15DataAnalysisWithPandas.ipynb

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      "name": "15DataAnalysisWithPandas.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_enGfiG98ak",
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      },
      "source": [
        "# 1) Pandas Series"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MsZSLWm9_vN",
        "colab_type": "text"
      },
      "source": [
        "## Create a Series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1c1IV7cP99Vf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hw8UxcEPLIwF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 1) creating pandas series from a list"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7I7-42nmLPr9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_data = [10,20,30,40,50]\n",
        "labels = ['a','b','c','d','e']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "27766724-4772-4af5-9d86-c25857ffbb42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "pd.Series(data=my_data)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    10\n",
              "1    20\n",
              "2    30\n",
              "3    40\n",
              "4    50\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o7pL-VyiMLOp",
        "colab_type": "code",
        "outputId": "b6eed892-92dc-4876-903e-939b188cbf2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "pd.Series(data=my_data, index=labels)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    10\n",
              "b    20\n",
              "c    30\n",
              "d    40\n",
              "e    50\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqivr3MFMdUe",
        "colab_type": "code",
        "outputId": "e5311c72-df7c-4711-bbb1-14c3760709f2",
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      },
      "source": [
        "pd.Series(my_data, labels)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    10\n",
              "b    20\n",
              "c    30\n",
              "d    40\n",
              "e    50\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJ1voxqLM27A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 2) Pandas series using numpy array"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcH5hDD1M-L_",
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        "outputId": "5b5d896f-1a1e-4ca3-ad4b-657a2565f0b5",
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        }
      },
      "source": [
        "arr = np.array(my_data)\n",
        "arr"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 20, 30, 40, 50])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZy_XScjNK1Z",
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        "outputId": "8e20f9c1-e848-45d9-86b4-f26d0bb0e3bb",
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          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "pd.Series(data=arr)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    10\n",
              "1    20\n",
              "2    30\n",
              "3    40\n",
              "4    50\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-M4_GHONQsE",
        "colab_type": "code",
        "outputId": "e819bc36-91c1-44d1-b763-fbac413e9f14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "pd.Series(data=arr,index=labels)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    10\n",
              "b    20\n",
              "c    30\n",
              "d    40\n",
              "e    50\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RtxMukONcrb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 3) pandas series using a dictionary"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7zczs5BNnb6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d = {'a':100,'b':200, 'c':300, 'd':400}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvX-78o9NyQz",
        "colab_type": "code",
        "outputId": "dd572bd7-3cc3-4d7e-a7fb-0463af4f2fbc",
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          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "d"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'a': 100, 'b': 200, 'c': 300, 'd': 400}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_RYwekZNzt-",
        "colab_type": "code",
        "outputId": "ae30e7ac-f8a0-40ee-cbd0-93de48e5081b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "pd.Series(d)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    100\n",
              "b    200\n",
              "c    300\n",
              "d    400\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEB1o_9tODCU",
        "colab_type": "code",
        "outputId": "2f370b4c-757b-4102-ca88-3227bbdbb7c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
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      },
      "source": [
        "pd.Series(data=labels)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    a\n",
              "1    b\n",
              "2    c\n",
              "3    d\n",
              "4    e\n",
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          "metadata": {
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          "execution_count": 15
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Ya7chaleObcQ",
        "colab_type": "code",
        "outputId": "beb9ce75-274f-4a19-973a-3c9f7b6de56d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "pd.Series(data=[sum, len])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    <built-in function sum>\n",
              "1    <built-in function len>\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JW3-JSAgPEMi",
        "colab_type": "text"
      },
      "source": [
        "## Grab Info from Pandas Series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GobmwCQPI6d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ser1 = pd.Series(data=[1,2,3,4], index=['Red','Green','Blue','Orange'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iP8U6zKPPpeU",
        "colab_type": "code",
        "outputId": "0c1b0d0f-5cff-4840-83ef-d9ca9771bd12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "ser1"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Red       1\n",
              "Green     2\n",
              "Blue      3\n",
              "Orange    4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emon9RG0Pr28",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ser2 = pd.Series(data=[1,2,5,4], index=['Red','Green','Yellow','Orange'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzyocxCHP1ff",
        "colab_type": "code",
        "outputId": "ed33c95a-5736-4d4e-e4ee-7057e7892fd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "ser2"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Red       1\n",
              "Green     2\n",
              "Yellow    5\n",
              "Orange    4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-NuqjKlP3jI",
        "colab_type": "code",
        "outputId": "8014ce09-b416-47e6-d4df-c21801592426",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "ser2['Green']"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJKSrmGOQDuX",
        "colab_type": "code",
        "outputId": "a8c8c040-2ec9-484f-e328-61b42bdc1e1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "ser1['Blue']"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLKrgW2HQXYE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ser3 = pd.Series(data=['a','b','c','d','e'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlfUkv-eQkR9",
        "colab_type": "code",
        "outputId": "f1ec7866-23b8-48ca-c717-1fb8b86fe2b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "ser3"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    a\n",
              "1    b\n",
              "2    c\n",
              "3    d\n",
              "4    e\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QawAoFoVQmfF",
        "colab_type": "code",
        "outputId": "3ee77c0f-10c5-4e41-f2c8-c9e995e7a90a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "ser3[1]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'b'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DKtLRPInQ1TH",
        "colab_type": "code",
        "outputId": "fa4332c3-43ac-45b5-b40c-25190adee8d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "ser3[0:3]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    a\n",
              "1    b\n",
              "2    c\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdLSph2aQ5HR",
        "colab_type": "code",
        "outputId": "8719dc70-1b63-4d64-e2c5-9e31def53351",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "ser1"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Red       1\n",
              "Green     2\n",
              "Blue      3\n",
              "Orange    4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dJZ5RUBiRECq",
        "colab_type": "code",
        "outputId": "12f84c48-b7e3-4e73-c7f3-3d7d173670ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "ser2"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Red       1\n",
              "Green     2\n",
              "Yellow    5\n",
              "Orange    4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XC3HvsBvRLyW",
        "colab_type": "code",
        "outputId": "2c50eb4c-b1f3-40a0-9e63-c006d1e40f4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "ser1 + ser2"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Blue      NaN\n",
              "Green     4.0\n",
              "Orange    8.0\n",
              "Red       2.0\n",
              "Yellow    NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QAh7EOXY4CP",
        "colab_type": "text"
      },
      "source": [
        "# 2) DataFrames"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LKbp9VZxY5jv",
        "colab_type": "text"
      },
      "source": [
        "## DataFrames Part 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8QmP2ZSbqc7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from numpy.random import randn\n",
        "np.random.seed(101)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BiQmkSNOcrnQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(data=randn(5,4), index=['a','b','c','d','e'],columns=['w','x','y','z'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zq_Ehr0tdPe3",
        "colab_type": "code",
        "outputId": "85caf605-b434-4f38-b70d-811a88af1370",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlvrGoWZeImx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 1) Indexing and Selection"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qqIjyWAueMC2",
        "colab_type": "code",
        "outputId": "00ee5c27-81c5-4a16-9fe8-3acdd8770529",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "df['w']"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    2.706850\n",
              "b    0.651118\n",
              "c   -2.018168\n",
              "d    0.188695\n",
              "e    0.190794\n",
              "Name: w, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1SvIZzneXne",
        "colab_type": "code",
        "outputId": "6538a818-2c8b-423d-d2b6-cb213ce53be8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "type(df['w'])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sp-N1zTyejPZ",
        "colab_type": "code",
        "outputId": "a407a251-0658-412c-850c-c13b324ed524",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "type(df)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmdO7pumexPW",
        "colab_type": "code",
        "outputId": "db4b1426-6208-483c-f52e-8e7a0a411329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xko80JLfezpZ",
        "colab_type": "code",
        "outputId": "a6b206d7-321a-482b-e326-2bf6a51d58b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df[['x','y']]"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          x         y\n",
              "a  0.628133  0.907969\n",
              "b -0.319318 -0.848077\n",
              "c  0.740122  0.528813\n",
              "d -0.758872 -0.933237\n",
              "e  1.978757  2.605967"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZdy_Gq0e7NU",
        "colab_type": "code",
        "outputId": "f6104c65-4d27-4e35-ce38-162492a8474b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "type(df[['x','y']])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "219WsUCMfDn3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 2) add a new column"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXCHsU7zfJGM",
        "colab_type": "code",
        "outputId": "09e123de-caf5-4c88-ffd7-36c66bdd803c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Kz67V-ffP5Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['new'] = df['w'] + df['x']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YY4YTiTYfYiy",
        "colab_type": "code",
        "outputId": "eb14ae6b-03ae-4016-fbb0-dbfa5ed1d7b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>new</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "      <td>3.334983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "      <td>0.331800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "      <td>-1.278046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "      <td>-0.570177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "      <td>2.169552</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z       new\n",
              "a  2.706850  0.628133  0.907969  0.503826  3.334983\n",
              "b  0.651118 -0.319318 -0.848077  0.605965  0.331800\n",
              "c -2.018168  0.740122  0.528813 -0.589001 -1.278046\n",
              "d  0.188695 -0.758872 -0.933237  0.955057 -0.570177\n",
              "e  0.190794  1.978757  2.605967  0.683509  2.169552"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSZoqqk9fjCK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 3) removing columns and rows"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rg_1iblUfq56",
        "colab_type": "code",
        "outputId": "da8fd588-ff26-4525-cf00-a171c7346d86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df.drop(labels='new',axis=1)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHiqddSHf9Ne",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop(labels='new',axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2VoynuOgP_Q",
        "colab_type": "code",
        "outputId": "1cc244cf-b3ba-469e-98ac-8bd1e4b64fb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAsyGU78gvxA",
        "colab_type": "code",
        "outputId": "b283586f-a77e-4f97-c13f-5bf8ddaf42bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df.drop('b',axis=0)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAroGq-_g90b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 4) selecting the rows"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHPl_St5hBl8",
        "colab_type": "code",
        "outputId": "b6cebc7c-9d35-4761-89be-0e5363de27f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unbrVL2chFL4",
        "colab_type": "code",
        "outputId": "b76393fe-8c17-44b7-e552-8c973a59bdc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "df.loc['c']"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "w   -2.018168\n",
              "x    0.740122\n",
              "y    0.528813\n",
              "z   -0.589001\n",
              "Name: c, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEelC2CkhOiI",
        "colab_type": "code",
        "outputId": "1e3b9c91-32d5-49e4-d32c-fb55e7daf9f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "type(df.loc['c'])"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8eRNjpFHh5Lk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# selecting the subset of rows and columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaR5XLa-iAG3",
        "colab_type": "code",
        "outputId": "08f1c9c4-0e1f-47a9-e7cd-4d629a1bf00e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xxooeL3ViWjA",
        "colab_type": "code",
        "outputId": "874c8cc6-251e-4fbe-9dee-70ae7b9a2eba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "df.loc['c','x']"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7401220570561068"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ESmUek_IiuMm",
        "colab_type": "code",
        "outputId": "3b698a58-8b28-47c5-9c21-af1defeb5b2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "df.loc[['d','e'],['y','z']]"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          y         z\n",
              "d -0.933237  0.955057\n",
              "e  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAWhAGDMj6Lw",
        "colab_type": "text"
      },
      "source": [
        "## DataFrames Part 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPWGMFd5j8Aa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from numpy.random import randn\n",
        "np.random.seed(101)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYRlzdFyj-bf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.DataFrame(randn(5,4),[\"a\",\"b\",\"c\",\"d\",\"e\"],[\"w\",\"x\",\"y\",\"z\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yu85EufBXWgB",
        "colab_type": "code",
        "outputId": "825d76d3-8827-45d7-a066-3c57806e1c13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNBKHtEKXeN3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 1) conditional selection"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTTbjluwXgru",
        "colab_type": "code",
        "outputId": "29359965-2785-4f5a-bd8e-6e257f2a94b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df > 0"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>True</td>\n",
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              "      <td>True</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "       w      x      y      z\n",
              "a   True   True   True   True\n",
              "b   True  False  False   True\n",
              "c  False   True   True  False\n",
              "d   True  False  False   True\n",
              "e   True   True   True   True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5PcQb7a_Xzwp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "b = df > 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5_d4rMVX09o",
        "colab_type": "code",
        "outputId": "69954947-aaa4-4125-a8ea-94d1f6832b07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "b"
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      "execution_count": 63,
      "outputs": [
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              "      <td>True</td>\n",
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              "</table>\n",
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            "text/plain": [
              "       w      x      y      z\n",
              "a   True   True   True   True\n",
              "b   True  False  False   True\n",
              "c  False   True   True  False\n",
              "d   True  False  False   True\n",
              "e   True   True   True   True"
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          "metadata": {
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          "execution_count": 63
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZmQ7oKXX3rn",
        "colab_type": "code",
        "outputId": "c29776ff-3a4b-423b-ab34-2cc8180b5d94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df[b]"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>a</th>\n",
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              "      <td>0.503826</td>\n",
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              "      <th>b</th>\n",
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              "      <th>c</th>\n",
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              "      <th>e</th>\n",
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              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
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          "execution_count": 64
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8DT1rupYMMY",
        "colab_type": "code",
        "outputId": "98406303-2af9-46e1-8fea-96eda362f8aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df[df > 0]"
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      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "          w         x         y         z\n",
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    {
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        "colab": {
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      "source": [
        "df['w'] > 0"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a     True\n",
              "b     True\n",
              "c    False\n",
              "d     True\n",
              "e     True\n",
              "Name: w, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 66
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      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "5TpYUKxUYgeJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d = df['w'] > 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2JcL7qlYjPv",
        "colab_type": "code",
        "outputId": "406fe4e9-d45c-4503-d589-e682cbccefe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "d"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a     True\n",
              "b     True\n",
              "c    False\n",
              "d     True\n",
              "e     True\n",
              "Name: w, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 68
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      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
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      },
      "source": [
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      "execution_count": 69,
      "outputs": [
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          "data": {
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              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
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      "execution_count": 70,
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              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkkzS8XQZNON",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 2) multiple conditions"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmcDGU_EZRog",
        "colab_type": "code",
        "outputId": "2d17cf41-bfc8-4aee-d659-784ef73124af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guXaDGBMZT6t",
        "colab_type": "code",
        "outputId": "ad382a39-e08b-4f79-9e91-ff44d1a7a434",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "df[(df['w']>0) & (df['y']>1)] # and operator"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkBss_3PaUhQ",
        "colab_type": "code",
        "outputId": "608b1314-0a69-43c1-d95c-d8fb9970c988",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df[(df['w']>0) | (df['y']>1)] # or operator"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7bqSvRyYa0Zs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 3) index"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYCQrH0pa3Ob",
        "colab_type": "code",
        "outputId": "1a4a1f85-7129-4de6-a72a-57611e79d3ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z\n",
              "a  2.706850  0.628133  0.907969  0.503826\n",
              "b  0.651118 -0.319318 -0.848077  0.605965\n",
              "c -2.018168  0.740122  0.528813 -0.589001\n",
              "d  0.188695 -0.758872 -0.933237  0.955057\n",
              "e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ct4ATh6Aa47v",
        "colab_type": "code",
        "outputId": "af980353-a31e-487a-9278-e55c253ed0b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df.reset_index()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>index</th>\n",
              "      <th>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a</td>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b</td>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>c</td>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>d</td>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e</td>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  index         w         x         y         z\n",
              "0     a  2.706850  0.628133  0.907969  0.503826\n",
              "1     b  0.651118 -0.319318 -0.848077  0.605965\n",
              "2     c -2.018168  0.740122  0.528813 -0.589001\n",
              "3     d  0.188695 -0.758872 -0.933237  0.955057\n",
              "4     e  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKRIXCllbKir",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_index = ['R','G','B','O','Y']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r--9HPvZbTek",
        "colab_type": "code",
        "outputId": "e71f08ec-95b8-46d5-d443-4732985043ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "new_index"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['R', 'G', 'B', 'O', 'Y']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSKeLxuNbZm1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Colors'] = new_index"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3ebCiqibgV7",
        "colab_type": "code",
        "outputId": "cad6bcaf-a876-458e-cc15-75e6501db8d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>Colors</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "      <td>R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "      <td>G</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          w         x         y         z Colors\n",
              "a  2.706850  0.628133  0.907969  0.503826      R\n",
              "b  0.651118 -0.319318 -0.848077  0.605965      G\n",
              "c -2.018168  0.740122  0.528813 -0.589001      B\n",
              "d  0.188695 -0.758872 -0.933237  0.955057      O\n",
              "e  0.190794  1.978757  2.605967  0.683509      Y"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFdahWbgbu1F",
        "colab_type": "code",
        "outputId": "9f7b0bb4-32e2-45f4-f989-2daecd70c261",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "df.set_index('Colors')"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>w</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colors</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>R</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>O</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Y</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               w         x         y         z\n",
              "Colors                                        \n",
              "R       2.706850  0.628133  0.907969  0.503826\n",
              "G       0.651118 -0.319318 -0.848077  0.605965\n",
              "B      -2.018168  0.740122  0.528813 -0.589001\n",
              "O       0.188695 -0.758872 -0.933237  0.955057\n",
              "Y       0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLakvjgVX6GY",
        "colab_type": "text"
      },
      "source": [
        "## DataFrames Part 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCtlJXZ2X89l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Multi-index and index hierarchy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Op4YMF6YBri",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from numpy.random import randn\n",
        "np.random.seed(101)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnzCSf-ae0DD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "outside = ['Red','Red','Red','Green','Green','Green']\n",
        "inside = [1,2,3,1,2,3]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vauNam4ZkU37",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hier_index = list(zip(outside,inside))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcLdNtf0keqV",
        "colab_type": "code",
        "outputId": "d3ad3db6-51a4-4a3a-d32a-92840a334d3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "hier_index"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Red', 1), ('Red', 2), ('Red', 3), ('Green', 1), ('Green', 2), ('Green', 3)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCGBSNZ1kkXV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "multi_index = pd.MultiIndex.from_tuples(hier_index, names=['Colors','Numbers'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rgiVMIqmk7QD",
        "colab_type": "code",
        "outputId": "86c6ac71-5da2-4797-bdbd-7529cc9d4138",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "multi_index"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiIndex([(  'Red', 1),\n",
              "            (  'Red', 2),\n",
              "            (  'Red', 3),\n",
              "            ('Green', 1),\n",
              "            ('Green', 2),\n",
              "            ('Green', 3)],\n",
              "           names=['Colors', 'Numbers'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqB_qCTTlANC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(data=randn(6,2), index=multi_index, columns=['A','B'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "635FvoH9lVu5",
        "colab_type": "code",
        "outputId": "4953e8fd-8260-4351-dc4c-415d5ad2d91f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colors</th>\n",
              "      <th>Numbers</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Red</th>\n",
              "      <th>1</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Green</th>\n",
              "      <th>1</th>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       A         B\n",
              "Colors Numbers                    \n",
              "Red    1        2.706850  0.628133\n",
              "       2        0.907969  0.503826\n",
              "       3        0.651118 -0.319318\n",
              "Green  1       -0.848077  0.605965\n",
              "       2       -2.018168  0.740122\n",
              "       3        0.528813 -0.589001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "febbTHZpl89a",
        "colab_type": "code",
        "outputId": "de091321-df2e-4b2b-a296-2ddfa44da5bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "df.loc['Red'].loc[1]['A']"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.706849839399938"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yi5inDQPmdpD",
        "colab_type": "code",
        "outputId": "68a612d5-9372-4845-91aa-f8ab9115a2c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "df.loc['Green'].loc[2]['B']"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7401220570561068"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFe8dd43mu2m",
        "colab_type": "code",
        "outputId": "75db73a7-30b8-48ac-d137-07030c186b8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "df.index.names"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FrozenList(['Colors', 'Numbers'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABC6Rd5Om6k1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.index.names = ['Col','Num']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0s4aDlGnBk3",
        "colab_type": "code",
        "outputId": "8306158f-b98a-47b6-9d75-df8ddf423c44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col</th>\n",
              "      <th>Num</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Red</th>\n",
              "      <th>1</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Green</th>\n",
              "      <th>1</th>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  A         B\n",
              "Col   Num                    \n",
              "Red   1    2.706850  0.628133\n",
              "      2    0.907969  0.503826\n",
              "      3    0.651118 -0.319318\n",
              "Green 1   -0.848077  0.605965\n",
              "      2   -2.018168  0.740122\n",
              "      3    0.528813 -0.589001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zdlydlk7nKrX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# cross section"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TKxEdpynYlR",
        "colab_type": "code",
        "outputId": "d86b9fcb-90d0-412d-ed3f-adcd1ba0c8fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col</th>\n",
              "      <th>Num</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Red</th>\n",
              "      <th>1</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Green</th>\n",
              "      <th>1</th>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  A         B\n",
              "Col   Num                    \n",
              "Red   1    2.706850  0.628133\n",
              "      2    0.907969  0.503826\n",
              "      3    0.651118 -0.319318\n",
              "Green 1   -0.848077  0.605965\n",
              "      2   -2.018168  0.740122\n",
              "      3    0.528813 -0.589001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWBDRLhmnZ8W",
        "colab_type": "code",
        "outputId": "661f59f9-7125-4280-eda8-d1e3352a7b2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df.loc['Red']"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Num</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            A         B\n",
              "Num                    \n",
              "1    2.706850  0.628133\n",
              "2    0.907969  0.503826\n",
              "3    0.651118 -0.319318"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34FAJ153nk85",
        "colab_type": "code",
        "outputId": "3df1dadc-82f5-4cfa-a2ea-2a8e1f2790b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df.xs('Red')"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Num</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            A         B\n",
              "Num                    \n",
              "1    2.706850  0.628133\n",
              "2    0.907969  0.503826\n",
              "3    0.651118 -0.319318"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epIUXLFtnwro",
        "colab_type": "code",
        "outputId": "cc2bec6e-926b-4ba5-b6d4-0fd87a423b8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "df.xs(1, level='Num')"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Red</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Green</th>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              A         B\n",
              "Col                      \n",
              "Red    2.706850  0.628133\n",
              "Green -0.848077  0.605965"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wCm55gFH4wl",
        "colab_type": "text"
      },
      "source": [
        "# 3) Missing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8ozot3hH6X3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxuMblNPK6Zm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d = {'A':[1,2,np.nan], 'B':[3,np.nan,np.nan], 'C':[4,5,6]}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bP0kCe1wLOIK",
        "colab_type": "code",
        "outputId": "d87069f2-10d0-4f80-89e6-561668a24354",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "d"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A': [1, 2, nan], 'B': [3, nan, nan], 'C': [4, 5, 6]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RRQQgOOLUP4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(d)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPYjP4ZkLcSL",
        "colab_type": "code",
        "outputId": "ae6eee56-2e5a-4e39-e6e5-968f9189a12e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A    B  C\n",
              "0  1.0  3.0  4\n",
              "1  2.0  NaN  5\n",
              "2  NaN  NaN  6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMeqtIOvLk3H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dropna method"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HKKFky6FLnAE",
        "colab_type": "code",
        "outputId": "20aa7f04-0030-4953-e87b-f368dcae274c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "df.dropna()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A    B  C\n",
              "0  1.0  3.0  4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHh2hZ6EMQsZ",
        "colab_type": "code",
        "outputId": "d3043bc2-2acc-4db6-bb3c-94d5eb10a239",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "df.dropna(axis=1)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   C\n",
              "0  4\n",
              "1  5\n",
              "2  6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_tddIfTMc0k",
        "colab_type": "code",
        "outputId": "43cc8863-05a4-4b76-cc1d-c94aa5ce4675",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "df.dropna(thresh=2, axis=0)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A    B  C\n",
              "0  1.0  3.0  4\n",
              "1  2.0  NaN  5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sseDLyKOM26Y",
        "colab_type": "code",
        "outputId": "f659829f-8fc0-47e0-b32e-b470860e983c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "df.dropna(thresh=1, axis=1)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A    B  C\n",
              "0  1.0  3.0  4\n",
              "1  2.0  NaN  5\n",
              "2  NaN  NaN  6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zh9Ihbz8ND06",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# fillna method"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCqFXLQaNG-U",
        "colab_type": "code",
        "outputId": "8be68ff3-4b38-416a-c1cc-24fb2389b3a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A    B  C\n",
              "0  1.0  3.0  4\n",
              "1  2.0  NaN  5\n",
              "2  NaN  NaN  6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CA2wcu7hNJiB",
        "colab_type": "code",
        "outputId": "e1dd19b2-c206-4b1d-f72f-23ba4769140d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "df.fillna(value='New Value')"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>New Value</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>New Value</td>\n",
              "      <td>New Value</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           A          B  C\n",
              "0          1          3  4\n",
              "1          2  New Value  5\n",
              "2  New Value  New Value  6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccLo4zscNehj",
        "colab_type": "code",
        "outputId": "8b1062de-8ee5-4da4-eca4-d6871c03a43b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "df['A'].fillna(value=df['A'].mean())"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.0\n",
              "1    2.0\n",
              "2    1.5\n",
              "Name: A, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9grPXN7L5DP4",
        "colab_type": "text"
      },
      "source": [
        "# 4) Groupby Method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "50UxEpSA5Ezm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fO-EJVev62sk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "team_data = {'Company':['Apple','Apple','FB','FB','Google','Google'],\n",
        "             'Person':['Mark','Tom','John','Sara','Mia','Emma'],\n",
        "             'Sales':[200,150,350,125,260,180]}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3XVGPC5-7l2t",
        "colab_type": "code",
        "outputId": "3ffa4e8d-fb5f-403c-ac63-151c76ba72ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "team_data"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Company': ['Apple', 'Apple', 'FB', 'FB', 'Google', 'Google'],\n",
              " 'Person': ['Mark', 'Tom', 'John', 'Sara', 'Mia', 'Emma'],\n",
              " 'Sales': [200, 150, 350, 125, 260, 180]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIrH_uln7qci",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(team_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Z8M2Ajx7vbI",
        "colab_type": "code",
        "outputId": "d5903c5a-49e9-4ea5-85cb-310741f7c7ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Company</th>\n",
              "      <th>Person</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Mark</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Apple</td>\n",
              "      <td>Tom</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FB</td>\n",
              "      <td>John</td>\n",
              "      <td>350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FB</td>\n",
              "      <td>Sara</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Google</td>\n",
              "      <td>Mia</td>\n",
              "      <td>260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Google</td>\n",
              "      <td>Emma</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Company Person  Sales\n",
              "0   Apple   Mark    200\n",
              "1   Apple    Tom    150\n",
              "2      FB   John    350\n",
              "3      FB   Sara    125\n",
              "4  Google    Mia    260\n",
              "5  Google   Emma    180"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KL0N9a1P746k",
        "colab_type": "code",
        "outputId": "1d001380-7d46-4456-a58f-623e59208d60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "df.groupby(by='Company')"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f9d6e76b9b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SeDb9eqM8B7z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "b = df.groupby(by='Company')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1A1Z0iyF8E1f",
        "colab_type": "code",
        "outputId": "bfcca9c6-ea85-4cc7-f2ba-3c88400ddc83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "b.mean()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Apple</th>\n",
              "      <td>175.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FB</th>\n",
              "      <td>237.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google</th>\n",
              "      <td>220.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Sales\n",
              "Company       \n",
              "Apple    175.0\n",
              "FB       237.5\n",
              "Google   220.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FD4M4cfX8Szh",
        "colab_type": "code",
        "outputId": "c8b7964c-a675-446f-e0c1-a878fd648d64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "b.sum()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Apple</th>\n",
              "      <td>350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FB</th>\n",
              "      <td>475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google</th>\n",
              "      <td>440</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Sales\n",
              "Company       \n",
              "Apple      350\n",
              "FB         475\n",
              "Google     440"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isrprA0-8WIz",
        "colab_type": "code",
        "outputId": "5a335677-8f91-4929-a206-5c10d5e40626",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "b.std()"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Apple</th>\n",
              "      <td>35.355339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FB</th>\n",
              "      <td>159.099026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google</th>\n",
              "      <td>56.568542</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Sales\n",
              "Company            \n",
              "Apple     35.355339\n",
              "FB       159.099026\n",
              "Google    56.568542"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuV710Kc8h43",
        "colab_type": "code",
        "outputId": "a9310adf-b6dc-4dc7-c1a9-8370b9ef3fd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "b.sum().loc['Apple']"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sales    350\n",
              "Name: Apple, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjnjKtdI8znd",
        "colab_type": "code",
        "outputId": "22836f60-7c6b-4c2b-8444-c320f7996402",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "df.groupby(by='Company').sum().loc['FB']"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sales    475\n",
              "Name: FB, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5V9j29c9NvK",
        "colab_type": "code",
        "outputId": "9b613848-a55f-4c3f-f710-14c4c6653e9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "b.count()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Person</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Apple</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FB</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Person  Sales\n",
              "Company               \n",
              "Apple         2      2\n",
              "FB            2      2\n",
              "Google        2      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hi80oY19YMq",
        "colab_type": "code",
        "outputId": "36fc6178-16b1-4f53-cad3-33f56dd0797f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "b.max()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Person</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Apple</th>\n",
              "      <td>Tom</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FB</th>\n",
              "      <td>Sara</td>\n",
              "      <td>350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google</th>\n",
              "      <td>Mia</td>\n",
              "      <td>260</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Person  Sales\n",
              "Company              \n",
              "Apple      Tom    200\n",
              "FB        Sara    350\n",
              "Google     Mia    260"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sj9PaJ5A9gEZ",
        "colab_type": "code",
        "outputId": "80912b16-da8f-4fb0-ae6c-55f100819f6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "b.min()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Person</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Apple</th>\n",
              "      <td>Mark</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FB</th>\n",
              "      <td>John</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google</th>\n",
              "      <td>Emma</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Person  Sales\n",
              "Company              \n",
              "Apple     Mark    150\n",
              "FB        John    125\n",
              "Google    Emma    180"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUtZ3agF9qWV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# groupby method with describe function"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Rls1Kyr9xCf",
        "colab_type": "code",
        "outputId": "cf5645ea-bffb-4421-f911-86e893a2e059",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df.groupby(by='Company').describe()"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">Sales</th>\n",
              "    </tr>\n",
              "    <tr>\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>Company</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>Apple</th>\n",
              "      <td>2.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>35.355339</td>\n",
              "      <td>150.0</td>\n",
              "      <td>162.50</td>\n",
              "      <td>175.0</td>\n",
              "      <td>187.50</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FB</th>\n",
              "      <td>2.0</td>\n",
              "      <td>237.5</td>\n",
              "      <td>159.099026</td>\n",
              "      <td>125.0</td>\n",
              "      <td>181.25</td>\n",
              "      <td>237.5</td>\n",
              "      <td>293.75</td>\n",
              "      <td>350.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google</th>\n",
              "      <td>2.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>56.568542</td>\n",
              "      <td>180.0</td>\n",
              "      <td>200.00</td>\n",
              "      <td>220.0</td>\n",
              "      <td>240.00</td>\n",
              "      <td>260.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Sales                                                        \n",
              "        count   mean         std    min     25%    50%     75%    max\n",
              "Company                                                              \n",
              "Apple     2.0  175.0   35.355339  150.0  162.50  175.0  187.50  200.0\n",
              "FB        2.0  237.5  159.099026  125.0  181.25  237.5  293.75  350.0\n",
              "Google    2.0  220.0   56.568542  180.0  200.00  220.0  240.00  260.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJjlogOh-B7j",
        "colab_type": "code",
        "outputId": "2354df49-22e6-416f-9085-505324d74c4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "df.groupby(by='Company').describe().transpose()"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Company</th>\n",
              "      <th>Apple</th>\n",
              "      <th>FB</th>\n",
              "      <th>Google</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">Sales</th>\n",
              "      <th>count</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>175.000000</td>\n",
              "      <td>237.500000</td>\n",
              "      <td>220.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>35.355339</td>\n",
              "      <td>159.099026</td>\n",
              "      <td>56.568542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>180.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>162.500000</td>\n",
              "      <td>181.250000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>175.000000</td>\n",
              "      <td>237.500000</td>\n",
              "      <td>220.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>187.500000</td>\n",
              "      <td>293.750000</td>\n",
              "      <td>240.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>260.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Company           Apple          FB      Google\n",
              "Sales count    2.000000    2.000000    2.000000\n",
              "      mean   175.000000  237.500000  220.000000\n",
              "      std     35.355339  159.099026   56.568542\n",
              "      min    150.000000  125.000000  180.000000\n",
              "      25%    162.500000  181.250000  200.000000\n",
              "      50%    175.000000  237.500000  220.000000\n",
              "      75%    187.500000  293.750000  240.000000\n",
              "      max    200.000000  350.000000  260.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4GnSqUC7-Rm5",
        "colab_type": "code",
        "outputId": "52273ddf-1eeb-4d18-da5b-29f80bfd63c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "df.groupby(by='Company').describe().loc['FB']"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sales  count      2.000000\n",
              "       mean     237.500000\n",
              "       std      159.099026\n",
              "       min      125.000000\n",
              "       25%      181.250000\n",
              "       50%      237.500000\n",
              "       75%      293.750000\n",
              "       max      350.000000\n",
              "Name: FB, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fU6lmBwVgpBl",
        "colab_type": "text"
      },
      "source": [
        "# 5) Merging, Joining and Concatenating"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2lZZEeTagrFj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_dl9j85kRgj",
        "colab_type": "text"
      },
      "source": [
        "## 1) DataFrames Concatenation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtJ4IFmbg4IL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1=pd.DataFrame({'A':['A0','A1','A2','A3'],\n",
        "                  'B':['B0','B1','B2','B3'],\n",
        "                  'C':['C0','C1','C2','C3'],\n",
        "                  'D': ['D0','D1','D2','D3']},\n",
        "                   index = [0, 1, 2, 3])\n",
        " \n",
        "df2=pd.DataFrame({'A':['A4','A5','A6','A7'],\n",
        "                  'B':['B4','B5','B6','B7'],\n",
        "                  'C':['C4','C5','C6','C7'],\n",
        "                  'D':['D4','D5','D6','D7']},\n",
        "                   index = [4, 5, 6, 7])\n",
        " \n",
        "df3=pd.DataFrame({'A':['A8','A9','A10','A11'],\n",
        "                  'B':['B8','B9','B10','B11'],\n",
        "                  'C':['C8','C9','C10','C11'],\n",
        "                  'D':['D8','D9','D10','D11']},\n",
        "                   index = [8, 9, 10, 11])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bw0MO3RjwkZI",
        "colab_type": "code",
        "outputId": "8fb3a8b2-6146-4c14-afbc-4b63fbe338ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df1"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    A   B   C   D\n",
              "0  A0  B0  C0  D0\n",
              "1  A1  B1  C1  D1\n",
              "2  A2  B2  C2  D2\n",
              "3  A3  B3  C3  D3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfMgbFgSwm7h",
        "colab_type": "code",
        "outputId": "62c41626-1a24-461d-b38b-fd429d526ef7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df2"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A4</td>\n",
              "      <td>B4</td>\n",
              "      <td>C4</td>\n",
              "      <td>D4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>A5</td>\n",
              "      <td>B5</td>\n",
              "      <td>C5</td>\n",
              "      <td>D5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>A6</td>\n",
              "      <td>B6</td>\n",
              "      <td>C6</td>\n",
              "      <td>D6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>A7</td>\n",
              "      <td>B7</td>\n",
              "      <td>C7</td>\n",
              "      <td>D7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    A   B   C   D\n",
              "4  A4  B4  C4  D4\n",
              "5  A5  B5  C5  D5\n",
              "6  A6  B6  C6  D6\n",
              "7  A7  B7  C7  D7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2ljhMnRwpaY",
        "colab_type": "code",
        "outputId": "c0ecc903-1cc9-41e1-991c-878868944fe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df3"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>A8</td>\n",
              "      <td>B8</td>\n",
              "      <td>C8</td>\n",
              "      <td>D8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>A9</td>\n",
              "      <td>B9</td>\n",
              "      <td>C9</td>\n",
              "      <td>D9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>A10</td>\n",
              "      <td>B10</td>\n",
              "      <td>C10</td>\n",
              "      <td>D10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>A11</td>\n",
              "      <td>B11</td>\n",
              "      <td>C11</td>\n",
              "      <td>D11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      A    B    C    D\n",
              "8    A8   B8   C8   D8\n",
              "9    A9   B9   C9   D9\n",
              "10  A10  B10  C10  D10\n",
              "11  A11  B11  C11  D11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jr6n13iiw6a4",
        "colab_type": "code",
        "outputId": "5c9e3e30-f083-4b86-e424-c83309c58ddd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "pd.concat(objs=[df1,df2,df3])"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A4</td>\n",
              "      <td>B4</td>\n",
              "      <td>C4</td>\n",
              "      <td>D4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>A5</td>\n",
              "      <td>B5</td>\n",
              "      <td>C5</td>\n",
              "      <td>D5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>A6</td>\n",
              "      <td>B6</td>\n",
              "      <td>C6</td>\n",
              "      <td>D6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>A7</td>\n",
              "      <td>B7</td>\n",
              "      <td>C7</td>\n",
              "      <td>D7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>A8</td>\n",
              "      <td>B8</td>\n",
              "      <td>C8</td>\n",
              "      <td>D8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>A9</td>\n",
              "      <td>B9</td>\n",
              "      <td>C9</td>\n",
              "      <td>D9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>A10</td>\n",
              "      <td>B10</td>\n",
              "      <td>C10</td>\n",
              "      <td>D10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>A11</td>\n",
              "      <td>B11</td>\n",
              "      <td>C11</td>\n",
              "      <td>D11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      A    B    C    D\n",
              "0    A0   B0   C0   D0\n",
              "1    A1   B1   C1   D1\n",
              "2    A2   B2   C2   D2\n",
              "3    A3   B3   C3   D3\n",
              "4    A4   B4   C4   D4\n",
              "5    A5   B5   C5   D5\n",
              "6    A6   B6   C6   D6\n",
              "7    A7   B7   C7   D7\n",
              "8    A8   B8   C8   D8\n",
              "9    A9   B9   C9   D9\n",
              "10  A10  B10  C10  D10\n",
              "11  A11  B11  C11  D11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "jeimt3EZxWDl",
        "colab_type": "code",
        "outputId": "574cc392-4657-4667-f116-276d9f134b63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "pd.concat(objs=[df1,df2,df3],axis=1)"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A5</td>\n",
              "      <td>B5</td>\n",
              "      <td>C5</td>\n",
              "      <td>D5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A6</td>\n",
              "      <td>B6</td>\n",
              "      <td>C6</td>\n",
              "      <td>D6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A7</td>\n",
              "      <td>B7</td>\n",
              "      <td>C7</td>\n",
              "      <td>D7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\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>A8</td>\n",
              "      <td>B8</td>\n",
              "      <td>C8</td>\n",
              "      <td>D8</td>\n",
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              "      <th>9</th>\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>A9</td>\n",
              "      <td>B9</td>\n",
              "      <td>C9</td>\n",
              "      <td>D9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\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>A10</td>\n",
              "      <td>B10</td>\n",
              "      <td>C10</td>\n",
              "      <td>D10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\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>A11</td>\n",
              "      <td>B11</td>\n",
              "      <td>C11</td>\n",
              "      <td>D11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "      A    B    C    D    A    B    C    D    A    B    C    D\n",
              "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
              "4   NaN  NaN  NaN  NaN   A4   B4   C4   D4  NaN  NaN  NaN  NaN\n",
              "5   NaN  NaN  NaN  NaN   A5   B5   C5   D5  NaN  NaN  NaN  NaN\n",
              "6   NaN  NaN  NaN  NaN   A6   B6   C6   D6  NaN  NaN  NaN  NaN\n",
              "7   NaN  NaN  NaN  NaN   A7   B7   C7   D7  NaN  NaN  NaN  NaN\n",
              "8   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A8   B8   C8   D8\n",
              "9   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A9   B9   C9   D9\n",
              "10  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A10  B10  C10  D10\n",
              "11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A11  B11  C11  D11"
            ]
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x1XKck8wkdF4",
        "colab_type": "text"
      },
      "source": [
        "## 2) DataFrames Merging"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e05zoE6Wki0R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df4=pd.DataFrame({'Key':['K0','K1','K2','K3'],\n",
        "                   'A':['A0','A1','A2','A3'],\n",
        "                   'B':['B0','B1','B2','B3']})\n",
        " \n",
        "df5=pd.DataFrame({'Key':['K0','K1','K2','K3'],\n",
        "                    'C':['C0','C1','C2','C3'],\n",
        "                    'D':['D0','D1','D2','D3']})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYKFkB5UyY2v",
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        "outputId": "abd85a82-64ae-4ad1-f5aa-016e454bb39a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
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      },
      "source": [
        "df4"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K1</td>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K2</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K3</td>\n",
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              "      <td>B3</td>\n",
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              "  Key   A   B\n",
              "0  K0  A0  B0\n",
              "1  K1  A1  B1\n",
              "2  K2  A2  B2\n",
              "3  K3  A3  B3"
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          "base_uri": "https://localhost:8080/",
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        }
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      "source": [
        "df5"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>D0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K3</td>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "  Key   C   D\n",
              "0  K0  C0  D0\n",
              "1  K1  C1  D1\n",
              "2  K2  C2  D2\n",
              "3  K3  C3  D3"
            ]
          },
          "metadata": {
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          "execution_count": 144
        }
      ]
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      "cell_type": "code",
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        "id": "eRVOp4TYyxvp",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "pd.merge(left=df4, right=df5, on='Key')"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>K1</td>\n",
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              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K2</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K3</td>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Key   A   B   C   D\n",
              "0  K0  A0  B0  C0  D0\n",
              "1  K1  A1  B1  C1  D1\n",
              "2  K2  A2  B2  C2  D2\n",
              "3  K3  A3  B3  C3  D3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RRNmjI3kmMv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df6 = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\n",
        "                     'key2': ['K0', 'K1', 'K0', 'K1'],\n",
        "                        'A': ['A0', 'A1', 'A2', 'A3'],\n",
        "                        'B': ['B0', 'B1', 'B2', 'B3']})\n",
        "    \n",
        "df7 = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\n",
        "                               'key2': ['K0', 'K0', 'K0', 'K0'],\n",
        "                                  'C': ['C0', 'C1', 'C2', 'C3'],\n",
        "                                  'D': ['D0', 'D1', 'D2', 'D3']})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQsx0P2RzmMt",
        "colab_type": "code",
        "outputId": "623d73f6-4f7e-4e27-f2ce-f7a459a2454d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df6"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>key1</th>\n",
              "      <th>key2</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K0</td>\n",
              "      <td>K0</td>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K0</td>\n",
              "      <td>K1</td>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K1</td>\n",
              "      <td>K0</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K2</td>\n",
              "      <td>K1</td>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  key1 key2   A   B\n",
              "0   K0   K0  A0  B0\n",
              "1   K0   K1  A1  B1\n",
              "2   K1   K0  A2  B2\n",
              "3   K2   K1  A3  B3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbSoxPJLkrp7",
        "colab_type": "code",
        "outputId": "76995a37-6d14-46b4-cb1d-07a77999672f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df7"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>key1</th>\n",
              "      <th>key2</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K0</td>\n",
              "      <td>K0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K1</td>\n",
              "      <td>K0</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K1</td>\n",
              "      <td>K0</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K2</td>\n",
              "      <td>K0</td>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  key1 key2   C   D\n",
              "0   K0   K0  C0  D0\n",
              "1   K1   K0  C1  D1\n",
              "2   K1   K0  C2  D2\n",
              "3   K2   K0  C3  D3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZ4KvJhXzu_a",
        "colab_type": "code",
        "outputId": "d45a93ac-978b-4f65-a1d7-40da5362529a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "pd.merge(left=df6, right=df7, on=['key1','key2'])"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>key1</th>\n",
              "      <th>key2</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K0</td>\n",
              "      <td>K0</td>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K1</td>\n",
              "      <td>K0</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K1</td>\n",
              "      <td>K0</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  key1 key2   A   B   C   D\n",
              "0   K0   K0  A0  B0  C0  D0\n",
              "1   K1   K0  A2  B2  C1  D1\n",
              "2   K1   K0  A2  B2  C2  D2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDPCQShMlFRf",
        "colab_type": "text"
      },
      "source": [
        "## 3) DataFrames Join"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-4LZ0YjlIvg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df8 = pd.DataFrame({'A':['A0','A1','A2','A3'],\n",
        "                  'B':['B0','B1','B2','B3']},\n",
        "                    index=['K0','K1','K2','K3'])\n",
        " \n",
        "df9 = pd.DataFrame({'C':['C0','C1','C2','C3'],\n",
        "                  'D':['D0','D1','D2','D3']},\n",
        "                    index=['K0','K1','K2','K3'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3zqkSxXlUru",
        "colab_type": "code",
        "outputId": "18d5bf27-41dd-4057-d106-098b0a54f33c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df8"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>K0</th>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K1</th>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K2</th>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K3</th>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A   B\n",
              "K0  A0  B0\n",
              "K1  A1  B1\n",
              "K2  A2  B2\n",
              "K3  A3  B3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRXRYIeM0Uh8",
        "colab_type": "code",
        "outputId": "c669a788-4ea2-4038-bcf6-8f094e24ea6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df9"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>K0</th>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K1</th>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K2</th>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K3</th>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     C   D\n",
              "K0  C0  D0\n",
              "K1  C1  D1\n",
              "K2  C2  D2\n",
              "K3  C3  D3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFpoTYhM0ayC",
        "colab_type": "code",
        "outputId": "8d946afa-5bd9-43c8-9a25-40e9d8e87098",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df8.join(df9)"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>K0</th>\n",
              "      <td>A0</td>\n",
              "      <td>B0</td>\n",
              "      <td>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K1</th>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "      <td>C1</td>\n",
              "      <td>D1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K2</th>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K3</th>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A   B   C   D\n",
              "K0  A0  B0  C0  D0\n",
              "K1  A1  B1  C1  D1\n",
              "K2  A2  B2  C2  D2\n",
              "K3  A3  B3  C3  D3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    }
  ]
}