data-analysis/041-pandas-qcut/pandas-qcut.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas `qcut()`\n",
    "\n",
    "This is a notebook for the medium article [All Pandas qcut() you should know for binning numerical data based on sample quantiles](https://bindichen.medium.com/all-pandas-qcut-you-should-know-for-binning-numerical-data-based-on-sample-quantiles-c8b13a8ed844)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
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       "      <td>2</td>\n",
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       "      <td>67</td>\n",
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       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age\n",
       "0     2\n",
       "1    67\n",
       "2    40\n",
       "3    32\n",
       "4     4\n",
       "5    15\n",
       "6    82\n",
       "7    99\n",
       "8    26\n",
       "9    30\n",
       "10   50\n",
       "11   78"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'age': [2, 67, 40, 32, 4, 15, 82, 99, 26, 30, 50, 78]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.729324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.750000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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       "             age\n",
       "count  12.000000\n",
       "mean   43.750000\n",
       "std    31.729324\n",
       "min     2.000000\n",
       "25%    23.250000\n",
       "50%    36.000000\n",
       "75%    69.750000\n",
       "max    99.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Discretizing into equal-sized buckets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'age': [2, 67, 40, 32, 4, 15, 82, 99, 26, 30, 50, 78]})\n",
    "\n",
    "df['age_group'] = pd.qcut(df['age'], 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>(1.999, 28.667]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>(55.667, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>(28.667, 55.667]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>(28.667, 55.667]</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>(1.999, 28.667]</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>(1.999, 28.667]</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>(55.667, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "      <td>(55.667, 99.0]</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>(1.999, 28.667]</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>(28.667, 55.667]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>(28.667, 55.667]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>78</td>\n",
       "      <td>(55.667, 99.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age         age_group\n",
       "0     2   (1.999, 28.667]\n",
       "1    67    (55.667, 99.0]\n",
       "2    40  (28.667, 55.667]\n",
       "3    32  (28.667, 55.667]\n",
       "4     4   (1.999, 28.667]\n",
       "5    15   (1.999, 28.667]\n",
       "6    82    (55.667, 99.0]\n",
       "7    99    (55.667, 99.0]\n",
       "8    26   (1.999, 28.667]\n",
       "9    30  (28.667, 55.667]\n",
       "10   50  (28.667, 55.667]\n",
       "11   78    (55.667, 99.0]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.999, 28.667]     4\n",
       "(28.667, 55.667]    4\n",
       "(55.667, 99.0]      4\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (1.999, 28.667]\n",
       "1       (55.667, 99.0]\n",
       "2     (28.667, 55.667]\n",
       "3     (28.667, 55.667]\n",
       "4      (1.999, 28.667]\n",
       "5      (1.999, 28.667]\n",
       "6       (55.667, 99.0]\n",
       "7       (55.667, 99.0]\n",
       "8      (1.999, 28.667]\n",
       "9     (28.667, 55.667]\n",
       "10    (28.667, 55.667]\n",
       "11      (55.667, 99.0]\n",
       "Name: age_group, dtype: category\n",
       "Categories (3, interval[float64]): [(1.999, 28.667] < (28.667, 55.667] < (55.667, 99.0]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look at the result\n",
    "df['age_group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>(1.999, 28.667]</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>(28.667, 55.667]</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>(28.667, 55.667]</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>(28.667, 55.667]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>(28.667, 55.667]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>(55.667, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>(55.667, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "      <td>(55.667, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>78</td>\n",
       "      <td>(55.667, 99.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age         age_group\n",
       "0     2   (1.999, 28.667]\n",
       "4     4   (1.999, 28.667]\n",
       "5    15   (1.999, 28.667]\n",
       "8    26   (1.999, 28.667]\n",
       "2    40  (28.667, 55.667]\n",
       "3    32  (28.667, 55.667]\n",
       "9    30  (28.667, 55.667]\n",
       "10   50  (28.667, 55.667]\n",
       "1    67    (55.667, 99.0]\n",
       "6    82    (55.667, 99.0]\n",
       "7    99    (55.667, 99.0]\n",
       "11   78    (55.667, 99.0]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('age_group')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Discretizing into buckets with a list of quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>age_group</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>(1.999, 5.1]</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>(1.999, 5.1]</td>\n",
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       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>(5.1, 36.0]</td>\n",
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       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>(5.1, 36.0]</td>\n",
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       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>(5.1, 36.0]</td>\n",
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       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>(5.1, 36.0]</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>(36.0, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>(36.0, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>(36.0, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "      <td>(36.0, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>(36.0, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>78</td>\n",
       "      <td>(36.0, 99.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     age_group\n",
       "0     2  (1.999, 5.1]\n",
       "4     4  (1.999, 5.1]\n",
       "3    32   (5.1, 36.0]\n",
       "5    15   (5.1, 36.0]\n",
       "8    26   (5.1, 36.0]\n",
       "9    30   (5.1, 36.0]\n",
       "1    67  (36.0, 99.0]\n",
       "2    40  (36.0, 99.0]\n",
       "6    82  (36.0, 99.0]\n",
       "7    99  (36.0, 99.0]\n",
       "10   50  (36.0, 99.0]\n",
       "11   78  (36.0, 99.0]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group'] = pd.qcut(df['age'], [0, .1, .5, 1])\n",
    "df.sort_values('age_group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36.0, 99.0]    6\n",
       "(5.1, 36.0]     4\n",
       "(1.999, 5.1]    2\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('age_group')['age_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same as pd.qcut(df['age'], 4)\n",
    "df['age_group'] = pd.qcut(df['age'], [0, .25, .5, .75, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>(1.999, 23.25]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>(36.0, 69.75]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>(36.0, 69.75]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>(23.25, 36.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>(1.999, 23.25]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>(1.999, 23.25]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>(69.75, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "      <td>(69.75, 99.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>(23.25, 36.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>(23.25, 36.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>(36.0, 69.75]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>78</td>\n",
       "      <td>(69.75, 99.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age       age_group\n",
       "0     2  (1.999, 23.25]\n",
       "1    67   (36.0, 69.75]\n",
       "2    40   (36.0, 69.75]\n",
       "3    32   (23.25, 36.0]\n",
       "4     4  (1.999, 23.25]\n",
       "5    15  (1.999, 23.25]\n",
       "6    82   (69.75, 99.0]\n",
       "7    99   (69.75, 99.0]\n",
       "8    26   (23.25, 36.0]\n",
       "9    30   (23.25, 36.0]\n",
       "10   50   (36.0, 69.75]\n",
       "11   78   (69.75, 99.0]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     (1.999, 23.25]\n",
       "1      (36.0, 69.75]\n",
       "2      (36.0, 69.75]\n",
       "3      (23.25, 36.0]\n",
       "4     (1.999, 23.25]\n",
       "5     (1.999, 23.25]\n",
       "6      (69.75, 99.0]\n",
       "7      (69.75, 99.0]\n",
       "8      (23.25, 36.0]\n",
       "9      (23.25, 36.0]\n",
       "10     (36.0, 69.75]\n",
       "11     (69.75, 99.0]\n",
       "Name: age_group, dtype: category\n",
       "Categories (4, interval[float64]): [(1.999, 23.25] < (23.25, 36.0] < (36.0, 69.75] < (69.75, 99.0]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.999, 23.25]    3\n",
       "(23.25, 36.0]     3\n",
       "(36.0, 69.75]     3\n",
       "(69.75, 99.0]     3\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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",
       "        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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.729324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age\n",
       "count  12.000000\n",
       "mean   43.750000\n",
       "std    31.729324\n",
       "min     2.000000\n",
       "25%    23.250000\n",
       "50%    36.000000\n",
       "75%    69.750000\n",
       "max    99.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Adding custom labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=['Millennial', 'Gen X', 'Boomer', 'Greatest']\n",
    "df['age_group'] = pd.qcut(df['age'], [0, .1, 0.3, .6, 1], labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>age</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Millennial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>Greatest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>Boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>Boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Millennial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>Gen X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>Greatest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "      <td>Greatest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>Gen X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>Boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>Greatest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>78</td>\n",
       "      <td>Greatest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age   age_group\n",
       "0     2  Millennial\n",
       "1    67    Greatest\n",
       "2    40      Boomer\n",
       "3    32      Boomer\n",
       "4     4  Millennial\n",
       "5    15       Gen X\n",
       "6    82    Greatest\n",
       "7    99    Greatest\n",
       "8    26       Gen X\n",
       "9    30      Boomer\n",
       "10   50    Greatest\n",
       "11   78    Greatest"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Millennial\n",
       "1       Greatest\n",
       "2         Boomer\n",
       "3         Boomer\n",
       "4     Millennial\n",
       "5          Gen X\n",
       "6       Greatest\n",
       "7       Greatest\n",
       "8          Gen X\n",
       "9         Boomer\n",
       "10      Greatest\n",
       "11      Greatest\n",
       "Name: age_group, dtype: category\n",
       "Categories (4, object): ['Millennial' < 'Gen X' < 'Boomer' < 'Greatest']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>age</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Millennial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Millennial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>Gen X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>Gen X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>Boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>Boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>Boomer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>Greatest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>Greatest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "      <td>Greatest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>Greatest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>78</td>\n",
       "      <td>Greatest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age   age_group\n",
       "0     2  Millennial\n",
       "4     4  Millennial\n",
       "5    15       Gen X\n",
       "8    26       Gen X\n",
       "2    40      Boomer\n",
       "3    32      Boomer\n",
       "9    30      Boomer\n",
       "1    67    Greatest\n",
       "6    82    Greatest\n",
       "7    99    Greatest\n",
       "10   50    Greatest\n",
       "11   78    Greatest"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('age_group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Millennial    2\n",
       "Gen X         2\n",
       "Boomer        3\n",
       "Greatest      5\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Returning bins with retbins=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# It is useful when q is passed as a single number value\n",
    "result, bins = pd.qcut(\n",
    "    df['age'], \n",
    "    5,                  # A single number value\n",
    "    retbins=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2. , 17.2, 30.8, 46. , 75.8, 99. ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Configuring the bin precision with `precision`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (1.999, 28.667]\n",
       "1       (55.667, 99.0]\n",
       "2     (28.667, 55.667]\n",
       "3     (28.667, 55.667]\n",
       "4      (1.999, 28.667]\n",
       "5      (1.999, 28.667]\n",
       "6       (55.667, 99.0]\n",
       "7       (55.667, 99.0]\n",
       "8      (1.999, 28.667]\n",
       "9     (28.667, 55.667]\n",
       "10    (28.667, 55.667]\n",
       "11      (55.667, 99.0]\n",
       "Name: age, dtype: category\n",
       "Categories (3, interval[float64]): [(1.999, 28.667] < (28.667, 55.667] < (55.667, 99.0]]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You may notice all the bin interval values \n",
    "# we have made so far have some decimal points, for example\n",
    "pd.qcut(df['age'], 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (1.0, 29.0]\n",
       "1     (56.0, 99.0]\n",
       "2     (29.0, 56.0]\n",
       "3     (29.0, 56.0]\n",
       "4      (1.0, 29.0]\n",
       "5      (1.0, 29.0]\n",
       "6     (56.0, 99.0]\n",
       "7     (56.0, 99.0]\n",
       "8      (1.0, 29.0]\n",
       "9     (29.0, 56.0]\n",
       "10    (29.0, 56.0]\n",
       "11    (56.0, 99.0]\n",
       "Name: age, dtype: category\n",
       "Categories (3, interval[float64]): [(1.0, 29.0] < (29.0, 56.0] < (56.0, 99.0]]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can set the precision to 0 to avoid any decimal place.\n",
    "pd.qcut(df['age'], 3, precision=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (1.9, 28.7]\n",
       "1     (55.7, 99.0]\n",
       "2     (28.7, 55.7]\n",
       "3     (28.7, 55.7]\n",
       "4      (1.9, 28.7]\n",
       "5      (1.9, 28.7]\n",
       "6     (55.7, 99.0]\n",
       "7     (55.7, 99.0]\n",
       "8      (1.9, 28.7]\n",
       "9     (28.7, 55.7]\n",
       "10    (28.7, 55.7]\n",
       "11    (55.7, 99.0]\n",
       "Name: age, dtype: category\n",
       "Categories (3, interval[float64]): [(1.9, 28.7] < (28.7, 55.7] < (55.7, 99.0]]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for 1 decimal place.\n",
    "pd.qcut(df['age'], 3, precision=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thanks for reading\n",
    "\n",
    "This is a notebook for the medium article [All Pandas qcut() you should know for binning numerical data based on sample quantiles](https://bindichen.medium.com/all-pandas-qcut-you-should-know-for-binning-numerical-data-based-on-sample-quantiles-c8b13a8ed844)\n",
    "\n",
    "Please check out article for instructions"
   ]
  },
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   "metadata": {},
   "outputs": [],
   "source": []
  }
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