data-analysis/026-pandas-cut/pandas-cut.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas cut()\n",
    "\n",
    "This is a notebook for the medium article [All Pandas cut() you should know for transforming numerical data into categorical data](https://bindichen.medium.com/all-pandas-cut-you-should-know-for-transforming-numerical-data-into-categorical-data-1370cf7f4c4f)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "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": [],
   "source": [
    "df = pd.DataFrame({'age': [2, 67, 40, 32, 4, 15, 82, 99, 26, 30]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>30</td>\n",
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      "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"
      ]
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Discretizing into equal-sized bins with `bins=int`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Discretize into three equal-sized bins.\n",
    "df['age_group'] = pd.cut(df['age'], 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>7</th>\n",
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       "   age         age_group\n",
       "0    2   (1.903, 34.333]\n",
       "1   67    (66.667, 99.0]\n",
       "2   40  (34.333, 66.667]\n",
       "3   32   (1.903, 34.333]\n",
       "4    4   (1.903, 34.333]\n",
       "5   15   (1.903, 34.333]\n",
       "6   82    (66.667, 99.0]\n",
       "7   99    (66.667, 99.0]\n",
       "8   26   (1.903, 34.333]\n",
       "9   30   (1.903, 34.333]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     (1.903, 34.333]\n",
       "1      (66.667, 99.0]\n",
       "2    (34.333, 66.667]\n",
       "3     (1.903, 34.333]\n",
       "4     (1.903, 34.333]\n",
       "5     (1.903, 34.333]\n",
       "6      (66.667, 99.0]\n",
       "7      (66.667, 99.0]\n",
       "8     (1.903, 34.333]\n",
       "9     (1.903, 34.333]\n",
       "Name: age_group, dtype: category\n",
       "Categories (3, interval[float64]): [(1.903, 34.333] < (34.333, 66.667] < (66.667, 99.0]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Adding custom bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age_group'] = pd.cut(df['age'], bins=[0, 12, 19, 61, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "   age  age_group\n",
       "0    2    (0, 12]\n",
       "1   67  (61, 100]\n",
       "2   40   (19, 61]\n",
       "3   32   (19, 61]\n",
       "4    4    (0, 12]\n",
       "5   15   (12, 19]\n",
       "6   82  (61, 100]\n",
       "7   99  (61, 100]\n",
       "8   26   (19, 61]\n",
       "9   30   (19, 61]"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (0, 12]\n",
       "1    (61, 100]\n",
       "2     (19, 61]\n",
       "3     (19, 61]\n",
       "4      (0, 12]\n",
       "5     (12, 19]\n",
       "6    (61, 100]\n",
       "7    (61, 100]\n",
       "8     (19, 61]\n",
       "9     (19, 61]\n",
       "Name: age_group, dtype: category\n",
       "Categories (4, interval[int64]): [(0, 12] < (12, 19] < (19, 61] < (61, 100]]"
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['age_group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>67</td>\n",
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       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>(61, 100]</td>\n",
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       "      <th>7</th>\n",
       "      <td>99</td>\n",
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      "text/plain": [
       "   age  age_group\n",
       "0    2    (0, 12]\n",
       "4    4    (0, 12]\n",
       "5   15   (12, 19]\n",
       "2   40   (19, 61]\n",
       "3   32   (19, 61]\n",
       "8   26   (19, 61]\n",
       "9   30   (19, 61]\n",
       "1   67  (61, 100]\n",
       "6   82  (61, 100]\n",
       "7   99  (61, 100]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by age_group\n",
    "df.sort_values('age_group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 12]      2\n",
       "(12, 19]     1\n",
       "(19, 61]     4\n",
       "(61, 100]    3\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Adding labels to bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=[0, 12, 19, 61, 100]\n",
    "labels=['<12', 'Teen', 'Adult', 'Older']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   age age_group\n",
       "0    2       <12\n",
       "1   67     Older\n",
       "2   40     Adult\n",
       "3   32     Adult\n",
       "4    4       <12\n",
       "5   15      Teen\n",
       "6   82     Older\n",
       "7   99     Older\n",
       "8   26     Adult\n",
       "9   30     Adult"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group'] = pd.cut(df['age'], bins, labels=labels)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      <12\n",
       "1    Older\n",
       "2    Adult\n",
       "3    Adult\n",
       "4      <12\n",
       "5     Teen\n",
       "6    Older\n",
       "7    Older\n",
       "8    Adult\n",
       "9    Adult\n",
       "Name: age_group, dtype: category\n",
       "Categories (4, object): ['<12' < 'Teen' < 'Adult' < 'Older']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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   "source": [
    "df['age_group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "    }\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>&lt;12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>&lt;12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>Teen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>Older</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>Older</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "      <td>Older</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age age_group\n",
       "0    2       <12\n",
       "4    4       <12\n",
       "5   15      Teen\n",
       "2   40     Adult\n",
       "3   32     Adult\n",
       "8   26     Adult\n",
       "9   30     Adult\n",
       "1   67     Older\n",
       "6   82     Older\n",
       "7   99     Older"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('age_group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<12      2\n",
       "Teen     1\n",
       "Adult    4\n",
       "Older    3\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Configuring leftmost edge or rightmost edge with `right=False`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      [0, 12)\n",
       "1    [61, 100)\n",
       "2     [19, 61)\n",
       "3     [19, 61)\n",
       "4      [0, 12)\n",
       "5     [12, 19)\n",
       "6    [61, 100)\n",
       "7    [61, 100)\n",
       "8     [19, 61)\n",
       "9     [19, 61)\n",
       "Name: age, dtype: category\n",
       "Categories (4, interval[int64]): [[0, 12) < [12, 19) < [19, 61) < [61, 100)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(df['age'], bins=[0, 12, 19, 61, 100], right=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Including the lowest value with `include_lowest=True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>(61.0, 100.0]</td>\n",
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       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>(2.0, 12.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>(12.0, 19.0]</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>(61.0, 100.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "      <td>(61.0, 100.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age      age_group\n",
       "0    2            NaN\n",
       "1   67  (61.0, 100.0]\n",
       "2   40   (19.0, 61.0]\n",
       "3   32   (19.0, 61.0]\n",
       "4    4    (2.0, 12.0]\n",
       "5   15   (12.0, 19.0]\n",
       "6   82  (61.0, 100.0]\n",
       "7   99  (61.0, 100.0]\n",
       "8   26   (19.0, 61.0]\n",
       "9   30   (19.0, 61.0]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group'] = pd.cut(df['age'], bins=[2, 12, 19, 61, 100])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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, 12.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>(61.0, 100.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>(1.999, 12.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>(12.0, 19.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>(61.0, 100.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "      <td>(61.0, 100.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age      age_group\n",
       "0    2  (1.999, 12.0]\n",
       "1   67  (61.0, 100.0]\n",
       "2   40   (19.0, 61.0]\n",
       "3   32   (19.0, 61.0]\n",
       "4    4  (1.999, 12.0]\n",
       "5   15   (12.0, 19.0]\n",
       "6   82  (61.0, 100.0]\n",
       "7   99  (61.0, 100.0]\n",
       "8   26   (19.0, 61.0]\n",
       "9   30   (19.0, 61.0]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group'] = pd.cut(\n",
    "    df['age'], \n",
    "    bins=[2, 12, 19, 61, 100], \n",
    "    include_lowest=True\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Passing an IntervalIndex to bins \n",
    "Passing an IntervalIndex for bins results in those categories exactly. Notice that values not covered by the IntervalIndex are set to NaN. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = pd.IntervalIndex.from_tuples([(0, 12), (19, 61), (61, 100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IntervalIndex([(0, 12], (19, 61], (61, 100]],\n",
       "              closed='right',\n",
       "              dtype='interval[int64]')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age_group'] = pd.cut(df['age'], bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>(0.0, 12.0]</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>(61.0, 100.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>(0.0, 12.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>82</td>\n",
       "      <td>(61.0, 100.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>99</td>\n",
       "      <td>(61.0, 100.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>(19.0, 61.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age      age_group\n",
       "0    2    (0.0, 12.0]\n",
       "1   67  (61.0, 100.0]\n",
       "2   40   (19.0, 61.0]\n",
       "3   32   (19.0, 61.0]\n",
       "4    4    (0.0, 12.0]\n",
       "5   15            NaN\n",
       "6   82  (61.0, 100.0]\n",
       "7   99  (61.0, 100.0]\n",
       "8   26   (19.0, 61.0]\n",
       "9   30   (19.0, 61.0]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Returning bins with `retbins=True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tt is useful when bins is passed as a single number value\n",
    "result, bins = pd.cut(\n",
    "    df['age'], \n",
    "    bins=4,     # A single number value\n",
    "    retbins=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.903, 26.25 , 50.5  , 74.75 , 99.   ])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Creating unordered categories with `ordered=False`\n",
    "\n",
    "New in version 1.1.0.\n",
    "\n",
    "`ordered=False` will result in unordered categories when labels are passed. This parameter can be used to allow non-unique labels: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age_group'] = pd.cut(\n",
    "    df['age'], \n",
    "    bins=[0, 12, 19, 61, 100], \n",
    "    labels=['<12', 'Teen', 'Adult', 'Older'], \n",
    "    ordered=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      <12\n",
       "1    Older\n",
       "2    Adult\n",
       "3    Adult\n",
       "4      <12\n",
       "5     Teen\n",
       "6    Older\n",
       "7    Older\n",
       "8    Adult\n",
       "9    Adult\n",
       "Name: age_group, dtype: category\n",
       "Categories (4, object): ['<12', 'Teen', 'Adult', 'Older']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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