My_point.ipynb

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              "                                 Unnamed: 0 Unnamed: 1        Unnamed: 2  \\\n",
              "0                                       NaN        NaN               NaN   \n",
              "1                                    Fənlər     Kredit  İmtahandan əvvəl   \n",
              "2                    Sistem proqramlaşdırma          7                46   \n",
              "3                                       HPC          8                48   \n",
              "4                                   Fəlsəfə          3                49   \n",
              "5                       Web texnologiyaları          4                48   \n",
              "6  Ehtimal nəzəriyyəsi və riyazi statistika          4                35   \n",
              "7            Paralel paylanmış hesablamalar          4                35   \n",
              "\n",
              "         Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6  Unnamed: 7  \n",
              "0               NaN        NaN        NaN        NaN         NaN  \n",
              "1  İmtahandan sonra      Yekun     Hərflə   Ortalama  Stipendiya  \n",
              "2                45         91          A      21.23    Zerbechi  \n",
              "3                47         95          A      25.33    Zerbechi  \n",
              "4                41         90          B          9    Zerbechi  \n",
              "5                43         91          A      12.13    Zerbechi  \n",
              "6                39         74          C       9.87    Zerbechi  \n",
              "7                36         71          C       9.47    Zerbechi  "
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              "      <td>İmtahandan əvvəl</td>\n",
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              "      <td>4</td>\n",
              "      <td>48</td>\n",
              "      <td>43</td>\n",
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              "      <th>6</th>\n",
              "      <td>Ehtimal nəzəriyyəsi və riyazi statistika</td>\n",
              "      <td>4</td>\n",
              "      <td>35</td>\n",
              "      <td>39</td>\n",
              "      <td>74</td>\n",
              "      <td>C</td>\n",
              "      <td>9.87</td>\n",
              "      <td>Zerbechi</td>\n",
              "    </tr>\n",
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              "      <th>7</th>\n",
              "      <td>Paralel paylanmış hesablamalar</td>\n",
              "      <td>4</td>\n",
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              "      <td>71</td>\n",
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          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "import pandas as pd\n",
        "data=pd.read_excel('My_point.xlsx')\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dropna(subset='Unnamed: 0',inplace=True)"
      ],
      "metadata": {
        "id": "5UETxi5-agIX"
      },
      "execution_count": 6,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "data.info()"
      ],
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        },
        "id": "jRpufNfARoSx",
        "outputId": "26a5c22f-1a11-4f7d-ab61-2b2bbde344bc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 7 entries, 1 to 7\n",
            "Data columns (total 8 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Unnamed: 0  7 non-null      object\n",
            " 1   Unnamed: 1  7 non-null      object\n",
            " 2   Unnamed: 2  7 non-null      object\n",
            " 3   Unnamed: 3  7 non-null      object\n",
            " 4   Unnamed: 4  7 non-null      object\n",
            " 5   Unnamed: 5  7 non-null      object\n",
            " 6   Unnamed: 6  7 non-null      object\n",
            " 7   Unnamed: 7  7 non-null      object\n",
            "dtypes: object(8)\n",
            "memory usage: 504.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
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        "id": "sm4zitZjZxOR",
        "outputId": "c892cccc-4996-4fd8-9d8e-9685f002565a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Unnamed: 0 Unnamed: 1        Unnamed: 2  \\\n",
              "1                                    Fənlər     Kredit  İmtahandan əvvəl   \n",
              "2                    Sistem proqramlaşdırma          7                46   \n",
              "3                                       HPC          8                48   \n",
              "4                                   Fəlsəfə          3                49   \n",
              "5                       Web texnologiyaları          4                48   \n",
              "6  Ehtimal nəzəriyyəsi və riyazi statistika          4                35   \n",
              "7            Paralel paylanmış hesablamalar          4                35   \n",
              "\n",
              "         Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6  Unnamed: 7  \n",
              "1  İmtahandan sonra      Yekun     Hərflə   Ortalama  Stipendiya  \n",
              "2                45         91          A      21.23    Zerbechi  \n",
              "3                47         95          A      25.33    Zerbechi  \n",
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        "data=data[1:]\n",
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          "data": {
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              "1                                    Fənlər Kredit İmtahandan əvvəl  \\\n",
              "2                    Sistem proqramlaşdırma      7               46   \n",
              "3                                       HPC      8               48   \n",
              "4                                   Fəlsəfə      3               49   \n",
              "5                       Web texnologiyaları      4               48   \n",
              "6  Ehtimal nəzəriyyəsi və riyazi statistika      4               35   \n",
              "7            Paralel paylanmış hesablamalar      4               35   \n",
              "\n",
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              "2               45    91      A    21.23   Zerbechi  \n",
              "3               47    95      A    25.33   Zerbechi  \n",
              "4               41    90      B        9   Zerbechi  \n",
              "5               43    91      A    12.13   Zerbechi  \n",
              "6               39    74      C     9.87   Zerbechi  \n",
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              "      <th>7</th>\n",
              "      <td>Paralel paylanmış hesablamalar</td>\n",
              "      <td>4</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>71</td>\n",
              "      <td>C</td>\n",
              "      <td>9.47</td>\n",
              "      <td>Zerbechi</td>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  .colab-df-spinner {\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
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              "\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yWZJlCP5b8M_",
        "outputId": "7c31e738-d5b2-4ca0-c653-9bbee7d57351"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 6 entries, 2 to 7\n",
            "Data columns (total 8 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   Fənlər            6 non-null      object\n",
            " 1   Kredit            6 non-null      object\n",
            " 2   İmtahandan əvvəl  6 non-null      object\n",
            " 3   İmtahandan sonra  6 non-null      object\n",
            " 4   Yekun             6 non-null      object\n",
            " 5   Hərflə            6 non-null      object\n",
            " 6   Ortalama          6 non-null      object\n",
            " 7   Stipendiya        6 non-null      object\n",
            "dtypes: object(8)\n",
            "memory usage: 432.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Kredit'].astype(int)\n",
        "data['İmtahandan əvvəl'].astype(int)\n",
        "data['İmtahandan sonra'].astype(int)\n",
        "data['Yekun'].astype(int)\n",
        "data['Ortalama'].astype(float)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKPXvKf8cICq",
        "outputId": "77f86e69-c53a-4810-945f-e2652da68e5d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    21.23\n",
              "3    25.33\n",
              "4     9.00\n",
              "5    12.13\n",
              "6     9.87\n",
              "7     9.47\n",
              "Name: Ortalama, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "EX2ZXdkMdD6Z",
        "outputId": "d27c6adf-0716-478c-dca0-1939d9947f92"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1                                    Fənlər Kredit İmtahandan əvvəl  \\\n",
              "2                    Sistem proqramlaşdırma      7               46   \n",
              "3                                       HPC      8               48   \n",
              "4                                   Fəlsəfə      3               49   \n",
              "5                       Web texnologiyaları      4               48   \n",
              "6  Ehtimal nəzəriyyəsi və riyazi statistika      4               35   \n",
              "7            Paralel paylanmış hesablamalar      4               35   \n",
              "\n",
              "1 İmtahandan sonra Yekun Hərflə Ortalama Stipendiya  \n",
              "2               45    91      A    21.23   Zerbechi  \n",
              "3               47    95      A    25.33   Zerbechi  \n",
              "4               41    90      B        9   Zerbechi  \n",
              "5               43    91      A    12.13   Zerbechi  \n",
              "6               39    74      C     9.87   Zerbechi  \n",
              "7               36    71      C     9.47   Zerbechi  "
            ],
            "text/html": [
              "\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ehtimal nəzəriyyəsi və riyazi statistika</td>\n",
              "      <td>4</td>\n",
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              "      <td>39</td>\n",
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              "    .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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-a528cd98-b0bf-4069-916e-dac1333c394c 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-a528cd98-b0bf-4069-916e-dac1333c394c');\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",
              "\n",
              "\n",
              "<div id=\"df-440e887c-deba-43d5-946d-647e863ed0c3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-440e887c-deba-43d5-946d-647e863ed0c3')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-440e887c-deba-43d5-946d-647e863ed0c3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_56a7d146-4552-4fc9-874a-a15fb54c5f86\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_56a7d146-4552-4fc9-874a-a15fb54c5f86 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YADHinkBdHlt",
        "outputId": "546e99e3-13dc-485c-e98f-f3a977ed6293"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 6 entries, 2 to 7\n",
            "Data columns (total 8 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   Fənlər            6 non-null      object\n",
            " 1   Kredit            6 non-null      object\n",
            " 2   İmtahandan əvvəl  6 non-null      object\n",
            " 3   İmtahandan sonra  6 non-null      object\n",
            " 4   Yekun             6 non-null      object\n",
            " 5   Hərflə            6 non-null      object\n",
            " 6   Ortalama          6 non-null      object\n",
            " 7   Stipendiya        6 non-null      object\n",
            "dtypes: object(8)\n",
            "memory usage: 432.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Ortalama'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgfWPLgBdL2T",
        "outputId": "c23158cf-1d6f-4f80-b83b-fc96b669ea5d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.505"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sea\n",
        "sea.barplot(data,x='İmtahandan əvvəl',y='İmtahandan sonra',color='hotpink',width=0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "HPVVCE5fdrSN",
        "outputId": "2a74e9b0-b3fc-4d7d-deec-e913a996fc65"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='İmtahandan əvvəl', ylabel='İmtahandan sonra'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "data.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "VtG7XH4PebhM",
        "outputId": "f47df601-bd7f-4fbf-b293-7e1a89ac08ed"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZsJEX51rfH_F"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}