second_pandas.ipynb

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      "name": "second_pandas.ipynb",
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/subhamghimire/Machine-Learning/blob/master/second_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "metadata": {
        "id": "m3NOaDjagl-r",
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      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ELFRQLnoV5E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1Gqf5NVgwPw",
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        }
      },
      "source": [
        "pd.DataFrame({'Yes': [50, 21], 'No': [131, 2]})"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Yes</th>\n",
              "      <th>No</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50</td>\n",
              "      <td>131</td>\n",
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              "      <th>1</th>\n",
              "      <td>21</td>\n",
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              "   Yes   No\n",
              "0   50  131\n",
              "1   21    2"
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      },
      "source": [
        "pd.DataFrame({'0':[0,0],'1':[0,1]}) #and operation"
      ],
      "execution_count": 5,
      "outputs": [
        {
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              "   0  1\n",
              "0  0  0\n",
              "1  0  1"
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          "execution_count": 5
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        "outputId": "4194c9af-dffd-4d57-a975-ae34ed9402da",
        "colab": {
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          "height": 118
        }
      },
      "source": [
        "pd.Series([1,2,3,4,5],['a','b','c','d','e'])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "b    2\n",
              "c    3\n",
              "d    4\n",
              "e    5\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PoGyKjDznTee",
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        "outputId": "89974b44-64bf-4cbe-9cbb-7934e60c2c06",
        "colab": {
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          "height": 34
        }
      },
      "source": [
        "my_data = [1,2,3,4]\n",
        "arr = np.array(my_data)\n",
        "labels = ['a','b','c','d']\n",
        "d = {'a':0,'b':10,'c':100}\n",
        "arr"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
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        }
      },
      "source": [
        "pd.Series(my_data,labels)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "b    2\n",
              "c    3\n",
              "d    4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "pd.Series(d)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a      0\n",
              "b     10\n",
              "c    100\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tLPPFdZo40B",
        "colab_type": "code",
        "outputId": "5eb2bffb-3a62-4075-a781-4dbb76b6b625",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "labels"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['a', 'b', 'c', 'd']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2_Wu_9VplEA",
        "colab_type": "code",
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      },
      "source": [
        "pd.Series(labels)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    a\n",
              "1    b\n",
              "2    c\n",
              "3    d\n",
              "dtype: object"
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          "metadata": {
            "tags": []
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          "execution_count": 11
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      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "R9bBRYVTpxNx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ser1 = pd.Series([1,2,3,4],index = ['USA', 'Germany','USSR', 'Japan'])                                   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7G2uipiqTh-",
        "colab_type": "code",
        "outputId": "c48ff398-58bf-4a9b-bd4b-9e7eebccbe1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
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      },
      "source": [
        "ser1"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USA        1\n",
              "Germany    2\n",
              "USSR       3\n",
              "Japan      4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwNlzqi7qaeu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ser2 = pd.Series([1,2,5,4],index = ['USA', 'Germany','Italy', 'Japan'])                                   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkY3o_Ueqb0I",
        "colab_type": "code",
        "outputId": "5d819983-039d-4e75-c857-0370bc288c9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "ser2"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USA        1\n",
              "Germany    2\n",
              "Italy      5\n",
              "Japan      4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3KuLn18qc67",
        "colab_type": "code",
        "outputId": "e035429d-cda8-448f-ee7c-77f69be6d7e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "ser1 + ser2"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Germany    4.0\n",
              "Italy      NaN\n",
              "Japan      8.0\n",
              "USA        2.0\n",
              "USSR       NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CI9FVfINqiyL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from numpy.random import randn\n",
        "np.random.seed(101)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCC2SXkotbKK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(101)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9B6hviqwtd9z",
        "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": "PRR58J9itrkL",
        "colab_type": "code",
        "outputId": "abba238a-d787-42b2-99f8-6acc8477cdb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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            ],
            "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": 20
        }
      ]
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "df.X"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    0.628133\n",
              "B   -0.319318\n",
              "C    0.740122\n",
              "D   -0.758872\n",
              "E    1.978757\n",
              "Name: X, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4aTrV3YuWtN",
        "colab_type": "code",
        "outputId": "254ea4b0-77b5-4b8f-f850-1d6eb2248e98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "df['X']\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    0.628133\n",
              "B   -0.319318\n",
              "C    0.740122\n",
              "D   -0.758872\n",
              "E    1.978757\n",
              "Name: X, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndj8PsLfukZW",
        "colab_type": "code",
        "outputId": "f28cddf0-b0e5-4928-afe3-27c2720ab2af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(df['X'])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
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      },
      "source": [
        "type(df)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYVcTJbPusBc",
        "colab_type": "code",
        "outputId": "7ba48e26-77f8-4a0d-9f90-a4ea4e3ab15b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df[['W','Y']] #selecting columns"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>Y</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.907969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.848077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.528813</td>\n",
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              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.933237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>2.605967</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          W         Y\n",
              "A  2.706850  0.907969\n",
              "B  0.651118 -0.848077\n",
              "C -2.018168  0.528813\n",
              "D  0.188695 -0.933237\n",
              "E  0.190794  2.605967"
            ]
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          "metadata": {
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          "execution_count": 25
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        "df['new'] = df['W'] + df['X']\n",
        "df"
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      "execution_count": 26,
      "outputs": [
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              "      <th>C</th>\n",
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              "    <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",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
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              "          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"
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          "execution_count": 26
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        "df.drop('new',axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "metadata": {
        "id": "XBZRxppEvXxS",
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      "execution_count": 28,
      "outputs": [
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              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
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              "          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",
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              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
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          "execution_count": 28
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      "execution_count": 29,
      "outputs": [
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              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
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              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
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              "          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",
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              "D  0.188695 -0.758872 -0.933237  0.955057"
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      "cell_type": "code",
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        "df.shape"
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      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
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      "cell_type": "code",
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      "execution_count": 31,
      "outputs": [
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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",
              "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"
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          "execution_count": 31
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    {
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      "metadata": {
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        "colab_type": "code",
        "outputId": "1b22cf70-430f-4d08-bf7f-ad1351c7f89e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "df.loc['A']   #selecting rows"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "W    2.706850\n",
              "X    0.628133\n",
              "Y    0.907969\n",
              "Z    0.503826\n",
              "Name: A, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Ilpnoj5LxpYE",
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      "source": [
        "df.iloc[0]"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "W    2.706850\n",
              "X    0.628133\n",
              "Y    0.907969\n",
              "Z    0.503826\n",
              "Name: A, dtype: float64"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 33
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      "source": [
        "df.loc[['A','B'],['W','X']]"
      ],
      "execution_count": 34,
      "outputs": [
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      "outputs": [
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              "0.6281327087844596"
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          "metadata": {
            "tags": []
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          "execution_count": 35
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      "outputs": [
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              "      <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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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       NaN       NaN  0.605965\n",
              "C       NaN  0.740122  0.528813       NaN\n",
              "D  0.188695       NaN       NaN  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYBcmlW8zhLq",
        "colab_type": "code",
        "outputId": "8b4af95b-0518-4f2f-a95c-b42a3fda9f8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df[df['W']>0]"
      ],
      "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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              "</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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGIIMH1lzn09",
        "colab_type": "code",
        "outputId": "b3eca60c-b3d2-4ffc-c124-3444043b2a49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "df[df['W']>0]['X']"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    0.628133\n",
              "B   -0.319318\n",
              "D   -0.758872\n",
              "E    1.978757\n",
              "Name: X, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "St3TS4ng0kKC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "outside = ['G1','G1','G1','G2','G2','G2']\n",
        "inside = [1,2,3,1,2,3]\n",
        "hier_index = list(zip(outside,inside))\n",
        "hier_index = pd.MultiIndex.from_tuples(hier_index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71_l9j1x8VjB",
        "colab_type": "code",
        "outputId": "eceb91ca-ae38-48ab-cce7-18607155a2fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "hier_index"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiIndex([('G1', 1),\n",
              "            ('G1', 2),\n",
              "            ('G1', 3),\n",
              "            ('G2', 1),\n",
              "            ('G2', 2),\n",
              "            ('G2', 3)],\n",
              "           )"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeFJ8IwT8XCa",
        "colab_type": "code",
        "outputId": "a5044bb9-f351-4956-9add-5b92fc64a953",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "df = pd.DataFrame(np.random.randn(6,2),index=hier_index,columns=['A','B'])\n",
        "df"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">G1</th>\n",
              "      <th>1</th>\n",
              "      <td>0.302665</td>\n",
              "      <td>1.693723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.706086</td>\n",
              "      <td>-1.159119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.134841</td>\n",
              "      <td>0.390528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">G2</th>\n",
              "      <th>1</th>\n",
              "      <td>0.166905</td>\n",
              "      <td>0.184502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.807706</td>\n",
              "      <td>0.072960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.638787</td>\n",
              "      <td>0.329646</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             A         B\n",
              "G1 1  0.302665  1.693723\n",
              "   2 -1.706086 -1.159119\n",
              "   3 -0.134841  0.390528\n",
              "G2 1  0.166905  0.184502\n",
              "   2  0.807706  0.072960\n",
              "   3  0.638787  0.329646"
            ]
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          "execution_count": 41
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "df.loc['G1']"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.302665</td>\n",
              "      <td>1.693723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.706086</td>\n",
              "      <td>-1.159119</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.390528</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          A         B\n",
              "1  0.302665  1.693723\n",
              "2 -1.706086 -1.159119\n",
              "3 -0.134841  0.390528"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "DmrjCJIE8dnG",
        "colab_type": "code",
        "outputId": "09fcd30a-25a6-4b90-cd11-5024566712ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "df.index.names = ['Group','Num']\n",
        "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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              "      <th>A</th>\n",
              "      <th>B</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">G1</th>\n",
              "      <th>1</th>\n",
              "      <td>0.302665</td>\n",
              "      <td>1.693723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.706086</td>\n",
              "      <td>-1.159119</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.134841</td>\n",
              "      <td>0.390528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">G2</th>\n",
              "      <th>1</th>\n",
              "      <td>0.166905</td>\n",
              "      <td>0.184502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.807706</td>\n",
              "      <td>0.072960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.638787</td>\n",
              "      <td>0.329646</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  A         B\n",
              "Group Num                    \n",
              "G1    1    0.302665  1.693723\n",
              "      2   -1.706086 -1.159119\n",
              "      3   -0.134841  0.390528\n",
              "G2    1    0.166905  0.184502\n",
              "      2    0.807706  0.072960\n",
              "      3    0.638787  0.329646"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
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      "metadata": {
        "id": "XLEjtsUB8oLc",
        "colab_type": "code",
        "outputId": "3a233b58-d244-471c-d81d-a416ebb939ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "df.xs('G1')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "      <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>0.302665</td>\n",
              "      <td>1.693723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.706086</td>\n",
              "      <td>-1.159119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.134841</td>\n",
              "      <td>0.390528</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            A         B\n",
              "Num                    \n",
              "1    0.302665  1.693723\n",
              "2   -1.706086 -1.159119\n",
              "3   -0.134841  0.390528"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hyp0TvVi8sR-",
        "colab_type": "code",
        "outputId": "68a1c04d-2cce-4066-8c25-0d288f2c72f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "df.xs(1,level='Num')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Group</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>G1</th>\n",
              "      <td>0.302665</td>\n",
              "      <td>1.693723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G2</th>\n",
              "      <td>0.166905</td>\n",
              "      <td>0.184502</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              A         B\n",
              "Group                    \n",
              "G1     0.302665  1.693723\n",
              "G2     0.166905  0.184502"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
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    {
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          "height": 254
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        "outputId": "fdac9808-3659-490b-ff38-e966bb32149d"
      },
      "source": [
        "df"
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      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Num</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">G1</th>\n",
              "      <th>1</th>\n",
              "      <td>0.302665</td>\n",
              "      <td>1.693723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.706086</td>\n",
              "      <td>-1.159119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.134841</td>\n",
              "      <td>0.390528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">G2</th>\n",
              "      <th>1</th>\n",
              "      <td>0.166905</td>\n",
              "      <td>0.184502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.807706</td>\n",
              "      <td>0.072960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.638787</td>\n",
              "      <td>0.329646</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  A         B\n",
              "Group Num                    \n",
              "G1    1    0.302665  1.693723\n",
              "      2   -1.706086 -1.159119\n",
              "      3   -0.134841  0.390528\n",
              "G2    1    0.166905  0.184502\n",
              "      2    0.807706  0.072960\n",
              "      3    0.638787  0.329646"
            ]
          },
          "metadata": {
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          },
          "execution_count": 46
        }
      ]
    },
    {
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        "colab": {}
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        "d = {'A':[1,2,np.nan],'B':[5,np.nan,np.nan],'C':[1,2,3]}"
      ],
      "execution_count": 0,
      "outputs": []
    },
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        "df = pd.DataFrame(d)"
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      "execution_count": 0,
      "outputs": []
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      "cell_type": "code",
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              "     A    B  C\n",
              "0  1.0  5.0  1\n",
              "1  2.0  NaN  2\n",
              "2  NaN  NaN  3"
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        "df.dropna(axis=1)"
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      "execution_count": 56,
      "outputs": [
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              "   C\n",
              "0  1\n",
              "1  2\n",
              "2  3"
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          "execution_count": 56
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        "df.dropna(thresh=2) #atmost 2 non-Nan should there"
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      "execution_count": 61,
      "outputs": [
        {
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              "     A    B  C\n",
              "0  1.0  5.0  1\n",
              "1  2.0  NaN  2"
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          "execution_count": 61
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      "execution_count": 62,
      "outputs": [
        {
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          "data": {
            "text/html": [
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              "     A    B  C\n",
              "0    1    5  1\n",
              "1    2  N/A  2\n",
              "2  N/A  N/A  3"
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          "execution_count": 62
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    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "293f1c7e-a3c6-4060-ca68-e690bc97abd8"
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      "source": [
        "df['A'].fillna(value=df['A'].mean())"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.0\n",
              "1    2.0\n",
              "2    1.5\n",
              "Name: A, dtype: float64"
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          "execution_count": 63
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ejd07tBFYAlP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Groupby\n",
        "data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],\n",
        "       'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],\n",
        "       'Sales':[200,120,340,124,243,350]} "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWpt7u-ZYn_A",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "df = pd.DataFrame(data)"
      ],
      "execution_count": 0,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "llmJaLcLYsLE",
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        "outputId": "ea5a36a5-071a-4c6b-bafb-25411059e3c2"
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      "source": [
        "df"
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      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>Person</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>GOOG</td>\n",
              "      <td>Sam</td>\n",
              "      <td>200</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GOOG</td>\n",
              "      <td>Charlie</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>Amy</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>Vanessa</td>\n",
              "      <td>124</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>FB</td>\n",
              "      <td>Carl</td>\n",
              "      <td>243</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>FB</td>\n",
              "      <td>Sarah</td>\n",
              "      <td>350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Company   Person  Sales\n",
              "0    GOOG      Sam    200\n",
              "1    GOOG  Charlie    120\n",
              "2    MSFT      Amy    340\n",
              "3    MSFT  Vanessa    124\n",
              "4      FB     Carl    243\n",
              "5      FB    Sarah    350"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "8982b0dc-9f30-40fd-890b-df491d426194"
      },
      "source": [
        "df.groupby('Company').mean()"
      ],
      "execution_count": 77,
      "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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              "      <th></th>\n",
              "      <th>Sales</th>\n",
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              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>FB</th>\n",
              "      <td>296.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GOOG</th>\n",
              "      <td>160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>232.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Sales\n",
              "Company       \n",
              "FB       296.5\n",
              "GOOG     160.0\n",
              "MSFT     232.0"
            ]
          },
          "metadata": {
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          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "fe6EnJSWYxRV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "byComp = df.groupby('Company')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IR4OVRW9ZLwZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "1abfb030-98c3-409d-a15c-7dd14243960e"
      },
      "source": [
        "byComp.sum().loc['FB']"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sales    593\n",
              "Name: FB, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cr-M1K4oZYfX",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "2e1da085-71c2-45eb-e69c-00e3edd5a158"
      },
      "source": [
        "byComp.describe().transpose()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\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>Company</th>\n",
              "      <th>FB</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>MSFT</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>296.500000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>232.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>75.660426</td>\n",
              "      <td>56.568542</td>\n",
              "      <td>152.735065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>243.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>124.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>269.750000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>178.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>296.500000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>232.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>323.250000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>286.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>350.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>340.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Company              FB        GOOG        MSFT\n",
              "Sales count    2.000000    2.000000    2.000000\n",
              "      mean   296.500000  160.000000  232.000000\n",
              "      std     75.660426   56.568542  152.735065\n",
              "      min    243.000000  120.000000  124.000000\n",
              "      25%    269.750000  140.000000  178.000000\n",
              "      50%    296.500000  160.000000  232.000000\n",
              "      75%    323.250000  180.000000  286.000000\n",
              "      max    350.000000  200.000000  340.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9CsmUPAjZ-hx",
        "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])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2WuOL0UbxT1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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]) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GVtH30krbzqc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "b9703642-3c96-4ccb-a03d-49adafd8f9d2"
      },
      "source": [
        "pd.concat([df1,df2])"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>D3</td>\n",
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              "    <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",
              "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"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
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    {
      "cell_type": "code",
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        "outputId": "102d10aa-1f2d-491f-f49f-a610a3c8cdb1"
      },
      "source": [
        "pd.concat([df1,df2],axis=1)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "      <td>C2</td>\n",
              "      <td>D2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\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>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",
              "    </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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A    B    C    D    A    B    C    D\n",
              "0   A0   B0   C0   D0  NaN  NaN  NaN  NaN\n",
              "1   A1   B1   C1   D1  NaN  NaN  NaN  NaN\n",
              "2   A2   B2   C2   D2  NaN  NaN  NaN  NaN\n",
              "3   A3   B3   C3   D3  NaN  NaN  NaN  NaN\n",
              "4  NaN  NaN  NaN  NaN   A4   B4   C4   D4\n",
              "5  NaN  NaN  NaN  NaN   A5   B5   C5   D5\n",
              "6  NaN  NaN  NaN  NaN   A6   B6   C6   D6\n",
              "7  NaN  NaN  NaN  NaN   A7   B7   C7   D7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJXNU6Uecd6G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
        "                     'A': ['A0', 'A1', 'A2', 'A3'],\n",
        "                     'B': ['B0', 'B1', 'B2', 'B3']})\n",
        "   \n",
        "right = 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": "-4L3AVjHcovD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "1a2a00a1-b78b-43ca-b9a6-a11d7d99424c"
      },
      "source": [
        "left"
      ],
      "execution_count": 86,
      "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",
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              "      <th></th>\n",
              "      <th>key</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>A0</td>\n",
              "      <td>B0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K1</td>\n",
              "      <td>A1</td>\n",
              "      <td>B1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>K2</td>\n",
              "      <td>A2</td>\n",
              "      <td>B2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>K3</td>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  key   A   B\n",
              "0  K0  A0  B0\n",
              "1  K1  A1  B1\n",
              "2  K2  A2  B2\n",
              "3  K3  A3  B3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDrofhsIcrqE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "76252a8c-e58a-4166-9868-165e990a97ae"
      },
      "source": [
        "right"
      ],
      "execution_count": 87,
      "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",
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              "\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>key</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>C0</td>\n",
              "      <td>D0</td>\n",
              "    </tr>\n",
              "    <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",
              "</div>"
            ],
            "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": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BBAQd1c-cs2D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "e867ee78-8c60-433d-fa54-414abc8ff0ec"
      },
      "source": [
        "pd.merge(left,right,how='inner',on='key')"
      ],
      "execution_count": 88,
      "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",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>key</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>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>A1</td>\n",
              "      <td>B1</td>\n",
              "      <td>C1</td>\n",
              "      <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": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3xv6qztctti",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "left = 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",
        "right = 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": {
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        "pd.merge(left, right, on=['key1', 'key2'])"
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      "execution_count": 90,
      "outputs": [
        {
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          "data": {
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              "  </tbody>\n",
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              "  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"
            ]
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          "execution_count": 90
        }
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          "height": 225
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        "pd.merge(left, right, how='outer', on=['key1', 'key2'])"
      ],
      "execution_count": 91,
      "outputs": [
        {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>3</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",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>K2</td>\n",
              "      <td>K1</td>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>K2</td>\n",
              "      <td>K0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C3</td>\n",
              "      <td>D3</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   K0   K1   A1   B1  NaN  NaN\n",
              "2   K1   K0   A2   B2   C1   D1\n",
              "3   K1   K0   A2   B2   C2   D2\n",
              "4   K2   K1   A3   B3  NaN  NaN\n",
              "5   K2   K0  NaN  NaN   C3   D3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
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      "source": [
        "pd.merge(left, right, how='right', on=['key1', 'key2'])"
      ],
      "execution_count": 92,
      "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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              "      <th>3</th>\n",
              "      <td>K2</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>C3</td>\n",
              "      <td>D3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "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\n",
              "3   K2   K0  NaN  NaN  C3  D3"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
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    {
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      "source": [
        "pd.merge(left, right, how='left', on=['key1', 'key2'])"
      ],
      "execution_count": 93,
      "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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              "      <td>NaN</td>\n",
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              "      <th>2</th>\n",
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              "      <th>3</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",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>K2</td>\n",
              "      <td>K1</td>\n",
              "      <td>A3</td>\n",
              "      <td>B3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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   K0   K1  A1  B1  NaN  NaN\n",
              "2   K1   K0  A2  B2   C1   D1\n",
              "3   K1   K0  A2  B2   C2   D2\n",
              "4   K2   K1  A3  B3  NaN  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pY3lO-8adbBX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\n",
        "                     'B': ['B0', 'B1', 'B2']},\n",
        "                      index=['K0', 'K1', 'K2']) \n",
        "\n",
        "right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\n",
        "                    'D': ['D0', 'D2', 'D3']},\n",
        "                      index=['K0', 'K2', 'K3'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4cLxxthdfXM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "87b0a78b-907e-491c-9aa4-7be9b78f11f1"
      },
      "source": [
        "left"
      ],
      "execution_count": 95,
      "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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              "      <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",
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              "    <tr>\n",
              "      <th>K2</th>\n",
              "      <td>A2</td>\n",
              "      <td>B2</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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "8191d7b0-2068-4ce0-f4df-39bc3c2f394c"
      },
      "source": [
        "left.join(right)"
      ],
      "execution_count": 96,
      "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",
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              "  <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>NaN</td>\n",
              "      <td>NaN</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A   B    C    D\n",
              "K0  A0  B0   C0   D0\n",
              "K1  A1  B1  NaN  NaN\n",
              "K2  A2  B2   C2   D2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hy5EIrlYdiwk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "5c64eb56-5650-4df8-d3b3-de27b17f0d71"
      },
      "source": [
        "left.join(right, how='outer')"
      ],
      "execution_count": 97,
      "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",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <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>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</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  NaN  NaN\n",
              "K2   A2   B2   C2   D2\n",
              "K3  NaN  NaN   C3   D3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKoViDjudqwI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "cf201591-2126-48f7-a500-e3f5fb830019"
      },
      "source": [
        "df = pd.DataFrame({'col1':[1,2,3,4],\n",
        "                   'col2':[444,555,666,444],\n",
        "                   'col3':['abc','def','ghi','xyz']})\n",
        "df.head()"
      ],
      "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>col1</th>\n",
              "      <th>col2</th>\n",
              "      <th>col3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>444</td>\n",
              "      <td>abc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>555</td>\n",
              "      <td>def</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>666</td>\n",
              "      <td>ghi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>444</td>\n",
              "      <td>xyz</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   col1  col2 col3\n",
              "0     1   444  abc\n",
              "1     2   555  def\n",
              "2     3   666  ghi\n",
              "3     4   444  xyz"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgS0heoJfm5J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6d7c2592-a7e8-4732-e360-ecf41be8456e"
      },
      "source": [
        "df['col2'].unique()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([444, 555, 666])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VY7rlCUkf2Ah",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0fe0cb5a-c013-4f3f-9817-84c71ebd0487"
      },
      "source": [
        "df['col2'].nunique() #length of unique value"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eASlX1D4f-6v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "3667e623-b833-43c2-c4b3-97873fd4d0b9"
      },
      "source": [
        "df['col2'].value_counts()"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "444    2\n",
              "555    1\n",
              "666    1\n",
              "Name: col2, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKIf2SCtgMPA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "2ce293ab-540d-4739-845d-4015fabe7bbc"
      },
      "source": [
        "df[(df['col1']>2) & (df['col2']==444)]"
      ],
      "execution_count": 127,
      "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>col1</th>\n",
              "      <th>col2</th>\n",
              "      <th>col3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>444</td>\n",
              "      <td>xyz</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   col1  col2 col3\n",
              "3     4   444  xyz"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGfNsF-dgX9n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Applying Functions\n",
        "def times2(x):\n",
        "  return x*2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E3kQAPgEg64O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "8db2ee6e-da65-47a6-e72d-b1c17fea41a5"
      },
      "source": [
        "df['col1'].apply(times2)"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2\n",
              "1    4\n",
              "2    6\n",
              "3    8\n",
              "Name: col1, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrNjhjxdg_vF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "6cf271e1-0b16-443a-8052-d9050b01c16b"
      },
      "source": [
        "df['col3'].apply(len)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3\n",
              "1    3\n",
              "2    3\n",
              "3    3\n",
              "Name: col3, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrX9xq0ShMrp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "b07606d1-a226-4748-f103-81d15b5d120d"
      },
      "source": [
        "df['col1'].apply(lambda x:x**2)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     1\n",
              "1     4\n",
              "2     9\n",
              "3    16\n",
              "Name: col1, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1vyu5nPBhFOV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Permanently Removing a Column\n",
        "del df['col1']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wH-Y8bgChUlt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "2f32340d-5d48-4599-8d6c-f47a6c9e85eb"
      },
      "source": [
        "df"
      ],
      "execution_count": 134,
      "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>col2</th>\n",
              "      <th>col3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>444</td>\n",
              "      <td>abc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>555</td>\n",
              "      <td>def</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>666</td>\n",
              "      <td>ghi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>444</td>\n",
              "      <td>xyz</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   col2 col3\n",
              "0   444  abc\n",
              "1   555  def\n",
              "2   666  ghi\n",
              "3   444  xyz"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cv-y8gethVXI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b219f0f3-bd2c-4212-df64-6e434d6d386f"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['col2', 'col3'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-I6dhj9haWY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d11c637d-cd5e-45c3-c7a0-f76afc4bba83"
      },
      "source": [
        "df.index"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=4, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZpnamNjhcNr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "e06ef4d5-3c04-4861-f0af-7887901644cf"
      },
      "source": [
        "df.sort_values(by='col2') #inplace=False by default"
      ],
      "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>col2</th>\n",
              "      <th>col3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>444</td>\n",
              "      <td>abc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>444</td>\n",
              "      <td>xyz</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>555</td>\n",
              "      <td>def</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>666</td>\n",
              "      <td>ghi</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   col2 col3\n",
              "0   444  abc\n",
              "3   444  xyz\n",
              "1   555  def\n",
              "2   666  ghi"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_5mzjIRh5UW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = {'A':['foo','foo','foo','bar','bar','bar'],\n",
        "     'B':['one','one','two','two','one','one'],\n",
        "       'C':['x','y','x','y','x','y'],\n",
        "       'D':[1,3,2,5,4,1]}\n",
        "\n",
        "df = pd.DataFrame(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWTEdr_QiEJR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "9a9c8ccf-6eaf-4706-d2e4-60bc88f5f183"
      },
      "source": [
        "df"
      ],
      "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>0</th>\n",
              "      <td>foo</td>\n",
              "      <td>one</td>\n",
              "      <td>x</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>foo</td>\n",
              "      <td>one</td>\n",
              "      <td>y</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>foo</td>\n",
              "      <td>two</td>\n",
              "      <td>x</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>bar</td>\n",
              "      <td>two</td>\n",
              "      <td>y</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bar</td>\n",
              "      <td>one</td>\n",
              "      <td>x</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>bar</td>\n",
              "      <td>one</td>\n",
              "      <td>y</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A    B  C  D\n",
              "0  foo  one  x  1\n",
              "1  foo  one  y  3\n",
              "2  foo  two  x  2\n",
              "3  bar  two  y  5\n",
              "4  bar  one  x  4\n",
              "5  bar  one  y  1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOZRzGmOiE8P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "47e2f5cc-62e8-42ef-edc8-cac067e8981a"
      },
      "source": [
        "df.pivot_table(values='D',index=['A', 'B'],columns=['C'])"
      ],
      "execution_count": 139,
      "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>C</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
              "      <th>one</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
              "      <th>one</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "C          x    y\n",
              "A   B            \n",
              "bar one  4.0  1.0\n",
              "    two  NaN  5.0\n",
              "foo one  1.0  3.0\n",
              "    two  2.0  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ps21Bn3GiJVM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0366fd1a-128e-4f46-a166-1d316ff700b3"
      },
      "source": [
        "#Data input and output\n",
        "pwd"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbM_yI8ij3P_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "f4b087c0-cbd4-4d52-baf9-029bb44b868b"
      },
      "source": [
        "df = pd.read_csv('example')\n",
        "df"
      ],
      "execution_count": 144,
      "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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<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",
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              "      <th>0</th>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
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              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
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              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    a   b   c   d\n",
              "0   0   1   2   3\n",
              "1   4   5   6   7\n",
              "2   8   9  10  11\n",
              "3  12  13  14  15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8WWR9TgkvCr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv('Output',index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o66WTBK3lYAV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "8792c623-612e-433d-b975-3eac143524f0"
      },
      "source": [
        "pd.read_csv('Output')"
      ],
      "execution_count": 149,
      "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",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>a</th>\n",
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              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    a   b   c   d\n",
              "0   0   1   2   3\n",
              "1   4   5   6   7\n",
              "2   8   9  10  11\n",
              "3  12  13  14  15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZX4iIiVKlZHB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "57c405a4-a587-44d4-c44e-dd168e372c04"
      },
      "source": [
        "pd.read_excel('Excel_Sample.xlsx',sheet_name='Sheet1')"
      ],
      "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>Unnamed: 0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0   a   b   c   d\n",
              "0           0   0   1   2   3\n",
              "1           1   4   5   6   7\n",
              "2           2   8   9  10  11\n",
              "3           3  12  13  14  15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWyybId6mT1Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_excel('Excel_Sample2.xlsx',sheet_name='Newsheet')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3SYIAXOmnL7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "b8e4ffbc-02f9-4e0f-cee0-b15273e4cf6b"
      },
      "source": [
        "df = pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.html')\n",
        "df[0]"
      ],
      "execution_count": 178,
      "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>Bank Name</th>\n",
              "      <th>City</th>\n",
              "      <th>ST</th>\n",
              "      <th>CERT</th>\n",
              "      <th>Acquiring Institution</th>\n",
              "      <th>Closing Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The First State Bank</td>\n",
              "      <td>Barboursville</td>\n",
              "      <td>WV</td>\n",
              "      <td>14361</td>\n",
              "      <td>MVB Bank, Inc.</td>\n",
              "      <td>April 3, 2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ericson State Bank</td>\n",
              "      <td>Ericson</td>\n",
              "      <td>NE</td>\n",
              "      <td>18265</td>\n",
              "      <td>Farmers and Merchants Bank</td>\n",
              "      <td>February 14, 2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>City National Bank of New Jersey</td>\n",
              "      <td>Newark</td>\n",
              "      <td>NJ</td>\n",
              "      <td>21111</td>\n",
              "      <td>Industrial Bank</td>\n",
              "      <td>November 1, 2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Resolute Bank</td>\n",
              "      <td>Maumee</td>\n",
              "      <td>OH</td>\n",
              "      <td>58317</td>\n",
              "      <td>Buckeye State Bank</td>\n",
              "      <td>October 25, 2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Louisa Community Bank</td>\n",
              "      <td>Louisa</td>\n",
              "      <td>KY</td>\n",
              "      <td>58112</td>\n",
              "      <td>Kentucky Farmers Bank Corporation</td>\n",
              "      <td>October 25, 2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>556</th>\n",
              "      <td>Superior Bank, FSB</td>\n",
              "      <td>Hinsdale</td>\n",
              "      <td>IL</td>\n",
              "      <td>32646</td>\n",
              "      <td>Superior Federal, FSB</td>\n",
              "      <td>July 27, 2001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557</th>\n",
              "      <td>Malta National Bank</td>\n",
              "      <td>Malta</td>\n",
              "      <td>OH</td>\n",
              "      <td>6629</td>\n",
              "      <td>North Valley Bank</td>\n",
              "      <td>May 3, 2001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>558</th>\n",
              "      <td>First Alliance Bank &amp; Trust Co.</td>\n",
              "      <td>Manchester</td>\n",
              "      <td>NH</td>\n",
              "      <td>34264</td>\n",
              "      <td>Southern New Hampshire Bank &amp; Trust</td>\n",
              "      <td>February 2, 2001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>559</th>\n",
              "      <td>National State Bank of Metropolis</td>\n",
              "      <td>Metropolis</td>\n",
              "      <td>IL</td>\n",
              "      <td>3815</td>\n",
              "      <td>Banterra Bank of Marion</td>\n",
              "      <td>December 14, 2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>560</th>\n",
              "      <td>Bank of Honolulu</td>\n",
              "      <td>Honolulu</td>\n",
              "      <td>HI</td>\n",
              "      <td>21029</td>\n",
              "      <td>Bank of the Orient</td>\n",
              "      <td>October 13, 2000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>561 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Bank Name  ...       Closing Date\n",
              "0                 The First State Bank  ...      April 3, 2020\n",
              "1                   Ericson State Bank  ...  February 14, 2020\n",
              "2     City National Bank of New Jersey  ...   November 1, 2019\n",
              "3                        Resolute Bank  ...   October 25, 2019\n",
              "4                Louisa Community Bank  ...   October 25, 2019\n",
              "..                                 ...  ...                ...\n",
              "556                 Superior Bank, FSB  ...      July 27, 2001\n",
              "557                Malta National Bank  ...        May 3, 2001\n",
              "558    First Alliance Bank & Trust Co.  ...   February 2, 2001\n",
              "559  National State Bank of Metropolis  ...  December 14, 2000\n",
              "560                   Bank of Honolulu  ...   October 13, 2000\n",
              "\n",
              "[561 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RidJW7cYneDD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sqlalchemy import create_engine"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s57gHxecnyHv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "63ed4aa4-2321-42ff-fdc9-5050f13805f5"
      },
      "source": [
        "engine = create_engine('sqlite:///:memory:', echo=True)\n",
        "#engine = create_engine('sqlite:///:memory:')\n",
        "conn = engine.connect()"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-04-22 12:00:33,614 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1\n",
            "2020-04-22 12:00:33,616 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:00:33,618 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1\n",
            "2020-04-22 12:00:33,619 INFO sqlalchemy.engine.base.Engine ()\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ppv0Ri1unymy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "b4375cc4-0d0c-4864-ef3c-ab13f6d5511b"
      },
      "source": [
        "df = pd.read_csv('example')\n",
        "df"
      ],
      "execution_count": 207,
      "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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    a   b   c   d\n",
              "0   0   1   2   3\n",
              "1   4   5   6   7\n",
              "2   8   9  10  11\n",
              "3  12  13  14  15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEcnikXwzHPh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "93ac0619-d0c1-4e75-b1b1-e6edf036e96c"
      },
      "source": [
        "df.to_sql('my_table',con=engine)"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-04-22 12:11:23,924 INFO sqlalchemy.engine.base.Engine PRAGMA main.table_info(\"my_table\")\n",
            "2020-04-22 12:11:23,925 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:23,927 INFO sqlalchemy.engine.base.Engine PRAGMA temp.table_info(\"my_table\")\n",
            "2020-04-22 12:11:23,928 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:23,936 INFO sqlalchemy.engine.base.Engine \n",
            "CREATE TABLE my_table (\n",
            "\t\"index\" BIGINT, \n",
            "\ta BIGINT, \n",
            "\tb BIGINT, \n",
            "\tc BIGINT, \n",
            "\td BIGINT\n",
            ")\n",
            "\n",
            "\n",
            "2020-04-22 12:11:23,938 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:23,945 INFO sqlalchemy.engine.base.Engine COMMIT\n",
            "2020-04-22 12:11:23,946 INFO sqlalchemy.engine.base.Engine CREATE INDEX ix_my_table_index ON my_table (\"index\")\n",
            "2020-04-22 12:11:23,948 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:23,950 INFO sqlalchemy.engine.base.Engine COMMIT\n",
            "2020-04-22 12:11:23,956 INFO sqlalchemy.engine.base.Engine BEGIN (implicit)\n",
            "2020-04-22 12:11:23,958 INFO sqlalchemy.engine.base.Engine INSERT INTO my_table (\"index\", a, b, c, d) VALUES (?, ?, ?, ?, ?)\n",
            "2020-04-22 12:11:23,959 INFO sqlalchemy.engine.base.Engine ((0, 0, 1, 2, 3), (1, 4, 5, 6, 7), (2, 8, 9, 10, 11), (3, 12, 13, 14, 15))\n",
            "2020-04-22 12:11:23,961 INFO sqlalchemy.engine.base.Engine COMMIT\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P40hrnSsn0Vq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "977cdfe4-9e72-449f-f48c-b8655a485abe"
      },
      "source": [
        "sql_df = pd.read_sql('my_table',conn)"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-04-22 12:11:51,948 INFO sqlalchemy.engine.base.Engine PRAGMA main.table_info(\"my_table\")\n",
            "2020-04-22 12:11:51,949 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,951 INFO sqlalchemy.engine.base.Engine SELECT name FROM sqlite_master WHERE type='table' ORDER BY name\n",
            "2020-04-22 12:11:51,951 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,953 INFO sqlalchemy.engine.base.Engine PRAGMA main.table_info(\"my_table\")\n",
            "2020-04-22 12:11:51,954 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,956 INFO sqlalchemy.engine.base.Engine SELECT sql FROM  (SELECT * FROM sqlite_master UNION ALL   SELECT * FROM sqlite_temp_master) WHERE name = 'my_table' AND type = 'table'\n",
            "2020-04-22 12:11:51,959 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,961 INFO sqlalchemy.engine.base.Engine PRAGMA main.foreign_key_list(\"my_table\")\n",
            "2020-04-22 12:11:51,963 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,964 INFO sqlalchemy.engine.base.Engine PRAGMA temp.foreign_key_list(\"my_table\")\n",
            "2020-04-22 12:11:51,967 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,968 INFO sqlalchemy.engine.base.Engine SELECT sql FROM  (SELECT * FROM sqlite_master UNION ALL   SELECT * FROM sqlite_temp_master) WHERE name = 'my_table' AND type = 'table'\n",
            "2020-04-22 12:11:51,969 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,974 INFO sqlalchemy.engine.base.Engine PRAGMA main.index_list(\"my_table\")\n",
            "2020-04-22 12:11:51,975 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,975 INFO sqlalchemy.engine.base.Engine PRAGMA main.index_info(\"ix_my_table_index\")\n",
            "2020-04-22 12:11:51,977 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,978 INFO sqlalchemy.engine.base.Engine PRAGMA main.index_list(\"my_table\")\n",
            "2020-04-22 12:11:51,981 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,983 INFO sqlalchemy.engine.base.Engine PRAGMA main.index_info(\"ix_my_table_index\")\n",
            "2020-04-22 12:11:51,986 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,987 INFO sqlalchemy.engine.base.Engine SELECT sql FROM  (SELECT * FROM sqlite_master UNION ALL   SELECT * FROM sqlite_temp_master) WHERE name = 'my_table' AND type = 'table'\n",
            "2020-04-22 12:11:51,990 INFO sqlalchemy.engine.base.Engine ()\n",
            "2020-04-22 12:11:51,995 INFO sqlalchemy.engine.base.Engine SELECT my_table.\"index\", my_table.a, my_table.b, my_table.c, my_table.d \n",
            "FROM my_table\n",
            "2020-04-22 12:11:51,997 INFO sqlalchemy.engine.base.Engine ()\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6pSO3XHn12-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "0649af9b-3b74-42e0-a3fe-7a2663de4c10"
      },
      "source": [
        "sql_df"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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            ],
            "text/plain": [
              "   index   a   b   c   d\n",
              "0      0   0   1   2   3\n",
              "1      1   4   5   6   7\n",
              "2      2   8   9  10  11\n",
              "3      3  12  13  14  15"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJbP9FuTod0S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}