{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Group numeric values into categories (bins)\n",
        "\n",
        "We often have a column with many numeric values and we want into group them to bins or buckets, such as age groups or value tiers. In Excel we can do it using and of the following options: \n",
        "\n",
        "- Data Menu -> Data Analysis -> Histogram or Rank and Percentile\n",
        "- _VLOOKUP_ (`=LOOKUP(A1,{0,7,14,31,90,180,360},{\"0-6\",\"7-13\",\"14-30\",\"31-89\",\"90-179\",\"180-359\",\">360\"})`, for example), or \n",
        "- _IF_ (`=if(b2>30,\"large\",if(A1>20,\"medium\",if(A1>=10,\"small\",if(A1<10,\"tiny\",\"\"))))`, for example)\n",
        "- _INDEX_ (`=INDEX({\"Small\",\"Medium\",\"Large\"},LARGE(IF(A1>{0,11,21},{1,2,3}),1))`, for example)\n",
        "\n",
        "\n",
        "[![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aiola-lab/from-excel-to-pandas/blob/master/notebooks/02.08_group_values.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Cut Function\n",
        "\n",
        "Since this is a common need, Pandas has built-in functions _cut_ and _qcut_ that make it more flexible and accurate. We start with importing pandas."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Real-life example\n",
        "\n",
        "Let's analyze data regarding the impact of weatehr information on numbers of bike rentals. We know that when it is too cold or too hot, people rent less bike. But what is the cutoff temperature value? When we listen to the next 10 days weather forecast, how should we prepare our bike rental fleet to meet the expected demand?\n",
        "\n",
        "### Loading Data\n",
        "\n",
        "As usual, let's load a dataset to work on. We will use the dataset that is used before, \"Bike Share\". This is a data set about the demand of bike share service in Seoul. Please note that we need to modify the default encoding of _read_csv_ from 'UTF-8' to 'latin1'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "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>Date</th>\n",
              "      <th>Rented Bike Count</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Temperature(°C)</th>\n",
              "      <th>Humidity(%)</th>\n",
              "      <th>Wind speed (m/s)</th>\n",
              "      <th>Visibility (10m)</th>\n",
              "      <th>Dew point temperature(°C)</th>\n",
              "      <th>Solar Radiation (MJ/m2)</th>\n",
              "      <th>Rainfall(mm)</th>\n",
              "      <th>Snowfall (cm)</th>\n",
              "      <th>Seasons</th>\n",
              "      <th>Holiday</th>\n",
              "      <th>Functioning Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01/12/2017</td>\n",
              "      <td>254</td>\n",
              "      <td>0</td>\n",
              "      <td>-5.2</td>\n",
              "      <td>37</td>\n",
              "      <td>2.2</td>\n",
              "      <td>2000</td>\n",
              "      <td>-17.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01/12/2017</td>\n",
              "      <td>204</td>\n",
              "      <td>1</td>\n",
              "      <td>-5.5</td>\n",
              "      <td>38</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2000</td>\n",
              "      <td>-17.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01/12/2017</td>\n",
              "      <td>173</td>\n",
              "      <td>2</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>39</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2000</td>\n",
              "      <td>-17.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01/12/2017</td>\n",
              "      <td>107</td>\n",
              "      <td>3</td>\n",
              "      <td>-6.2</td>\n",
              "      <td>40</td>\n",
              "      <td>0.9</td>\n",
              "      <td>2000</td>\n",
              "      <td>-17.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01/12/2017</td>\n",
              "      <td>78</td>\n",
              "      <td>4</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2000</td>\n",
              "      <td>-18.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8755</th>\n",
              "      <td>30/11/2018</td>\n",
              "      <td>1003</td>\n",
              "      <td>19</td>\n",
              "      <td>4.2</td>\n",
              "      <td>34</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1894</td>\n",
              "      <td>-10.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8756</th>\n",
              "      <td>30/11/2018</td>\n",
              "      <td>764</td>\n",
              "      <td>20</td>\n",
              "      <td>3.4</td>\n",
              "      <td>37</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2000</td>\n",
              "      <td>-9.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8757</th>\n",
              "      <td>30/11/2018</td>\n",
              "      <td>694</td>\n",
              "      <td>21</td>\n",
              "      <td>2.6</td>\n",
              "      <td>39</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1968</td>\n",
              "      <td>-9.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8758</th>\n",
              "      <td>30/11/2018</td>\n",
              "      <td>712</td>\n",
              "      <td>22</td>\n",
              "      <td>2.1</td>\n",
              "      <td>41</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1859</td>\n",
              "      <td>-9.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8759</th>\n",
              "      <td>30/11/2018</td>\n",
              "      <td>584</td>\n",
              "      <td>23</td>\n",
              "      <td>1.9</td>\n",
              "      <td>43</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1909</td>\n",
              "      <td>-9.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Autumn</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8760 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date  Rented Bike Count  Hour  Temperature(°C)  Humidity(%)  \\\n",
              "0     01/12/2017                254     0             -5.2           37   \n",
              "1     01/12/2017                204     1             -5.5           38   \n",
              "2     01/12/2017                173     2             -6.0           39   \n",
              "3     01/12/2017                107     3             -6.2           40   \n",
              "4     01/12/2017                 78     4             -6.0           36   \n",
              "...          ...                ...   ...              ...          ...   \n",
              "8755  30/11/2018               1003    19              4.2           34   \n",
              "8756  30/11/2018                764    20              3.4           37   \n",
              "8757  30/11/2018                694    21              2.6           39   \n",
              "8758  30/11/2018                712    22              2.1           41   \n",
              "8759  30/11/2018                584    23              1.9           43   \n",
              "\n",
              "      Wind speed (m/s)  Visibility (10m)  Dew point temperature(°C)  \\\n",
              "0                  2.2              2000                      -17.6   \n",
              "1                  0.8              2000                      -17.6   \n",
              "2                  1.0              2000                      -17.7   \n",
              "3                  0.9              2000                      -17.6   \n",
              "4                  2.3              2000                      -18.6   \n",
              "...                ...               ...                        ...   \n",
              "8755               2.6              1894                      -10.3   \n",
              "8756               2.3              2000                       -9.9   \n",
              "8757               0.3              1968                       -9.9   \n",
              "8758               1.0              1859                       -9.8   \n",
              "8759               1.3              1909                       -9.3   \n",
              "\n",
              "      Solar Radiation (MJ/m2)  Rainfall(mm)  Snowfall (cm) Seasons  \\\n",
              "0                         0.0           0.0            0.0  Winter   \n",
              "1                         0.0           0.0            0.0  Winter   \n",
              "2                         0.0           0.0            0.0  Winter   \n",
              "3                         0.0           0.0            0.0  Winter   \n",
              "4                         0.0           0.0            0.0  Winter   \n",
              "...                       ...           ...            ...     ...   \n",
              "8755                      0.0           0.0            0.0  Autumn   \n",
              "8756                      0.0           0.0            0.0  Autumn   \n",
              "8757                      0.0           0.0            0.0  Autumn   \n",
              "8758                      0.0           0.0            0.0  Autumn   \n",
              "8759                      0.0           0.0            0.0  Autumn   \n",
              "\n",
              "         Holiday Functioning Day  \n",
              "0     No Holiday             Yes  \n",
              "1     No Holiday             Yes  \n",
              "2     No Holiday             Yes  \n",
              "3     No Holiday             Yes  \n",
              "4     No Holiday             Yes  \n",
              "...          ...             ...  \n",
              "8755  No Holiday             Yes  \n",
              "8756  No Holiday             Yes  \n",
              "8757  No Holiday             Yes  \n",
              "8758  No Holiday             Yes  \n",
              "8759  No Holiday             Yes  \n",
              "\n",
              "[8760 rows x 14 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bike_share_data = (\n",
        "    pd\n",
        "    .read_csv(\n",
        "        'https://archive.ics.uci.edu/ml/machine-learning-databases/00560/SeoulBikeData.csv', \n",
        "        encoding='latin1'\n",
        "    )\n",
        ")\n",
        "bike_share_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Simple Data Visulizations\n",
        "\n",
        "* Start with the table above\n",
        "* Create Histograms for numeric columns\n",
        "* Plot the histograms"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "(\n",
        "    bike_share_data\n",
        "    [['Rented Bike Count']]\n",
        "    .plot(\n",
        "        kind='hist', \n",
        "        alpha=0.5, \n",
        "        title='Rented Bike Count'\n",
        "    )\n",
        ");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "(\n",
        "    bike_share_data\n",
        "    [['Temperature(°C)']]\n",
        "    .plot(\n",
        "        kind='hist', \n",
        "        alpha=0.5, \n",
        "        title='Temperature(°C)'\n",
        "    )\n",
        ");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Creating bins with _Cut_\n",
        "\n",
        "* Start with the table above\n",
        "* Focus on the temperature column\n",
        "* Create 5 bins"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0       (-6.36, 5.08]\n",
              "1       (-6.36, 5.08]\n",
              "2       (-6.36, 5.08]\n",
              "3       (-6.36, 5.08]\n",
              "4       (-6.36, 5.08]\n",
              "            ...      \n",
              "8755    (-6.36, 5.08]\n",
              "8756    (-6.36, 5.08]\n",
              "8757    (-6.36, 5.08]\n",
              "8758    (-6.36, 5.08]\n",
              "8759    (-6.36, 5.08]\n",
              "Name: Temperature(°C), Length: 8760, dtype: category\n",
              "Categories (5, interval[float64, right]): [(-17.857, -6.36] < (-6.36, 5.08] < (5.08, 16.52] < (16.52, 27.96] < (27.96, 39.4]]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(\n",
        "    pd\n",
        "    .cut(\n",
        "        bike_share_data\n",
        "        ['Temperature(°C)'], \n",
        "        5\n",
        "    )  \n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We see the interval fo each of the bins:\n",
        "`[(-17.857, -6.36] < (-6.36, 5.08] < (5.08, 16.52] < (16.52, 27.96] < (27.96, 39.4]]` that are in order and split to more or less equal sizes from temperature values perspective. Let's see how well they split the data:\n",
        "\n",
        "* Split the temperature value into 5 bins\n",
        "* Count the number of records in each bin\n",
        "* Sort the values by the temperature range (the index of the series)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(-17.857, -6.36]     529\n",
              "(-6.36, 5.08]       1989\n",
              "(5.08, 16.52]       2428\n",
              "(16.52, 27.96]      2923\n",
              "(27.96, 39.4]        891\n",
              "Name: Temperature(°C), dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(\n",
        "    pd\n",
        "    .cut(\n",
        "        bike_share_data\n",
        "        ['Temperature(°C)'], \n",
        "        5\n",
        "    )\n",
        "    .value_counts()\n",
        "    .sort_index()\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "First, we see that the bins are in order when we sort them, and not by alphabetic order. More importanty, we see that they are not equal in size of records, and not based on any other meaningful split.\n",
        "\n",
        "### Setting the bins' limits\n",
        "\n",
        "We can set the bins to be more meaningful by setting the limits explicitly. As experts in bicycles, we might know the temperature ranges that are suitable for different accessories and clothings. Let's fix the ranges based on this domain knowledge:\n",
        "\n",
        "* Split the temperature value into 5 bins based on bicycle professional ranges\n",
        "* Count the number of records in each bin\n",
        "* Sort the values by the temperature range (the index of the series)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(-18, 0]    1454\n",
              "(0, 8]      1731\n",
              "(8, 16]     1651\n",
              "(16, 24]    2148\n",
              "(24, 40]    1776\n",
              "Name: Temperature(°C), dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(\n",
        "    pd\n",
        "    .cut(\n",
        "        bike_share_data\n",
        "        ['Temperature(°C)'], \n",
        "        bins=[-18, 0, 8, 16, 24, 40]\n",
        "    )\n",
        "    .value_counts()\n",
        "    .sort_index()\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Adding meaningful labels\n",
        "\n",
        "* Create a new column in the table for the temperature ranges\n",
        "* Split the temperature value into 5 bins based on bicycle professional ranges\n",
        "* Count the number of records in each bin\n",
        "* Add human readable labels to the bins\n",
        "* Sort the values by the temperature range (the index of the series)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Below Freezing    1454\n",
              "Freezing          1731\n",
              "Cold              1651\n",
              "Warm              2148\n",
              "Sizzling          1776\n",
              "Name: Temperature Range, dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bike_share_data['Temperature Range'] = (\n",
        "    pd\n",
        "    .cut(\n",
        "        bike_share_data\n",
        "        ['Temperature(°C)'], \n",
        "        bins=[-18, 0, 8, 16, 24, 40],\n",
        "        labels=['Below Freezing', 'Freezing', 'Cold', 'Warm','Sizzling']\n",
        "    )\n",
        "    \n",
        ")\n",
        "(\n",
        "    bike_share_data\n",
        "    ['Temperature Range']\n",
        "    .value_counts()\n",
        "    .sort_index()\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We see that the order is still the right order and not alphabetical, and the split is more balanced in the number of records.\n",
        "\n",
        "### Boxplot for each bin\n",
        "\n",
        "Now we can take every group of records and calculate and plot their box-plot showing the mean and the different quantiles of the different groups."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.7/site-packages/numpy/core/_asarray.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "(\n",
        "    bike_share_data\n",
        "    [['Rented Bike Count','Temperature Range']]\n",
        "    .boxplot(by='Temperature Range')\n",
        ");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## What is wrong with Historgram\n",
        "\n",
        "As much as historgrams are popular and simple to plot, they have many limitation:\n",
        "- It depends (too much) on the number of bins.\n",
        "- It depends (too much) on variable’s maximum and minimum.\n",
        "- It doesn’t allow to detect relevant values.\n",
        "- It doesn’t allow to discern continuous from discrete variables.\n",
        "- It makes it hard to compare distributions.\n",
        "\n",
        "We will use a couple of other plot option to see the data distributions more accurately.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Kernel Density Estimator (KDE)\n",
        "\n",
        "This method is calculating and plotting the estimation of the data distribution, and it is part of the Pandas built-in functions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "(\n",
        "    bike_share_data\n",
        "    ['Rented Bike Count']\n",
        "    .plot\n",
        "    .kde(\n",
        "        title='Rented Bike Count',\n",
        "        grid=True\n",
        "    )\n",
        ");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "(\n",
        "    bike_share_data\n",
        "    ['Temperature(°C)']\n",
        "    .plot\n",
        "    .kde(\n",
        "        title='Temperature(°C)', \n",
        "        grid=True\n",
        "    )\n",
        ");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Cumulative Distribution Function (CDF)\n",
        "\n",
        "The second option is using counts of the different percentiles of the data and using a cumulative plot makes it easy to find the value of each percentile and calculate the percentage of data points between every given percentiles."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
            "  from cryptography.utils import int_from_bytes\n",
            "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
            "  from cryptography.utils import int_from_bytes\n",
            "Requirement already satisfied: statsmodels in /opt/conda/lib/python3.7/site-packages (0.11.0)\n",
            "Requirement already satisfied: scipy>=1.0 in /opt/conda/lib/python3.7/site-packages (from statsmodels) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.14 in /opt/conda/lib/python3.7/site-packages (from statsmodels) (1.20.3)\n",
            "Requirement already satisfied: pandas>=0.21 in /opt/conda/lib/python3.7/site-packages (from statsmodels) (1.3.5)\n",
            "Requirement already satisfied: patsy>=0.5 in /opt/conda/lib/python3.7/site-packages (from statsmodels) (0.5.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.21->statsmodels) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.21->statsmodels) (2019.3)\n",
            "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from patsy>=0.5->statsmodels) (1.14.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install statsmodels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "from statsmodels.distributions.empirical_distribution import ECDF\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ecdf = ECDF(bike_share_data['Temperature(°C)'])\n",
        "plt.plot(ecdf.x, ecdf.y)\n",
        "plt.grid(True)\n",
        "plt.title('Temperature(°C)'); "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ecdf = ECDF(bike_share_data['Rented Bike Count'])\n",
        "plt.plot(ecdf.x, ecdf.y)\n",
        "plt.grid(True)\n",
        "plt.title('Rented Bike Count'); "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Group by Quantiles using _qcut_\n",
        "\n",
        "When using a target score such as grades or number of rentals, it makes sense to use the split using the Quantiles of the scores. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Low          2194\n",
              "Medium       2186\n",
              "High         2190\n",
              "Very High    2190\n",
              "Name: Usage Level, dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bike_share_data['Usage Level'] = (\n",
        "    pd\n",
        "    .qcut(\n",
        "        bike_share_data['Rented Bike Count'], \n",
        "        q=4,\n",
        "        labels=['Low', 'Medium', 'High', 'Very High']\n",
        "    )\n",
        ")\n",
        "(\n",
        "    bike_share_data\n",
        "    ['Usage Level']\n",
        "    .value_counts()\n",
        "    .sort_index()\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.7/site-packages/numpy/core/_asarray.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "(\n",
        "    bike_share_data\n",
        "    [['Temperature(°C)','Usage Level']]\n",
        "    .boxplot(by='Usage Level')\n",
        ");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Heat map with the new value group\n",
        "\n",
        "Now that we have the new value categories and we can create heat map to see the hours of the day that are the peak hours that can be used with higher price tiers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.heatmap(\n",
        "    bike_share_data\n",
        "    [['Usage Level','Hour','Date']]\n",
        "    .pivot_table(\n",
        "        index='Hour', \n",
        "        columns='Usage Level',\n",
        "        aggfunc='count'\n",
        "    )\n",
        "    .droplevel(0, axis='columns'),\n",
        "    cmap=\"YlOrRd\"\n",
        ");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "instance_type": "ml.t3.medium",
    "kernelspec": {
      "display_name": "python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}