Canonical categorical variables in polars

canonical-categorical-variables-in-polars.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87a6d1d1-dc10-4660-879a-42038201518a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from string import ascii_uppercase\n",
    "\n",
    "import polars as pl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from plotnine import *\n",
    "\n",
    "theme_set(\n",
    "    theme_matplotlib()\n",
    "    + theme(\n",
    "        legend_position=(1, 1),\n",
    "        legend_background=element_rect(color=\"black\"),\n",
    "        legend_margin=5\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f928974d-c833-40b5-b29e-b12e4e7b60eb",
   "metadata": {},
   "source": [
    "Create some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3422234e-c455-4cc8-9ebc-ab44fd6c2c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (400, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>title</th><th>rating</th><th>genre</th></tr><tr><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;BICNYD&quot;</td><td>&quot;very good&quot;</td><td>&quot;thriller&quot;</td></tr><tr><td>&quot;MRYKWB&quot;</td><td>&quot;bad&quot;</td><td>&quot;drama&quot;</td></tr><tr><td>&quot;KWHFAN&quot;</td><td>&quot;very good&quot;</td><td>&quot;thriller&quot;</td></tr><tr><td>&quot;MCAKOD&quot;</td><td>&quot;excellent&quot;</td><td>&quot;drama&quot;</td></tr><tr><td>&quot;EZAJNS&quot;</td><td>&quot;good&quot;</td><td>&quot;action&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;TZOAFM&quot;</td><td>&quot;bad&quot;</td><td>&quot;drama&quot;</td></tr><tr><td>&quot;ODSIWX&quot;</td><td>&quot;good&quot;</td><td>&quot;comedy&quot;</td></tr><tr><td>&quot;BVGDUH&quot;</td><td>&quot;good&quot;</td><td>&quot;thriller&quot;</td></tr><tr><td>&quot;KLGHPI&quot;</td><td>&quot;very good&quot;</td><td>&quot;drama&quot;</td></tr><tr><td>&quot;HNSDPF&quot;</td><td>&quot;bad&quot;</td><td>&quot;drama&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (400, 3)\n",
       "┌────────┬───────────┬──────────┐\n",
       "│ title  ┆ rating    ┆ genre    │\n",
       "│ ---    ┆ ---       ┆ ---      │\n",
       "│ str    ┆ str       ┆ str      │\n",
       "╞════════╪═══════════╪══════════╡\n",
       "│ BICNYD ┆ very good ┆ thriller │\n",
       "│ MRYKWB ┆ bad       ┆ drama    │\n",
       "│ KWHFAN ┆ very good ┆ thriller │\n",
       "│ MCAKOD ┆ excellent ┆ drama    │\n",
       "│ EZAJNS ┆ good      ┆ action   │\n",
       "│ …      ┆ …         ┆ …        │\n",
       "│ TZOAFM ┆ bad       ┆ drama    │\n",
       "│ ODSIWX ┆ good      ┆ comedy   │\n",
       "│ BVGDUH ┆ good      ┆ thriller │\n",
       "│ KLGHPI ┆ very good ┆ drama    │\n",
       "│ HNSDPF ┆ bad       ┆ drama    │\n",
       "└────────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.seed(123)\n",
    "\n",
    "ratings = [\"very bad\", \"bad\", \"good\", \"very good\", \"excellent\"]\n",
    "ratings_weights = [.1, .2, .4, .2, .1]\n",
    "genres = [\"drama\", \"action\", \"comedy\", \"thriller\", \"animation\"]\n",
    "genres_weights = [.4, .25, .2, .1, .05]\n",
    "\n",
    "def make_movie():\n",
    "    return {\n",
    "        \"title\": \"\".join(random.sample(ascii_uppercase, k=6)),\n",
    "        \"rating\": random.choices(ratings, ratings_weights)[0],\n",
    "        \"genre\": random.choices(genres, genres_weights)[0]\n",
    "    }\n",
    "\n",
    "\n",
    "n = 400\n",
    "\n",
    "# For polars using categoricals is not helpful\n",
    "movies =  pl.DataFrame([make_movie() for i in range(n)])\n",
    "\n",
    "# pandas version with the canonical categorical types\n",
    "movies_pd = movies.to_pandas()\n",
    "movies_pd[\"genre\"] = movies_pd[\"genre\"].astype(pd.CategoricalDtype(genres))\n",
    "movies_pd[\"rating\"] = movies_pd[\"rating\"].astype(pd.CategoricalDtype(ratings, ordered=True))\n",
    "\n",
    "movies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248cf91d-0458-414e-afef-aefe050f7c50",
   "metadata": {},
   "source": [
    "Functions to create visualisations of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "28a5368c-73cb-4f9a-a45b-3a55a4ad16ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualise(data):\n",
    "    return (\n",
    "        ggplot(data, aes(\"rating\", fill=\"rating\"))\n",
    "        + geom_bar()\n",
    "        + scale_y_continuous(expand=(0, 0, .1, 0))\n",
    "    )\n",
    "\n",
    "def visualise_facetted(data):\n",
    "    return (\n",
    "        ggplot(data, aes(\"rating\", fill=\"rating\"))\n",
    "        + geom_bar()\n",
    "        + facet_wrap(\"genre\", nrow=1)\n",
    "        + scale_x_discrete(labels=lambda s: [x.replace(\" \", \"\\n\") for x in s])\n",
    "        + scale_y_continuous(expand=(0, 0, .1, 0))\n",
    "        + theme(\n",
    "            figure_size=(10, 5),\n",
    "            axis_text=element_text(size=6),\n",
    "            legend_text=element_text(size=6),\n",
    "            legend_key_size=12,\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1dce197-f800-4482-85fc-b3e18c11da8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise(movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "882ea691-94c5-4cd4-87b2-23c588cb6e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 1000
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise_facetted(movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a3a682d-99e5-41dd-b7dd-9ef3187c1612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise(movies_pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "375786bf-664a-465d-b09a-230200216ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 1000
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise_facetted(movies_pd)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}