EX_08/EX_08_pandas_dataframe_2.ipynb

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      "name": "pandas_dataframe_2.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFmrEaPO4kPo"
      },
      "source": [
        "# Pandas DataFrames\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47xmFDbsv45a"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tu1sl9SMwGJY"
      },
      "source": [
        "from numpy.random import randn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgdUH6XuwG2o"
      },
      "source": [
        "np.random.seed(101)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OD9QO0QwJJD"
      },
      "source": [
        "df=pd.DataFrame(randn(5,4), ['A','B','C','D','E'], ['W','X','Y','Z' ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLwEM0oUwMLY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "b8bdbeaa-cd3c-41e1-a454-04402ca8b003"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>-2.018168</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>-0.589001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118 -0.319318 -0.848077  0.605965\n",
              "C -2.018168  0.740122  0.528813 -0.589001\n",
              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1o2K4j04yaI"
      },
      "source": [
        "# Conditional Selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nquc39Hj4Go1"
      },
      "source": [
        "## Simple Conditional"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9VBmU7sV3339",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8c0ade2b-d761-4b5e-a03e-fc7fc46efdef"
      },
      "source": [
        "# Returning a bunch of booleans...:/\n",
        "df>0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "       W      X      Y      Z\n",
              "A   True   True   True   True\n",
              "B   True  False  False   True\n",
              "C  False   True   True  False\n",
              "D   True  False  False   True\n",
              "E   True   True   True   True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyZ1JYt08DZ9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bbb64269-0548-4bf2-f847-7faeeaf72a71"
      },
      "source": [
        "# This operation returns a DataFrame:\n",
        "type(df>0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kHMC7DJxPf5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "118ecae4-6ba9-4b53-cb65-a4d7620f1bcd"
      },
      "source": [
        "# To work with Conditional Selection the best way is,\n",
        "# instead of comparing and passing in the comparative of inequity, \n",
        "# pass in the entire dataframe, like this...\n",
        "df[df>0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byVE9X1l8aYD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b8ce275a-8dfe-40c4-fb20-196ad90e4663"
      },
      "source": [
        "# The previous operation returns a DataFrame:\n",
        "type(df[df>0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eT56OCoAxXDM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "39d7a7cd-5d8d-49de-a427-925be1b4b485"
      },
      "source": [
        "# ...as well as we can pass in the column as a parameter, or, even better,...\n",
        "df['W']>0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A     True\n",
              "B     True\n",
              "C    False\n",
              "D     True\n",
              "E     True\n",
              "Name: W, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "79045175-dd2e-4d8b-8f0f-265a6056de65"
      },
      "source": [
        "# The previous operation returns a Series:\n",
        "type(df['W']>0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWM3dwJfxd1v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "85eb76f9-d6da-4e30-8a86-c6515eda42bd"
      },
      "source": [
        "# ...for instance, return the DataFrame that 'W' is different from zero;\n",
        "# Here have the return of a subset of the dataframe (instead of bool type),\n",
        "# on which we can work, independently of the original dataframe.\n",
        "# Now we have no non-existent values (NaN) as output o/\n",
        "# It does not return the row 'C'\n",
        "df[df['W']>0]\n"
      ],
      "execution_count": null,
      "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>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",
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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",
              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmP1z1jRFal1",
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          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "0be64e63-ba82-4a02-96d0-fa09b60730f2"
      },
      "source": [
        "# Here is the DataFrame again...\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118 -0.319318 -0.848077  0.605965\n",
              "C -2.018168  0.740122  0.528813 -0.589001\n",
              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUuFzN5vxjBf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "b4aa0e32-7a3b-492c-dc40-fcc5cda74c2e"
      },
      "source": [
        "# Returning the DataFrame that 'Z' is less than zero;\n",
        "# It does return the row 'C' only:\n",
        "df[df['Z']<0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          W         X         Y         Z\n",
              "C -2.018168  0.740122  0.528813 -0.589001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CRhWQbjz6NR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "0e1a8cc1-e5f9-4492-8eb9-a68c0598727b"
      },
      "source": [
        "# If we want to return 'X' column where 'W' is greater than zero...\n",
        "# Slicing & Indexing like this:\n",
        "df[df['W']>0]['X']"
      ],
      "execution_count": null,
      "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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpZqz1TwGIbV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3e8f8775-d8a9-4e6e-91f4-5ffe5c235839"
      },
      "source": [
        "# The previous operation returns a Series:\n",
        "type(df[df['W']>0]['X'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kGWdCfjf38ei"
      },
      "source": [
        "## Multiple Conditional"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2BC3H8y0bK0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "0fa6c30d-c1f2-4946-dd53-b64d0a8060ac"
      },
      "source": [
        "# Remember to use parentheses to separate each conditional Selection; this is AND...\n",
        "# Use df[()&()] or df[()|()]:\n",
        "df[(df['W']>0)&(df['X']>1)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          W         X         Y         Z\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bt8X6S41Rxa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "a6a3c883-82da-4aae-a3f9-ca51ae6c6111"
      },
      "source": [
        "# ...And this is OR\n",
        "df[(df['W']>0)|(df['Y']>1)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118 -0.319318 -0.848077  0.605965\n",
              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-2Hn2Oq8wRl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "913510a0-0b42-402a-cd29-8dd5270b8641"
      },
      "source": [
        "# The previous operation returns a DataFrame:\n",
        "type(df[(df['W']>0)|(df['Y']>1)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDghMb-8C5AB"
      },
      "source": [
        "# Slicing & Indexing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "czChCH2i-Tf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "22546e71-5be9-4349-fb2c-35d591ddc250"
      },
      "source": [
        "# Another example: \n",
        "# I want to retrieve all DataFrame values where \n",
        "# column 'W' is greater than zero, filtering Columns 'W' and 'Y'\n",
        "# Slicing & Indexing like this: df[df[][]], in ONE STEP:\n",
        "df[(df['W']>0)][['W', 'Y']]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "  </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>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",
              "</div>"
            ],
            "text/plain": [
              "          W         Y\n",
              "A  2.706850  0.907969\n",
              "B  0.651118 -0.848077\n",
              "D  0.188695 -0.933237\n",
              "E  0.190794  2.605967"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmtsHobG9Yez"
      },
      "source": [
        "# But we can do it in MULTIPLE STEPS, like theses:\n",
        "boolser=df['W']>0 "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_gqj4g2Bb15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "5792b364-4f66-4f01-864b-505b9029368d"
      },
      "source": [
        "boolser"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A     True\n",
              "B     True\n",
              "C    False\n",
              "D     True\n",
              "E     True\n",
              "Name: W, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edECksXD9jDE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f62c0121-a8f3-4126-a8b3-cadee087e0ab"
      },
      "source": [
        "# The previous operation returns a Series:\n",
        "type(boolser)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lU4DAAtC9nrZ"
      },
      "source": [
        "# Filtering the DataFrame with booleans returned\n",
        "result=df[boolser]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tDwB_fkGBfOg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "ed871b83-e266-42d4-be8d-7d5affeb8952"
      },
      "source": [
        "result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.319318</td>\n",
              "      <td>-0.848077</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.758872</td>\n",
              "      <td>-0.933237</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118 -0.319318 -0.848077  0.605965\n",
              "D  0.188695 -0.758872 -0.933237  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "114TYlwe9znU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "df989608-adf2-4d85-e411-ee1e6d0bfdaa"
      },
      "source": [
        "# The previous operation returns a DataFrame:\n",
        "type(result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5dTM8Ot93F2"
      },
      "source": [
        "# Now create a python list with the columns we want to return\n",
        "myCols=['W', 'Y']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGy07jC69_Lp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c5ca8ad9-7ca8-4646-989e-94e3ee2e4f86"
      },
      "source": [
        "# The previous operation returns a Python List:\n",
        "type(myCols)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwxy3SmF-B55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "ec99b7f0-49bf-4091-d3b9-23ee84aa9b99"
      },
      "source": [
        "# Now filtering the DataFrame result pass in the list as filter, and\n",
        "# The result is the same as running in one cell [20] above\n",
        "# There you have it!\n",
        "\n",
        "result[myCols]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "  </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>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",
              "</div>"
            ],
            "text/plain": [
              "          W         Y\n",
              "A  2.706850  0.907969\n",
              "B  0.651118 -0.848077\n",
              "D  0.188695 -0.933237\n",
              "E  0.190794  2.605967"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVfUFZKg-F0K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "321cf8c1-7383-4183-efc8-ad79f91110b9"
      },
      "source": [
        "print(\"Thanks for reading. Favorite it if you Like it o/\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thanks for reading. Favorite it if you Like it o/\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}