Lxj3+8yvEBAADo07tKXnTRRZkzZ04mTpyYOXPmZObMmUmSadOm5f7779+uAwIAAOzo+vQat7322itz58593u3XXnvtZo8/99xzq5sKAACAHn064wYAAEDtCDcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCNdZ6APqvSqU79y1uzyPL12aP0UNywLjm1NfX1XosAAB42RFuvCiVSnc+9++/zuJla9KxsStNAxoybuzQfHjKQeINAAC2MZdK8qLct7g9i5etyTMbu9Kd5JmNXVm8bE3uW9xe69EAAOBlR7jxojyyfG06NnZtclvHxq4sXb62RhMBAMDLl3DjRdlj9JA0DWjY5LamAQ3ZffSQGk0EAAAvX8KNF+WAcc0ZN3ZoUtmYdHdn4P++xu2Acc21Hg0AAF52hBsvSn19XT485aDksTuT9oV57+T9vTEJAABsJ95Vkhetvr4udesfTdY/moP23qXW4wAAwMuWM24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFE24AAACFa+zLQUuWLMkFF1yQ1atXZ9iwYZk1a1b23HPPTY6ZPXt2brnlltTX12fAgAH54Ac/mKOOOmp7zAwAALBD6VO4zZgxI1OnTs3kyZNz4403Zvr06fnWt761yTEHHHBAzjzzzAwaNCgPPPBATj/99Nx1113ZaaedtsvgAAAAO4peL5Vsb2/PokWL0tramiRpbW3NokWLsmrVqk2OO+qoozJo0KAkyb777pvu7u6sXr16O4wMAACwY+n1jFtbW1tGjx6dhoaGJElDQ0NGjRqVtra2jBgxYrP3mTdvXvbYY4+MGTNmq4Zpbt55q47f3pqann14Ro4cUuNJyuUx6huPD9WyQ1TLDlEtO0S17FB1+nSp5Nb4xS9+kS9+8Yv5+te/vtX3bW9fl0qle1uP9KJ1dHSmqakxK1eurfUoxero6EwSj9EWjBw5xONDVewQ1bJDVMsOUS071Lv6+rotnsjq9VLJlpaWLF++PF1dXUmSrq6urFixIi0tLc87duHChfnoRz+a2bNnZ9y4cVWMDQAAwHN6Dbfm5uaMHz8+8+fPT5LMnz8/48ePf95lkvfdd18++MEP5ktf+lL233//7TMtAADADqhP/47bRRddlDlz5mTixImZM2dOZs6cmSSZNm1a7r///iTJzJkz8/TTT2f69OmZPHlyJk+enN///vfbb3IAAIAdRJ9e47bXXntl7ty5z7v92muv7fnxf/zHf2y7qQAAAOjRpzNuAAAA1I5wAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKJxwAwAAKFxjrQdg63RVuvPMxq5aj9Gj0t2dJNnwTGeNJynXqjVPe3yoih2iWnaIatkhqlXaDg0c0JCG+rpaj7FVhFs/88zGrjzy2Npaj9Hj6Y5nI7KkmUoz7OmurF69odZj0I/ZIaplh6iWHaJape3QHmOGZPDA/pVCLpUEAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAonHADAAAoXGOtBwB2XJVKdxa3rRWArG4AAA1uSURBVMmKJ57KqOGDMq5laOrr62o9FgBAcYQbUBOVSne+e+cf09a+IRs7KxnQWJ+W5sE59ei9xRsAwF9xqSRQE4vb1vREW5Js7KykrX1DFretqfFkAADlEW5ATax44qmeaHvOxs5KVjzxVI0mAgAol3ADamLU8EEZ0Ljpt6ABjfUZNXxQjSYCACiXcANqYlzL0LQ0D05318Z0d3f3vMZtXMvQWo8GAFAc4QbURH19XU49eu90PPqjdK74VSYdvqc3JgEAeAHCDaiZ+vq6VNY+ks6VC7P3rq8UbQAAL0C4AQAAFE64AQAAFE64AQAAFE64AQAAFE64AQAAFE64AQAAFE64AQAAFE64AQAAFK5P4bZkyZJMmTIlEydOzJQpU/Lwww8/75iurq7MnDkzxx57bN785jdn7ty523pWAACAHVKfwm3GjBmZOnVqbrvttkydOjXTp09/3jE333xzHnnkkSxYsCD//u//niuuuCKPPvroNh8YAADoHyqV7vzxz0/mR/cszR///GQqle5aj9Rv1XV3d2/x0Wtvb8/EiRNz9913p6GhIV1dXTnssMOyYMGCjBgxoue497znPTnllFNy3HHHJUkuvvjijB07NmeddVafh7ns6z/PE2ueeZFfyrb3+9//LnX1dfnbffar9Sg9urq780xHV63H6PHwkj8kSfb8f/62xpOUq7GxPp2dlVqPUSw71Ds7RLXsENWyQ7wY3d3dWbn66XRs7Ep3krokTQMaMnLYTqmrq6vpbAObGtJQ4xn+2vChA/P/nvn6F/z5xt4+QVtbW0aPHp2GhoYkSUNDQ0aNGpW2trZNwq2trS1jx47t+bilpSWPPfbYVg3bOKAhA5oatuo+29OrXv2qWo/wPHWdlXQ1lvN/KvYuKGpL9NAff58k2WvvfWs8Sbns0JbZoS3z+PTOY7RlHp8t6+7uzuIlf0rqB6RlzKgMGthY8yfcpbFDL2zD0xvT0flstCVJd5KOzq5s7Kpk8E4DajlaGhvq09hY1tt9NA7Ycgf1Gm4vpbPeOr6406cjRw7JypVraz1Gjw3PdOaRx8qZhy2bueBzaWxsyD+++621HoV+yg69sEqlO5f9bG7qd2rOwXsfkXEtQ1Nf7wnlX7NDWzZzweeSxOOzGZVKd7575x9TN2BIUt+YVWueSUtzQ049ei9/1v6X70Nb9t+/eSzLVz21yW3d3ck+uw7L4a8aU6OpnrXHmCEZPLCoFOp1d3rNzJaWlixfvjxdXc9entfV1ZUVK1akpaXlecctW7as5+O2traMGVPb3xAAXp6ee0LZtNuENI56bW7+74fz3Tv/WNz//IP+bHHbmrS1b0hdw4DU1dVlY2clbe0bsrhtTa1HK4LvQ70bNXxQBvzVWa0BjfUZNXxQjSbq33oNt+bm5owfPz7z589PksyfPz/jx4/f5DLJJDnuuOMyd+7cVCqVrFq1KrfffnsmTpy4faYGYIfmCSXbQqXSnfohe6Rx5MHeNGEzVjzxVDb+1evaNnZWsuKJp17gHjsW34d6N65laFqaB/fE24DG+rQ0D864lqE1nqx/6tP5wYsuuigXXHBBrrrqqgwdOjSzZs1KkkybNi3nnXdeXv3qV2fy5Mm5995785a3vCVJcs4552T33XfffpMDsMPa0hPKvXd9ZY2moj/5y7MlqW/Mzf/9cFqaB+fUo/d2qdv/eu5syV/+WXO25P/4PtS7+vq6nHr03lnctiZrnu7M0J0aXU5ahT6F21577bXZf5ft2muv7flxQ0NDZs6cue0mA9jBPXc2oH7wLvnjn5/0l91f8ISSav3l2ZIkm5wt8aT7Wc+dLWlr35CNnRVnS/6K70N9U19fl713fWWGDRuc1as31Hqcfq2sV+QBkMTZgN54Qtk34v+FOVvSO2dLtsz3IV5qwg2gQM4GbNlfPqFc8cRTGTV8kCeUf0X8b5mzJX3jbMkL832Il5pwAyiQswG9e+4Jpcdj88T/ljlbwrbg+xAvJeEGUCBnA6iW+N8yZ0uA/ka4ARTI2QCqJf5752wJ0J8IN4ACeVMAqiX+AV5ehBtAobwpANUQ/wAvL8INAF6mxD/Ay0d9rQcAAABgy4QbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbAABA4YQbbCeVSnfqh+yR+uYD88c/P5lKpbvWIwEA0E811noAeDmqVLrz3Tv/mKbdJiT1jbn5vx9OS/PgnHr03qmvr6v1eAAA9DPOuMF2sLhtTdraN6SuYUDq6uqysbOStvYNWdy2ptajAQDQDwk32A5WPPFUNnZWNrltY2clK554qkYTAQDQnwk32A5GDR+UAY2b/vEa0FifUcMH1WgiAAD6M+EG28G4lqFpaR7cE28DGuvT0jw441qG1ngyAAD6I29OAttBfX1dTj167yxuW5M1T3dm6E6NGdcy1BuTAADwogg32E7q6+uy966vzLBhg7N69YZajwMAQD/mUkkAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCCTcAAIDCNdZ6ALbOwAEN2WPMkFqPwVYYMnRQhu7UUOsx6MfsENWyQ1TLDlGt0nZo4IByZukr4dbPNNTXZfBAv239yYihO6XrmY21HoN+zA5RLTtEtewQ1bJD1XOpJAAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOGEGwAAQOEaaz3AX6qvr6v1CJtV6lz0H3aIatkhqmWHqJYdolp2aMt6e3zquru7u1+iWQAAAHgRXCoJAABQOOEGAABQOOEGAABQOOEGAABQOOEGAABQOOEGAABQOOEGAABQOOEGAABQOOEGAABQOOH2ApYsWZIpU6Zk4sSJmTJlSh5++OFaj0Q/8sQTT2TatGmZOHFiJk2alPe///1ZtWpVrcein7ryyiuz77775g9/+EOtR6GfeeaZZzJjxoy85S1vyaRJk/LJT36y1iPRz9xxxx056aSTMnny5Jx44olZsGBBrUeicLNmzcqECROe9/eW59bVE24vYMaMGZk6dWpuu+22TJ06NdOnT6/1SPQjdXV1Oeuss3Lbbbfl5ptvzu67757PfvaztR6Lfui3v/1tfv3rX2fXXXet9Sj0Q//2b/+WgQMH9nwvOv/882s9Ev1Id3d3Pvaxj+Uzn/lMbrzxxnzmM5/Jxz/+8VQqlVqPRsGOOeaYfPvb337e31ueW1dPuG1Ge3t7Fi1alNbW1iRJa2trFi1a5IwJfTZs2LAcdthhPR8fdNBBWbZsWQ0noj/q6OjIxRdfnIsuuqjWo9APrV+/PvPmzcv555+furq6JMkuu+xS46nob+rr67N27dokydq1azNq1KjU13v6yAs75JBD0tLSssltnltvG421HqBEbW1tGT16dBoaGpIkDQ0NGTVqVNra2jJixIgaT0d/U6lU8p3vfCcTJkyo9Sj0M1/84hdz4oknZrfddqv1KPRDS5cuzbBhw3LllVfm7rvvzite8Yqcf/75OeSQQ2o9Gv1EXV1dvvCFL+Tss8/O4MGDs379+nzlK1+p9Vj0Q55bbxv+lwlsZ5dcckkGDx6c008/vdaj0I8sXLgwv/nNbzJ16tRaj0I/1dXVlaVLl+bv/u7vcv311+cjH/lIzj333Kxbt67Wo9FPdHZ25stf/nKuuuqq3HHHHbn66qvzgQ98IOvXr6/1aLBDEm6b0dLSkuXLl6erqyvJs3/5rVix4nmnfaE3s2bNyp/+9Kd84QtfcGkJW+WXv/xlHnrooRxzzDGZMGFCHnvssbz73e/OXXfdVevR6CdaWlrS2NjYc2nSgQcemOHDh2fJkiU1noz+4ne/+11WrFiR1772tUmS1772tRk0aFAeeuihGk9Gf+O59bbhmeRmNDc3Z/z48Zk/f36SZP78+Rk/frxTuWyVz3/+8/nNb36T2bNnp6mpqdbj0M+85z3vyV133ZUf/ehH+dGPfpQxY8bka1/7Wo488shaj0Y/MWLEiBx22GH56U9/muTZd3Rrb2/P3/zN39R4MvqLMWPG5LHHHsvixYuTJA899FDa29uzxx571Hgy+hvPrbeNuu7u7u5aD1Gihx56KBdccEHWrFmToUOHZtasWRk3blytx6KfePDBB9Pa2po999wzO+20U5Jkt912y+zZs2s8Gf3VhAkTcs011+Rv//Zvaz0K/cjSpUtz4YUXZvXq1WlsbMwHPvCBvOlNb6r1WPQjN910U6699tqeN7g577zzcuyxx9Z4Kkp26aWXZsGCBXn88cczfPjwDBs2LN///vc9t94GhBsAAEDhXCoJAABQOOEGAABQOOEGAABQOOEGAABQOOEGAABQOOEGAABQOOEGAABQOOEGAABQuP8fwvr3zIc8dYQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "df_16_22=df[(df['Year']>=2016)&(df['Year']<=2022)]\n",
        "t2=df_16_22.groupby(by=['category'])['total_funding_usd'].describe()"
      ],
      "metadata": {
        "id": "KfvCTtmka3pH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_u5Qmg5oWjuQ",
        "outputId": "558613d4-ed6a-4dd3-9cac-68ac842063a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         count          mean           std        min  \\\n",
              "category                                                                \n",
              "administrative services  59.00   43308772.71  130790960.14       0.00   \n",
              "advertising              22.00    1164845.91    3151590.06       0.00   \n",
              "agriculture and farming  20.00   70260357.20   77734047.99       0.00   \n",
              "apps                    123.00   48922258.98  342357974.36       0.00   \n",
              "artificial intelligence  45.00   38212133.27  166234954.52       0.00   \n",
              "biotechnology             4.00     500000.00     577350.27       0.00   \n",
              "clothing and apparel     18.00    3945333.33   12006705.32       0.00   \n",
              "commerce and shopping   234.00   22784667.74   96534446.87       0.00   \n",
              "community and lifestyle  36.00    9703560.06   16743203.97       0.00   \n",
              "consumer electronics     12.00    5225166.67    5483943.95       0.00   \n",
              "consumer goods            5.00     480000.00     268328.16       0.00   \n",
              "content and publishing    8.00   37778515.38   69265494.74       0.00   \n",
              "data and analytics       17.00    1305954.47    1963305.20       0.00   \n",
              "design                    6.00    1625000.00    1653405.58  950000.00   \n",
              "education                51.00    3739392.71   10899436.87       0.00   \n",
              "energy                   20.00 3674051722.30 7941063342.63    1089.00   \n",
              "events                   16.00    2325164.12    2093665.10  100000.00   \n",
              "financial services      213.00   45876906.47  204904580.44       0.00   \n",
              "food and beverage        22.00    9009595.50   15574515.05       0.00   \n",
              "gaming                    2.00     329765.00     452880.68    9530.00   \n",
              "government and military   2.00  250000000.00  353553390.59       0.00   \n",
              "hardware                 11.00     184644.45     602152.13       0.00   \n",
              "health care              36.00   43544744.22   94766798.18       0.00   \n",
              "information technology   69.00    2377948.88    5150724.65       0.00   \n",
              "internet services        15.00    2211213.93    4078431.12       0.00   \n",
              "manufacturing             6.00   60425000.00  130965223.44       0.00   \n",
              "media and entertainment  14.00    1023977.00    2887795.29       0.00   \n",
              "mobile                    2.00          0.00          0.00       0.00   \n",
              "other                    43.00    2387315.74    7832849.42       0.00   \n",
              "professional services    10.00  308757000.00  398437908.48   10000.00   \n",
              "real estate              16.00  107497002.44  182791359.54       0.00   \n",
              "sales and marketing       4.00     402500.00     235000.00   50000.00   \n",
              "science and engineering   1.00    1615368.00           NaN 1615368.00   \n",
              "software                 16.00    3917639.06    7223928.12       0.00   \n",
              "sports                    3.00          0.00          0.00       0.00   \n",
              "sustainability            3.00          0.00          0.00       0.00   \n",
              "transportation           35.00  105910802.17  129298730.89       0.00   \n",
              "travel and tourism       10.00    2746967.40    2111712.22       0.00   \n",
              "\n",
              "                                 25%          50%           75%            max  \n",
              "category                                                                        \n",
              "administrative services    125000.00   1000000.00    2500000.00   504000000.00  \n",
              "advertising                 30000.00    111500.00    1100000.00    15000000.00  \n",
              "agriculture and farming         0.00   9500000.00  153947340.00   153947340.00  \n",
              "apps                       265897.50   1500000.00    5550000.00  2700000000.00  \n",
              "artificial intelligence         0.00    571780.00    2600000.00   800000000.00  \n",
              "biotechnology                   0.00    500000.00    1000000.00     1000000.00  \n",
              "clothing and apparel            0.00    108000.00    1000000.00    50000000.00  \n",
              "commerce and shopping       81239.00   1350000.00    9600000.00   920000000.00  \n",
              "community and lifestyle     51114.50   1500000.00    2625000.00    42000000.00  \n",
              "consumer electronics       156250.00   5525000.00    8050000.00    18000000.00  \n",
              "consumer goods             600000.00    600000.00     600000.00      600000.00  \n",
              "content and publishing      10211.25    689754.00   38126250.00   150000000.00  \n",
              "data and analytics          23613.00    253000.00    1350000.00     5090000.00  \n",
              "design                     950000.00    950000.00     950000.00     5000000.00  \n",
              "education                   22000.00    500000.00    2059272.00    73000000.00  \n",
              "energy                    2485087.00  54961362.50 1265991742.00 22000000000.00  \n",
              "events                     495953.25   2625000.00    3318750.00     5400000.00  \n",
              "financial services         250000.00   2590000.00   10872500.00  1590000000.00  \n",
              "food and beverage               0.00    270000.00   12000000.00    45192203.00  \n",
              "gaming                     169647.50    329765.00     489882.50      650000.00  \n",
              "government and military 125000000.00 250000000.00  375000000.00   500000000.00  \n",
              "hardware                        0.00         0.00        544.50     2000000.00  \n",
              "health care                104306.25   2520000.00   71498880.00   400756945.00  \n",
              "information technology          0.00    200000.00    2750000.00    34250000.00  \n",
              "internet services               0.00    510000.00    2000000.00    12028209.00  \n",
              "manufacturing               12500.00   2925000.00   23950000.00   326700000.00  \n",
              "media and entertainment         0.00    232613.00     468806.50    11000000.00  \n",
              "mobile                          0.00         0.00          0.00           0.00  \n",
              "other                         500.00    100000.00    1460000.00    50000000.00  \n",
              "professional services       62500.00    300000.00  771700000.00   771700000.00  \n",
              "real estate                 10000.00   2500000.00  146996927.25   544493416.00  \n",
              "sales and marketing        402500.00    520000.00     520000.00      520000.00  \n",
              "science and engineering   1615368.00   1615368.00    1615368.00     1615368.00  \n",
              "software                    10209.00    450000.00    2000000.00    18400000.00  \n",
              "sports                          0.00         0.00          0.00           0.00  \n",
              "sustainability                  0.00         0.00          0.00           0.00  \n",
              "transportation            1500000.00  10200000.00  270350000.00   270350000.00  \n",
              "travel and tourism         497183.50   3500000.00    4625000.00     5000000.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b1b3d528-210e-439b-b1d0-612cd0dc126b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>administrative services</th>\n",
              "      <td>59.00</td>\n",
              "      <td>43308772.71</td>\n",
              "      <td>130790960.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>125000.00</td>\n",
              "      <td>1000000.00</td>\n",
              "      <td>2500000.00</td>\n",
              "      <td>504000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>advertising</th>\n",
              "      <td>22.00</td>\n",
              "      <td>1164845.91</td>\n",
              "      <td>3151590.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>30000.00</td>\n",
              "      <td>111500.00</td>\n",
              "      <td>1100000.00</td>\n",
              "      <td>15000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agriculture and farming</th>\n",
              "      <td>20.00</td>\n",
              "      <td>70260357.20</td>\n",
              "      <td>77734047.99</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9500000.00</td>\n",
              "      <td>153947340.00</td>\n",
              "      <td>153947340.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>apps</th>\n",
              "      <td>123.00</td>\n",
              "      <td>48922258.98</td>\n",
              "      <td>342357974.36</td>\n",
              "      <td>0.00</td>\n",
              "      <td>265897.50</td>\n",
              "      <td>1500000.00</td>\n",
              "      <td>5550000.00</td>\n",
              "      <td>2700000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>artificial intelligence</th>\n",
              "      <td>45.00</td>\n",
              "      <td>38212133.27</td>\n",
              "      <td>166234954.52</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>571780.00</td>\n",
              "      <td>2600000.00</td>\n",
              "      <td>800000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>biotechnology</th>\n",
              "      <td>4.00</td>\n",
              "      <td>500000.00</td>\n",
              "      <td>577350.27</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>500000.00</td>\n",
              "      <td>1000000.00</td>\n",
              "      <td>1000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clothing and apparel</th>\n",
              "      <td>18.00</td>\n",
              "      <td>3945333.33</td>\n",
              "      <td>12006705.32</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>108000.00</td>\n",
              "      <td>1000000.00</td>\n",
              "      <td>50000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>commerce and shopping</th>\n",
              "      <td>234.00</td>\n",
              "      <td>22784667.74</td>\n",
              "      <td>96534446.87</td>\n",
              "      <td>0.00</td>\n",
              "      <td>81239.00</td>\n",
              "      <td>1350000.00</td>\n",
              "      <td>9600000.00</td>\n",
              "      <td>920000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>community and lifestyle</th>\n",
              "      <td>36.00</td>\n",
              "      <td>9703560.06</td>\n",
              "      <td>16743203.97</td>\n",
              "      <td>0.00</td>\n",
              "      <td>51114.50</td>\n",
              "      <td>1500000.00</td>\n",
              "      <td>2625000.00</td>\n",
              "      <td>42000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>consumer electronics</th>\n",
              "      <td>12.00</td>\n",
              "      <td>5225166.67</td>\n",
              "      <td>5483943.95</td>\n",
              "      <td>0.00</td>\n",
              "      <td>156250.00</td>\n",
              "      <td>5525000.00</td>\n",
              "      <td>8050000.00</td>\n",
              "      <td>18000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>consumer goods</th>\n",
              "      <td>5.00</td>\n",
              "      <td>480000.00</td>\n",
              "      <td>268328.16</td>\n",
              "      <td>0.00</td>\n",
              "      <td>600000.00</td>\n",
              "      <td>600000.00</td>\n",
              "      <td>600000.00</td>\n",
              "      <td>600000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>content and publishing</th>\n",
              "      <td>8.00</td>\n",
              "      <td>37778515.38</td>\n",
              "      <td>69265494.74</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10211.25</td>\n",
              "      <td>689754.00</td>\n",
              "      <td>38126250.00</td>\n",
              "      <td>150000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data and analytics</th>\n",
              "      <td>17.00</td>\n",
              "      <td>1305954.47</td>\n",
              "      <td>1963305.20</td>\n",
              "      <td>0.00</td>\n",
              "      <td>23613.00</td>\n",
              "      <td>253000.00</td>\n",
              "      <td>1350000.00</td>\n",
              "      <td>5090000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>design</th>\n",
              "      <td>6.00</td>\n",
              "      <td>1625000.00</td>\n",
              "      <td>1653405.58</td>\n",
              "      <td>950000.00</td>\n",
              "      <td>950000.00</td>\n",
              "      <td>950000.00</td>\n",
              "      <td>950000.00</td>\n",
              "      <td>5000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <td>51.00</td>\n",
              "      <td>3739392.71</td>\n",
              "      <td>10899436.87</td>\n",
              "      <td>0.00</td>\n",
              "      <td>22000.00</td>\n",
              "      <td>500000.00</td>\n",
              "      <td>2059272.00</td>\n",
              "      <td>73000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>energy</th>\n",
              "      <td>20.00</td>\n",
              "      <td>3674051722.30</td>\n",
              "      <td>7941063342.63</td>\n",
              "      <td>1089.00</td>\n",
              "      <td>2485087.00</td>\n",
              "      <td>54961362.50</td>\n",
              "      <td>1265991742.00</td>\n",
              "      <td>22000000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>events</th>\n",
              "      <td>16.00</td>\n",
              "      <td>2325164.12</td>\n",
              "      <td>2093665.10</td>\n",
              "      <td>100000.00</td>\n",
              "      <td>495953.25</td>\n",
              "      <td>2625000.00</td>\n",
              "      <td>3318750.00</td>\n",
              "      <td>5400000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>financial services</th>\n",
              "      <td>213.00</td>\n",
              "      <td>45876906.47</td>\n",
              "      <td>204904580.44</td>\n",
              "      <td>0.00</td>\n",
              "      <td>250000.00</td>\n",
              "      <td>2590000.00</td>\n",
              "      <td>10872500.00</td>\n",
              "      <td>1590000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>food and beverage</th>\n",
              "      <td>22.00</td>\n",
              "      <td>9009595.50</td>\n",
              "      <td>15574515.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>270000.00</td>\n",
              "      <td>12000000.00</td>\n",
              "      <td>45192203.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gaming</th>\n",
              "      <td>2.00</td>\n",
              "      <td>329765.00</td>\n",
              "      <td>452880.68</td>\n",
              "      <td>9530.00</td>\n",
              "      <td>169647.50</td>\n",
              "      <td>329765.00</td>\n",
              "      <td>489882.50</td>\n",
              "      <td>650000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>government and military</th>\n",
              "      <td>2.00</td>\n",
              "      <td>250000000.00</td>\n",
              "      <td>353553390.59</td>\n",
              "      <td>0.00</td>\n",
              "      <td>125000000.00</td>\n",
              "      <td>250000000.00</td>\n",
              "      <td>375000000.00</td>\n",
              "      <td>500000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hardware</th>\n",
              "      <td>11.00</td>\n",
              "      <td>184644.45</td>\n",
              "      <td>602152.13</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>544.50</td>\n",
              "      <td>2000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>health care</th>\n",
              "      <td>36.00</td>\n",
              "      <td>43544744.22</td>\n",
              "      <td>94766798.18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>104306.25</td>\n",
              "      <td>2520000.00</td>\n",
              "      <td>71498880.00</td>\n",
              "      <td>400756945.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>information technology</th>\n",
              "      <td>69.00</td>\n",
              "      <td>2377948.88</td>\n",
              "      <td>5150724.65</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>200000.00</td>\n",
              "      <td>2750000.00</td>\n",
              "      <td>34250000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>internet services</th>\n",
              "      <td>15.00</td>\n",
              "      <td>2211213.93</td>\n",
              "      <td>4078431.12</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>510000.00</td>\n",
              "      <td>2000000.00</td>\n",
              "      <td>12028209.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturing</th>\n",
              "      <td>6.00</td>\n",
              "      <td>60425000.00</td>\n",
              "      <td>130965223.44</td>\n",
              "      <td>0.00</td>\n",
              "      <td>12500.00</td>\n",
              "      <td>2925000.00</td>\n",
              "      <td>23950000.00</td>\n",
              "      <td>326700000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>media and entertainment</th>\n",
              "      <td>14.00</td>\n",
              "      <td>1023977.00</td>\n",
              "      <td>2887795.29</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>232613.00</td>\n",
              "      <td>468806.50</td>\n",
              "      <td>11000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mobile</th>\n",
              "      <td>2.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>other</th>\n",
              "      <td>43.00</td>\n",
              "      <td>2387315.74</td>\n",
              "      <td>7832849.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>500.00</td>\n",
              "      <td>100000.00</td>\n",
              "      <td>1460000.00</td>\n",
              "      <td>50000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>professional services</th>\n",
              "      <td>10.00</td>\n",
              "      <td>308757000.00</td>\n",
              "      <td>398437908.48</td>\n",
              "      <td>10000.00</td>\n",
              "      <td>62500.00</td>\n",
              "      <td>300000.00</td>\n",
              "      <td>771700000.00</td>\n",
              "      <td>771700000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>real estate</th>\n",
              "      <td>16.00</td>\n",
              "      <td>107497002.44</td>\n",
              "      <td>182791359.54</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10000.00</td>\n",
              "      <td>2500000.00</td>\n",
              "      <td>146996927.25</td>\n",
              "      <td>544493416.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sales and marketing</th>\n",
              "      <td>4.00</td>\n",
              "      <td>402500.00</td>\n",
              "      <td>235000.00</td>\n",
              "      <td>50000.00</td>\n",
              "      <td>402500.00</td>\n",
              "      <td>520000.00</td>\n",
              "      <td>520000.00</td>\n",
              "      <td>520000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>science and engineering</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1615368.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1615368.00</td>\n",
              "      <td>1615368.00</td>\n",
              "      <td>1615368.00</td>\n",
              "      <td>1615368.00</td>\n",
              "      <td>1615368.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>software</th>\n",
              "      <td>16.00</td>\n",
              "      <td>3917639.06</td>\n",
              "      <td>7223928.12</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10209.00</td>\n",
              "      <td>450000.00</td>\n",
              "      <td>2000000.00</td>\n",
              "      <td>18400000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sports</th>\n",
              "      <td>3.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sustainability</th>\n",
              "      <td>3.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transportation</th>\n",
              "      <td>35.00</td>\n",
              "      <td>105910802.17</td>\n",
              "      <td>129298730.89</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1500000.00</td>\n",
              "      <td>10200000.00</td>\n",
              "      <td>270350000.00</td>\n",
              "      <td>270350000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>travel and tourism</th>\n",
              "      <td>10.00</td>\n",
              "      <td>2746967.40</td>\n",
              "      <td>2111712.22</td>\n",
              "      <td>0.00</td>\n",
              "      <td>497183.50</td>\n",
              "      <td>3500000.00</td>\n",
              "      <td>4625000.00</td>\n",
              "      <td>5000000.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1b3d528-210e-439b-b1d0-612cd0dc126b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b1b3d528-210e-439b-b1d0-612cd0dc126b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UK2p63kdiU3j",
        "outputId": "98962e5d-a1b0-4827-a6dd-c9ca9192f506"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['roles', 'total_funding_usd', 'employee_count', 'rank', 'city',\n",
              "       'investment_type', 'announced_on', 'raised_amount_usd',\n",
              "       'post_money_valuation_usd', 'investor_count', 'org_uuid', 'org_name',\n",
              "       'lead_investor_uuids', 'round', 'Year', 'category', 'age',\n",
              "       'Year_Category'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df[['raised_amount_usd','post_money_valuation_usd','employee_count','investor_count','age','Year']]"
      ],
      "metadata": {
        "id": "K0hky1ypgPG5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "c=['raised_amount_usd','post_money_valuation_usd','investor_count']\n",
        "for c in c:\n",
        "  df1[c].fillna(df1[c].mean(),inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qygncaohWDL",
        "outputId": "9e2dd514-1765-4c3f-f6a4-557d9872a410"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return self._update_inplace(result)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vif_data = pd.DataFrame()\n",
        "vif_data[\"feature\"] =df1.columns\n",
        "# calculating VIF for each feature\n",
        "vif_data[\"VIF\"] = [variance_inflation_factor(df1.values, i) for i in range(len(df1.columns))]\n",
        "vif_data[\"mean\"]=vif_data[\"feature\"].apply(lambda x: np.mean(df1[x]))\n",
        "vif_data[\"S.d\"]=vif_data[\"feature\"].apply(lambda x: np.std(df1[x]))\n",
        "vif_data[\"50th quantile\"]=vif_data[\"feature\"].apply(lambda x: np.quantile(df1[x], .50))\n",
        "vif_data[\"25th quantile\"]=vif_data[\"feature\"].apply(lambda x: np.quantile(df1[x], .25))\n",
        "vif_data[\"75th quantile\"]=vif_data[\"feature\"].apply(lambda x: np.quantile(df1[x], .75))\n",
        "vif_data[\"max\"]=vif_data[\"feature\"].apply(lambda x: np.max(df1[x]))\n",
        "vif_data[\"min\"]=vif_data[\"feature\"].apply(lambda x: np.min(df1[x]))"
      ],
      "metadata": {
        "id": "G5zolab9deIb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vif_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "RE955slVi9HC",
        "outputId": "ddb3567c-18a3-4fed-f395-8317804493d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    feature  VIF        mean          S.d  50th quantile  \\\n",
              "0         raised_amount_usd 2.21 48575802.10 371108493.71     3000000.00   \n",
              "1  post_money_valuation_usd 1.91 25381960.22 551314066.00           0.00   \n",
              "2            employee_count 1.45      276.62      1234.37          39.00   \n",
              "3            investor_count 2.66        2.03         1.61           2.00   \n",
              "4                       age 2.40        7.35         7.95           6.00   \n",
              "5                      Year 3.73     2018.04         3.00        2018.00   \n",
              "\n",
              "   25th quantile  75th quantile            max     min  \n",
              "0      400000.00    48575802.10 10000000000.00 1000.00  \n",
              "1           0.00           0.00 20408000000.00    0.00  \n",
              "2           9.00          39.00       10000.00    0.00  \n",
              "3           1.00           2.03          16.00    1.00  \n",
              "4           4.00           9.00         132.00    0.00  \n",
              "5        2017.00        2020.00        2022.00 1999.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ee7a89e4-c00a-47c0-9238-21c7286aaa67\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>VIF</th>\n",
              "      <th>mean</th>\n",
              "      <th>S.d</th>\n",
              "      <th>50th quantile</th>\n",
              "      <th>25th quantile</th>\n",
              "      <th>75th quantile</th>\n",
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              "      <th>min</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>raised_amount_usd</td>\n",
              "      <td>2.21</td>\n",
              "      <td>48575802.10</td>\n",
              "      <td>371108493.71</td>\n",
              "      <td>3000000.00</td>\n",
              "      <td>400000.00</td>\n",
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              "      <td>10000000000.00</td>\n",
              "      <td>1000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>post_money_valuation_usd</td>\n",
              "      <td>1.91</td>\n",
              "      <td>25381960.22</td>\n",
              "      <td>551314066.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>20408000000.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>employee_count</td>\n",
              "      <td>1.45</td>\n",
              "      <td>276.62</td>\n",
              "      <td>1234.37</td>\n",
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              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>investor_count</td>\n",
              "      <td>2.66</td>\n",
              "      <td>2.03</td>\n",
              "      <td>1.61</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.03</td>\n",
              "      <td>16.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>age</td>\n",
              "      <td>2.40</td>\n",
              "      <td>7.35</td>\n",
              "      <td>7.95</td>\n",
              "      <td>6.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>132.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Year</td>\n",
              "      <td>3.73</td>\n",
              "      <td>2018.04</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2018.00</td>\n",
              "      <td>2017.00</td>\n",
              "      <td>2020.00</td>\n",
              "      <td>2022.00</td>\n",
              "      <td>1999.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ee7a89e4-c00a-47c0-9238-21c7286aaa67');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "r,p=stats.pearsonr(df1['raised_amount_usd'],df1['post_money_valuation_usd'])"
      ],
      "metadata": {
        "id": "8CqOxYT881QJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r1,p1=stats.pearsonr(df1['raised_amount_usd'],df1['employee_count'])"
      ],
      "metadata": {
        "id": "f1-HXNIkEy90"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2,p2=stats.pearsonr(df1['raised_amount_usd'],df1['investor_count'])"
      ],
      "metadata": {
        "id": "IgiQ7gJJI5Ed"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r3,p3=stats.pearsonr(df1['raised_amount_usd'],df1['age'])\n",
        "r4,p4=stats.pearsonr(df1['raised_amount_usd'],df1['Year'])"
      ],
      "metadata": {
        "id": "11IVhl_YJlEk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3XAWMQ7nLBr8",
        "outputId": "8aeee9c8-46cc-4aa6-9848-430b89fc00e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.30630923318241765"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vif_data['P value']=[0,1.9363683062428454e-205,2.9876162456841256e-53,2.7368258461032978e-05,9.126834911901075e-31,0.306]"
      ],
      "metadata": {
        "id": "3WpTKUEBJFqK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vif_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "m8rYhQN4KmuC",
        "outputId": "a1582ef2-dfb5-4899-c17f-f779baf4ea79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    feature  VIF        mean          S.d  50th quantile  \\\n",
              "0         raised_amount_usd 2.21 48575802.10 371108493.71     3000000.00   \n",
              "1  post_money_valuation_usd 1.91 25381960.22 551314066.00           0.00   \n",
              "2            employee_count 1.45      276.62      1234.37          39.00   \n",
              "3            investor_count 2.66        2.03         1.61           2.00   \n",
              "4                       age 2.40        7.35         7.95           6.00   \n",
              "5                      Year 3.73     2018.04         3.00        2018.00   \n",
              "\n",
              "   25th quantile  75th quantile            max     min  P value  \n",
              "0      400000.00    48575802.10 10000000000.00 1000.00     0.00  \n",
              "1           0.00           0.00 20408000000.00    0.00     0.00  \n",
              "2           9.00          39.00       10000.00    0.00     0.00  \n",
              "3           1.00           2.03          16.00    1.00     0.00  \n",
              "4           4.00           9.00         132.00    0.00     0.00  \n",
              "5        2017.00        2020.00        2022.00 1999.00     0.31  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>VIF</th>\n",
              "      <th>mean</th>\n",
              "      <th>S.d</th>\n",
              "      <th>50th quantile</th>\n",
              "      <th>25th quantile</th>\n",
              "      <th>75th quantile</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>P value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>raised_amount_usd</td>\n",
              "      <td>2.21</td>\n",
              "      <td>48575802.10</td>\n",
              "      <td>371108493.71</td>\n",
              "      <td>3000000.00</td>\n",
              "      <td>400000.00</td>\n",
              "      <td>48575802.10</td>\n",
              "      <td>10000000000.00</td>\n",
              "      <td>1000.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>post_money_valuation_usd</td>\n",
              "      <td>1.91</td>\n",
              "      <td>25381960.22</td>\n",
              "      <td>551314066.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>20408000000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>employee_count</td>\n",
              "      <td>1.45</td>\n",
              "      <td>276.62</td>\n",
              "      <td>1234.37</td>\n",
              "      <td>39.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>10000.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>investor_count</td>\n",
              "      <td>2.66</td>\n",
              "      <td>2.03</td>\n",
              "      <td>1.61</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.03</td>\n",
              "      <td>16.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>age</td>\n",
              "      <td>2.40</td>\n",
              "      <td>7.35</td>\n",
              "      <td>7.95</td>\n",
              "      <td>6.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>132.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Year</td>\n",
              "      <td>3.73</td>\n",
              "      <td>2018.04</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2018.00</td>\n",
              "      <td>2017.00</td>\n",
              "      <td>2020.00</td>\n",
              "      <td>2022.00</td>\n",
              "      <td>1999.00</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"p value between raised funding and valuation : {p3}\")\n",
        "print(\"-------------------------------------------------------\")\n",
        "print(f\"p value between raised funding and employee count : {p4}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWlJpSafEcUm",
        "outputId": "6e44ef2b-3dfb-4d76-930b-f9b2fb6db106"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "p value between raised funding and valuation : 9.126834911901075e-31\n",
            "-------------------------------------------------------\n",
            "p value between raised funding and employee count : 0.30630923318241765\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Data_analysis_Dubai_Entreupreneurship.ipynb",
      "provenance": [],
      "mount_file_id": "10YXudT7VVTiAoAFAKKknsPb29c4lpXdK",
      "authorship_tag": "ABX9TyNzzuGI6oe2HiO0XgSTxFvk",
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      "name": "python"
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}