pandas/boosted_value_counts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([\n",
    "    random.choice([\"a\", \"b\", \"c\", \"d\", np.nan]) for _ in range(1_000)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      218\n",
       "b      203\n",
       "NaN    197\n",
       "d      192\n",
       "c      190\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      0.218\n",
       "b      0.203\n",
       "NaN    0.197\n",
       "d      0.192\n",
       "c      0.190\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts(normalize=True, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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     ]
    }
   ],
   "source": [
    "def vc(\n",
    "    self,\n",
    "    sort: bool = True,\n",
    "    ascending: bool = False,\n",
    "    bins=None,\n",
    "    dropna: bool = False,\n",
    "):\n",
    "    \"\"\"\n",
    "    Returns value-counts with both counts and normalized counts.\n",
    "    The default value of dropna has been set to False.\n",
    "    \"\"\"\n",
    "    return pd.concat(\n",
    "        [\n",
    "            self.value_counts(sort=sort, ascending=ascending, dropna=dropna, bins=bins),\n",
    "            self.value_counts(\n",
    "                normalize=True, sort=sort, ascending=ascending, dropna=dropna, bins=bins\n",
    "            ),\n",
    "        ],\n",
    "        axis=1,\n",
    "        keys=(\"count\", \"normalized\"),\n",
    "    )\n",
    "\n",
    "\n",
    "pd.Series.vc = vc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>normalized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>218</td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>203</td>\n",
       "      <td>0.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>197</td>\n",
       "      <td>0.197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>192</td>\n",
       "      <td>0.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>190</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count  normalized\n",
       "a      218       0.218\n",
       "b      203       0.203\n",
       "NaN    197       0.197\n",
       "d      192       0.192\n",
       "c      190       0.190"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.vc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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