{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f131251f-fcb6-46c5-a0ca-2fbe204a0fe2",
   "metadata": {},
   "source": [
    "# Polars vs Pandas"
   ]
  },
  {
   "attachments": {
    "0b7753cc-a06f-4c07-a980-89e00d54d526.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "77192dd9-92a2-4ffe-9e86-7308c3441e73",
   "metadata": {},
   "source": [
    "![image.png](attachment:0b7753cc-a06f-4c07-a980-89e00d54d526.png)\n",
    "\n",
    "(Didn't know this was a thing before I googled \"Panda vs Polar\" accidently, Credits: [Panda & Polar Bear](https://www.pandaandpolarbear.com/))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad4eb9bb-09e0-4547-99f2-e5f276f69f8a",
   "metadata": {},
   "source": [
    "## Polars\n",
    "\n",
    "* [Python Docs](https://pola-rs.github.io/polars-book/user-guide/introduction.html)\n",
    "* [Github](https://github.com/pola-rs/polars)\n",
    "* [PyPI](https://pypi.org/project/polars/)\n",
    "* Features:\n",
    "    * Lazy & eager computation\n",
    "    * Rust implementation\n",
    "    * [Arrow](https://arrow.apache.org/) memory layout\n",
    "    * Easy and transparent parallelisation using multithreading\n",
    "    * PySpark-like syntax and thus heavily inspired by SQL\n",
    "    * Supports real NA values in contrast to Pandas\n",
    "    * Easily deal with complex data types, e.g. list of strings/floats\n",
    "    * Copy-On-Write (COW) semantics in contrast to Pandas where you kind of never know"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d5c010-10ed-492c-8d9e-edea0b21b8d8",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "\n",
    "* [Docs](https://pandas.pydata.org/)\n",
    "* [Github](https://github.com/pandas-dev/pandas)\n",
    "* [PyPI](https://pypi.org/project/pandas/)\n",
    "* Features:\n",
    "    * De facto standard data wrangling library for Python\n",
    "    * Multi-index for rows and columns\n",
    "    * Quite unrestricted in what's possible, e.g. everything hashable can be a column name like integers, floats, enums\n",
    "    * Tons of functionality\n",
    "    * No parallelization\n",
    "    * Built on top of Numpy\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5860c2-da49-4116-83d0-3a9fd5adf18b",
   "metadata": {},
   "source": [
    "# Other Contenders\n",
    "\n",
    "* [Vaex](https://github.com/vaexio/vaex): Lazy Out-of-Core dataframes\n",
    "* [Dask](https://docs.dask.org/): Built on top of Pandas and parallelizes it, on a single node or distributed.\n",
    "* [H2O Datatable](https://github.com/h2oai/datatable): Inspired by R's data.tables\n",
    "* [Modin](https://modin.readthedocs.io/en/latest/): Uses [Ray](https://docs.ray.io/) or [Dask](https://docs.dask.org/) to parallelize Pandas\n",
    "* [RAPIDS](https://rapids.ai/): Data analysis on GPU\n",
    "\n",
    "Performance [benchmark](https://h2oai.github.io/db-benchmark/) of various frameworks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb080c4-707a-40aa-b50a-63d321b59f2d",
   "metadata": {},
   "source": [
    "# Prelude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "faa9706b-9947-4bc0-a9b9-03d79bbe6d91",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "import numpy as np\n",
    "import polars as pl\n",
    "from pathlib import Path\n",
    "from polars import col, lit\n",
    "import pandas as pd\n",
    "from pandas.io.common import get_handle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87af4132-7bd3-4b57-a44d-aa46db56d594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.7.18'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7275368-d6aa-4894-b992-1d0c09ced85f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.4'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd0b13ef-bed2-4b3a-b311-bb58af2051a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download a huge csv as a test. Takes a while and only needed once...\n",
    "big_csv = Path(\"./big.csv\")\n",
    "csv_url = \"http://sdm.lbl.gov/fastbit/data/star2002-full.csv.gz\"\n",
    "\n",
    "if not big_csv.exists():\n",
    "    with get_handle(csv_url, compression=\"gzip\", mode=\"r\") as fh_in, open(big_csv, \"bw\") as fh_out:\n",
    "        fh_out.write(fh_in.handle.buffer.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72fbe42-002c-4807-bf42-f63861891b5a",
   "metadata": {},
   "source": [
    "## Eager Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9bdd5d61-e9c7-45ba-ac02-fe44d796f582",
   "metadata": {},
   "outputs": [],
   "source": [
    "edf = pl.read_csv(str(big_csv), has_headers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43458f9c-75c9-435a-87c6-46fe2285b5b0",
   "metadata": {},
   "outputs": [
    {
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       "1\n",
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      "text/plain": [
       "shape: (5, 1)\n",
       "╭──────────╮\n",
       "│ column_9 │\n",
       "│ ---      │\n",
       "│ i64      │\n",
       "╞══════════╡\n",
       "│ 654      │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 61       │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 7        │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 27       │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 1        │\n",
       "╰──────────╯"
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     "metadata": {},
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   ],
   "source": [
    "edf.filter(col(\"column_1\") == 1).select([\"column_9\"]).head()"
   ]
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  {
   "cell_type": "markdown",
   "id": "0571f577-9ee1-4654-a8f8-03e7b43a2ec1",
   "metadata": {},
   "source": [
    "#### alternatively *Pandas* style (not recommended!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f76e2c4b-c1f7-490a-9daf-970c55786ad6",
   "metadata": {},
   "outputs": [
    {
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       "27\n",
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       "<tr>\n",
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       "1\n",
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       "</tr>\n",
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      "text/plain": [
       "shape: (5, 1)\n",
       "╭──────────╮\n",
       "│ column_9 │\n",
       "│ ---      │\n",
       "│ i64      │\n",
       "╞══════════╡\n",
       "│ 654      │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 61       │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 7        │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 27       │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 1        │\n",
       "╰──────────╯"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "edf[edf[\"column_1\"] == 1][[\"column_9\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0539174-9bd1-4600-ab38-fbae1718b6bb",
   "metadata": {},
   "source": [
    "Why shouldn't I use the Pandas style? Because ...\n",
    "\n",
    "* it's much harder to read since it's not *operator chaining*,\n",
    "* it's more verbose if you assign actual variable names to your dataframes and not just use `df` all the time. Check out this filtering example: `agg_metric_df[agg_metric_df[\"metric_1\"] < 0.9]`. Using `col` to refer to the column of the current dataframe is much cleaner,\n",
    "* it's not possible to switch later from eager to lazy execution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa22ed17-2c93-4f53-8074-1aeb5ea3890b",
   "metadata": {},
   "source": [
    "## Lazy Execution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a53e8dd9-670a-4a2b-9000-a050b5caacf1",
   "metadata": {},
   "source": [
    "Just switching `read_csv` to `scan_csv` is all it needs to go from eager to lazy in this example. `collect` or `fetch` is then used to trigger the execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "069fbb48-ceef-4390-9389-c2f1a0e2333f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ldf = pl.scan_csv(str(big_csv), has_headers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb002e72-3f2a-49c9-8aff-6f05a5503760",
   "metadata": {},
   "outputs": [
    {
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       "shape: (5, 1)\n",
       "╭──────────╮\n",
       "│ column_9 │\n",
       "│ ---      │\n",
       "│ i64      │\n",
       "╞══════════╡\n",
       "│ 654      │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 61       │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 7        │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 27       │\n",
       "├╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 1        │\n",
       "╰──────────╯"
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "ldf = ldf.filter(col(\"column_1\") == 1)\n",
    "ldf.select([\"column_9\"]).collect().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a84693a1-1243-46ec-9664-1a476f4eb502",
   "metadata": {},
   "source": [
    "Pandas style fails in lazy mode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c317f1b-ca9b-4612-aba4-6542342f011c",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'LazyFrame' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-aaa4e676d17c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mldf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbig_csv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_headers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mldf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mldf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"column_1\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"column_9\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'LazyFrame' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "ldf = pl.scan_csv(str(big_csv), has_headers=False)\n",
    "ldf[ldf[\"column_1\"] == 1][[\"column_9\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1264d493-1a13-4c05-a5bf-f980e31759c2",
   "metadata": {},
   "source": [
    "## Slicing & Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ee0f6c-df69-4d78-9919-c0839df4f4ee",
   "metadata": {},
   "source": [
    "Slicing and indexing in Polars works with the help of the subscript syntax similar to Numpy, i.e. df[1] or df[1, 2]. Some simple rules apply:\n",
    "\n",
    "* indexing by a single dimension \n",
    "   * returns one or several rows if indexed by an integer, e.g. `df[42]`, `df[42:]`,\n",
    "   * returns one or several columns if index by a string, e.g. , `df[\"my_col\"]`, `df[[\"col1\", \"col2]]`, \n",
    "   \n",
    "* indexing by two dimensions  \n",
    "   * returns the row(s) indexed by an integer in the first dimension and the column(s) indexed by integer or string in the second dimension, e.g. `df[69, 42]` or `df[69, \"col_42\"]`\n",
    "   \n",
    "In case of integers also slices, e.g. `1:`, are possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae860ab5-719f-4d3c-aacf-0d873c3db42e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series: 'column_2' [i64]\n",
       "[\n",
       "\t1613423\n",
       "\t1613423\n",
       "\t1613423\n",
       "\t1613423\n",
       "\t1613423\n",
       "\t1613423\n",
       "\t1613423\n",
       "\t1613423\n",
       "\t1613423\n",
       "\t1613423\n",
       "]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edf[1] # this is a bug right now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d18838f5-43b0-4a96-b08c-170a9fb2fa00",
   "metadata": {},
   "outputs": [
    {
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       "<td>\n",
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       "i64\n",
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       "i64\n",
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       "f64\n",
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       "i64\n",
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       "<td>\n",
       "f64\n",
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       "<td>\n",
       "f64\n",
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       "f64\n",
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       "1\n",
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       "1613423\n",
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    "Since in Pandas there is an explicit index that can be any type, not just integer and columns that can have any immutable datatype, it has to workaround several ambiguities with special accessors like `iloc`, `loc`, `at`, `iat`, etc."
   ]
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   "execution_count": 17,
   "id": "18dd6be3-c014-4d03-8ea4-461264f99614",
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf = edf.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d9503d2b-6eac-499f-8cad-260e8dfdc1b1",
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     "execution_count": 18,
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    "pdf.iloc[1, 3]"
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   "cell_type": "code",
   "execution_count": 19,
   "id": "41685406-c32f-4955-bbe1-1a3d3c6edcb6",
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     "execution_count": 19,
     "metadata": {},
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    "# when mixing indexing by integer and by string it gets less comprehensible in Pandas\n",
    "pdf[\"column_4\"].iloc[1] "
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  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4caa0b93-8edc-468f-9fbe-00c49f08ec31",
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    {
     "data": {
      "text/plain": [
       "column_3    8.080000e+02\n",
       "column_4    2.001102e+07\n",
       "Name: 1, dtype: float64"
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     "execution_count": 20,
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   "execution_count": 21,
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       "      <td>2288071</td>\n",
       "      <td>-0.290154</td>\n",
       "      <td>0.446027</td>\n",
       "      <td>8.644362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       column_4  column_5  column_6  column_7  column_8  column_9  column_10  \\\n",
       "1  2.001102e+07   1613424       886         0         0        61        371   \n",
       "2  2.001102e+07   1613424       638         0         0         7        121   \n",
       "3  2.001102e+07   1613424      4259         0         0      1024       1302   \n",
       "\n",
       "      column_11  column_12  column_13  column_14  column_15   column_16  \n",
       "1  2.001120e+07  23.326479    2288071  -0.247330   0.455916   57.810596  \n",
       "2  2.001120e+07   2.444299    2288071  -0.390961   0.589534  167.757140  \n",
       "3  2.001120e+07   9.521868    2288071  -0.290154   0.446027    8.644362  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for slicing with column names and guaranteed indexing by integer we have to write:\n",
    "pdf.loc[:, \"column_4\":].iloc[1:4]\n",
    "# `pdf.loc[1:4, \"column_4\":]` works only as long the index is set correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05bc883-b4e5-4e47-8c96-ea66d071c6dc",
   "metadata": {},
   "source": [
    "## Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8bbd0a8-bb99-495c-bf39-90814e981609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1 \"class=\"dataframe \">\n",
       "<thead>\n",
       "<tr>\n",
       "<th>\n",
       "a\n",
       "</th>\n",
       "<th>\n",
       "b\n",
       "</th>\n",
       "<th>\n",
       "c\n",
       "</th>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "i64\n",
       "</td>\n",
       "<td>\n",
       "str\n",
       "</td>\n",
       "<td>\n",
       "i64\n",
       "</td>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td>\n",
       "1\n",
       "</td>\n",
       "<td>\n",
       "null\n",
       "</td>\n",
       "<td>\n",
       "42\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "2\n",
       "</td>\n",
       "<td>\n",
       "\"b\"\n",
       "</td>\n",
       "<td>\n",
       "69\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "3\n",
       "</td>\n",
       "<td>\n",
       "\"c\"\n",
       "</td>\n",
       "<td>\n",
       "null\n",
       "</td>\n",
       "</tr>\n",
       "</tbody>\n",
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       "</div>"
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      "text/plain": [
       "shape: (3, 3)\n",
       "╭─────┬──────┬──────╮\n",
       "│ a   ┆ b    ┆ c    │\n",
       "│ --- ┆ ---  ┆ ---  │\n",
       "│ i64 ┆ str  ┆ i64  │\n",
       "╞═════╪══════╪══════╡\n",
       "│ 1   ┆ null ┆ 42   │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤\n",
       "│ 2   ┆ \"b\"  ┆ 69   │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤\n",
       "│ 3   ┆ \"c\"  ┆ null │\n",
       "╰─────┴──────┴──────╯"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_df = pl.DataFrame({\"a\": [1, 2, 3], \"b\": [None, \"b\", \"c\"]})\n",
    "right_df = pl.DataFrame({\"a\": [1, 2], \"c\": [42, 69]})\n",
    "\n",
    "df = left_df.join(right_df, on=\"a\", how=\"left\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4048b787-153d-404a-879e-30ee4d2992bb",
   "metadata": {},
   "source": [
    "Note that the last element of the `c` column is `null`, not `NaN` as in Pandas, and the datatype is still int and not automatically converted to float as in Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f6ccfefd-be80-4647-9319-ca2fed5d359d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "str\n",
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       "3\n",
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       "\"c\"\n",
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       "null\n",
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      "text/plain": [
       "shape: (1, 3)\n",
       "╭─────┬─────┬──────╮\n",
       "│ a   ┆ b   ┆ c    │\n",
       "│ --- ┆ --- ┆ ---  │\n",
       "│ i64 ┆ str ┆ i64  │\n",
       "╞═════╪═════╪══════╡\n",
       "│ 3   ┆ \"c\" ┆ null │\n",
       "╰─────┴─────┴──────╯"
      ]
     },
     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "df.filter(col(\"c\").is_null())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d7c81e-379e-4083-9405-227b1e30c5b2",
   "metadata": {},
   "source": [
    "Pandas does something pretty scary here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4d1c909-d033-4950-971d-6213ce9eed5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "left_pdf = left_df.to_pandas()\n",
    "right_pdf = right_df.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87696fba-7218-4e30-a8b9-b6088ab54edc",
   "metadata": {},
   "source": [
    "Note that \"c\"-column has type int:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "599fd2b3-4cfd-4203-ae01-2f6ac8910052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    int64\n",
       "c    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right_pdf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cd62ddcd-c468-4ab0-a707-0426118d8c1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf = pd.merge(left_pdf, right_pdf, on=\"a\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d4036d01-bca2-474c-a680-a0e591793a1c",
   "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",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "    </tr>\n",
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       "      <td>69.0</td>\n",
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      "text/plain": [
       "   a     b     c\n",
       "0  1  None  42.0\n",
       "1  2     b  69.0\n",
       "2  3     c   NaN"
      ]
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     "execution_count": 28,
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   "source": [
    "pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "591aef4b-3719-4820-a0eb-6a763b6693fe",
   "metadata": {},
   "source": [
    "Depending on the datatype, Pandas shows `None` or `NaN`, also note that the column `c` was converted from `int` to `float` without our consent!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0012461f-b8ac-44c1-9d45-a688b996a42a",
   "metadata": {},
   "source": [
    "# New columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b1cb6ac4-1086-46fe-8953-43911a23d655",
   "metadata": {},
   "outputs": [
    {
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       "<th>\n",
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       "</th>\n",
       "<th>\n",
       "3*c\n",
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       "i64\n",
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      "text/plain": [
       "shape: (3, 4)\n",
       "╭─────┬──────┬──────┬──────╮\n",
       "│ a   ┆ b    ┆ c    ┆ 3*c  │\n",
       "│ --- ┆ ---  ┆ ---  ┆ ---  │\n",
       "│ i64 ┆ str  ┆ i64  ┆ i64  │\n",
       "╞═════╪══════╪══════╪══════╡\n",
       "│ 1   ┆ null ┆ 42   ┆ 126  │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤\n",
       "│ 2   ┆ \"b\"  ┆ 69   ┆ 207  │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤\n",
       "│ 3   ┆ \"c\"  ┆ null ┆ null │\n",
       "╰─────┴──────┴──────┴──────╯"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.with_column((lit(3)*col(\"c\")).alias(\"3*c\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "175dc586-b0a0-48c3-9493-b57cead09c56",
   "metadata": {},
   "source": [
    "Same is possible in Pandas but note that we have to retype again the variable name `pdf` just to access a column!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9974df4e-9b68-4139-8d4d-989ef89b2978",
   "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",
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      "text/plain": [
       "   a     b     c    3*c\n",
       "0  1  None  42.0  126.0\n",
       "1  2     b  69.0  207.0\n",
       "2  3     c   NaN    NaN"
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     "metadata": {},
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   ],
   "source": [
    "pdf.assign(**{\"3*c\": 3*pdf[\"c\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ae1c22-ea2d-4e80-97fb-77c0307b8a78",
   "metadata": {},
   "source": [
    "# Column Expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ec9db69a-3813-45e8-b1a5-25f1a0773ef6",
   "metadata": {},
   "outputs": [
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       "<tr>\n",
       "<td>\n",
       "3\n",
       "</td>\n",
       "<td>\n",
       "\"spam\"\n",
       "</td>\n",
       "<td>\n",
       "0.825\n",
       "</td>\n",
       "<td>\n",
       "\"B\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "null\n",
       "</td>\n",
       "<td>\n",
       "\"egg\"\n",
       "</td>\n",
       "<td>\n",
       "0.621\n",
       "</td>\n",
       "<td>\n",
       "\"C\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "5\n",
       "</td>\n",
       "<td>\n",
       "null\n",
       "</td>\n",
       "<td>\n",
       "0.891\n",
       "</td>\n",
       "<td>\n",
       "\"B\"\n",
       "</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "shape: (5, 4)\n",
       "╭──────┬────────┬────────┬────────╮\n",
       "│ nrs  ┆ names  ┆ random ┆ groups │\n",
       "│ ---  ┆ ---    ┆ ---    ┆ ---    │\n",
       "│ i64  ┆ str    ┆ f64    ┆ str    │\n",
       "╞══════╪════════╪════════╪════════╡\n",
       "│ 1    ┆ \"foo\"  ┆ 0.27   ┆ \"A\"    │\n",
       "├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤\n",
       "│ 2    ┆ \"ham\"  ┆ 0.521  ┆ \"A\"    │\n",
       "├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤\n",
       "│ 3    ┆ \"spam\" ┆ 0.825  ┆ \"B\"    │\n",
       "├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤\n",
       "│ null ┆ \"egg\"  ┆ 0.621  ┆ \"C\"    │\n",
       "├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┤\n",
       "│ 5    ┆ null   ┆ 0.891  ┆ \"B\"    │\n",
       "╰──────┴────────┴────────┴────────╯"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pl.DataFrame(\n",
    "    {\n",
    "        \"nrs\": [1, 2, 3, None, 5],\n",
    "        \"names\": [\"foo\", \"ham\", \"spam\", \"egg\", None],\n",
    "        \"random\": np.random.rand(5),\n",
    "        \"groups\": [\"A\", \"A\", \"B\", \"C\", \"B\"],\n",
    "    }\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b101c25a-1e2a-4288-ada6-259cdfa1fe00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# and in Pandas\n",
    "pdf = df.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f84dd6-2c68-49fb-a160-5b31848f4aa0",
   "metadata": {},
   "source": [
    "#### Construct a new dataframe with a sorted column and some aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "049fd3c0-7165-4783-b34d-7c4bf3065435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1 \"class=\"dataframe \">\n",
       "<thead>\n",
       "<tr>\n",
       "<th>\n",
       "nrs\n",
       "</th>\n",
       "<th>\n",
       "names\n",
       "</th>\n",
       "<th>\n",
       "unique_names_1\n",
       "</th>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "i64\n",
       "</td>\n",
       "<td>\n",
       "str\n",
       "</td>\n",
       "<td>\n",
       "u32\n",
       "</td>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td>\n",
       "11\n",
       "</td>\n",
       "<td>\n",
       "null\n",
       "</td>\n",
       "<td>\n",
       "5\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "11\n",
       "</td>\n",
       "<td>\n",
       "\"egg\"\n",
       "</td>\n",
       "<td>\n",
       "5\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "11\n",
       "</td>\n",
       "<td>\n",
       "\"foo\"\n",
       "</td>\n",
       "<td>\n",
       "5\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "11\n",
       "</td>\n",
       "<td>\n",
       "\"ham\"\n",
       "</td>\n",
       "<td>\n",
       "5\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "11\n",
       "</td>\n",
       "<td>\n",
       "\"spam\"\n",
       "</td>\n",
       "<td>\n",
       "5\n",
       "</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "╭─────┬────────┬────────────────╮\n",
       "│ nrs ┆ names  ┆ unique_names_1 │\n",
       "│ --- ┆ ---    ┆ ---            │\n",
       "│ i64 ┆ str    ┆ u32            │\n",
       "╞═════╪════════╪════════════════╡\n",
       "│ 11  ┆ null   ┆ 5              │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 11  ┆ \"egg\"  ┆ 5              │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 11  ┆ \"foo\"  ┆ 5              │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 11  ┆ \"ham\"  ┆ 5              │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 11  ┆ \"spam\" ┆ 5              │\n",
       "╰─────┴────────┴────────────────╯"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select(\n",
    "    [\n",
    "        pl.sum(\"nrs\"), # or equivalently col(\"nrs\").sum()\n",
    "        col(\"names\").sort(),\n",
    "        col(\"names\").n_unique().alias(\"unique_names_1\"),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d256d9-883f-490a-aeac-66a8c32100dc",
   "metadata": {},
   "source": [
    "In Pandas we create a new DataFrame and reference several times `pdf`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "18ca2426-6493-4802-aefa-3000dfc16432",
   "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>nrs</th>\n",
       "      <th>names</th>\n",
       "      <th>unique_names_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>egg</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.0</td>\n",
       "      <td>ham</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.0</td>\n",
       "      <td>spam</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nrs names  unique_names_1\n",
       "3  11.0   egg               5\n",
       "0  11.0   foo               5\n",
       "1  11.0   ham               5\n",
       "2  11.0  spam               5\n",
       "4  11.0  None               5"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    {\n",
    "        \"nrs\": pdf[\"nrs\"].sum(),\n",
    "        \"names\": pdf[\"names\"].sort_values(),\n",
    "        \"unique_names_1\": pdf[\"names\"].nunique(dropna=False),\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b74298-15c3-42e1-9c11-eae13be1d5ad",
   "metadata": {},
   "source": [
    "#### Select certain elements from a column by filtering from another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f8590483-2c00-44c5-bab3-2d24bc40bd27",
   "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>\n",
       "<th>\n",
       "names\n",
       "</th>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "str\n",
       "</td>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td>\n",
       "\"ham\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"spam\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"egg\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "null\n",
       "</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "shape: (4, 1)\n",
       "╭────────╮\n",
       "│ names  │\n",
       "│ ---    │\n",
       "│ str    │\n",
       "╞════════╡\n",
       "│ \"ham\"  │\n",
       "├╌╌╌╌╌╌╌╌┤\n",
       "│ \"spam\" │\n",
       "├╌╌╌╌╌╌╌╌┤\n",
       "│ \"egg\"  │\n",
       "├╌╌╌╌╌╌╌╌┤\n",
       "│ null   │\n",
       "╰────────╯"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select(col(\"names\").filter(col(\"random\") > 0.4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20f9806c-9a39-486d-a7e5-b05adbeda70a",
   "metadata": {},
   "source": [
    "Syntax in Pandas is way less readable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6558644e-cafb-46c2-87ae-ef5c5b404f55",
   "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
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       "    <tr>\n",
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       "      <td>None</td>\n",
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      "text/plain": [
       "  names\n",
       "1   ham\n",
       "2  spam\n",
       "3   egg\n",
       "4  None"
      ]
     },
     "execution_count": 36,
     "metadata": {},
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    }
   ],
   "source": [
    "pdf.loc[pdf[\"random\"] > 0.4][[\"names\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3bc6e05-0edf-4228-9c4e-71da29490f0c",
   "metadata": {},
   "source": [
    "Another way in Pandas is to use the query style:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "27d533cb-685d-4a44-8750-0af52af9a2a4",
   "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>names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>egg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  names\n",
       "1   ham\n",
       "2  spam\n",
       "3   egg\n",
       "4  None"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf.query(\"random > 0.4\")[[\"names\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fac4462-3437-4318-8dbc-bacf58ea05d5",
   "metadata": {},
   "source": [
    "The problem with Pandas' query style is that it is basically string hacking with no checks whatsoever until the code is executed. Also reuse of certain expressions is highly limited if you have just strings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e299dff-96a6-4fc3-9c70-ec7e606aaf8a",
   "metadata": {},
   "source": [
    "### Complex expressions are also possible"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74cab910-b981-4fc9-b111-5cad5013d0af",
   "metadata": {},
   "source": [
    "All expressions in Polars are *embarassingly parallel* by design and thus automatically parallelized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a7b72d13-9027-4903-99a7-23592983e5a7",
   "metadata": {},
   "outputs": [
    {
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       "shape: (5, 1)\n",
       "╭────────╮\n",
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       "│ f64    │\n",
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       "│ 2.973  │\n",
       "├╌╌╌╌╌╌╌╌┤\n",
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       "│ 0.0    │\n",
       "├╌╌╌╌╌╌╌╌┤\n",
       "│ 0.0    │\n",
       "├╌╌╌╌╌╌╌╌┤\n",
       "│ 0.0    │\n",
       "╰────────╯"
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   ],
   "source": [
    "df.select(\n",
    "    [\n",
    "        (pl.sum(\"nrs\") * pl.when(col(\"random\") > 0.5)\n",
    "                           .then(0)\n",
    "                           .otherwise(col(\"random\"))\n",
    "        ).alias(\"result\")\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9627522d-cb33-4313-9a31-b1ebd8e68b5e",
   "metadata": {},
   "source": [
    "SQL-like `when`/`then`/`otherwise` statements are not possible in Pandas, thus we have to use `np.where`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a47b5f88-4217-4cc6-aa20-ad93b18ffeb0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.973213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     result\n",
       "0  2.973213\n",
       "1  0.000000\n",
       "2  0.000000\n",
       "3  0.000000\n",
       "4  0.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(\n",
    "    np.where(pdf[\"random\"] > 0.5, 0, pdf[\"random\"] * pdf[\"nrs\"].sum()), name=\"result\"\n",
    ").to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0b74d891-31e0-4f86-8f72-12854ec06c03",
   "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>random</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.973213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     random\n",
       "0  2.973213\n",
       "1  0.000000\n",
       "2  0.000000\n",
       "3  0.000000\n",
       "4  0.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or easier to read but much slower since not vectorized at all\n",
    "pdf.random.apply(lambda x: 0 if x > 0.5 else x * pdf[\"nrs\"].sum()).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24f4014d-57f7-48e4-91c1-d624fcd9e9af",
   "metadata": {},
   "source": [
    "#### Even window expressions are possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "027ce5e3-8f56-471d-9d59-a15083c647b0",
   "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>\n",
       "<th>\n",
       "nrs\n",
       "</th>\n",
       "<th>\n",
       "names\n",
       "</th>\n",
       "<th>\n",
       "random\n",
       "</th>\n",
       "<th>\n",
       "groups\n",
       "</th>\n",
       "<th>\n",
       "sum[random]/groups\n",
       "</th>\n",
       "<th>\n",
       "random/name\n",
       "</th>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "i64\n",
       "</td>\n",
       "<td>\n",
       "str\n",
       "</td>\n",
       "<td>\n",
       "f64\n",
       "</td>\n",
       "<td>\n",
       "str\n",
       "</td>\n",
       "<td>\n",
       "f64\n",
       "</td>\n",
       "<td>\n",
       "list\n",
       "</td>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td>\n",
       "1\n",
       "</td>\n",
       "<td>\n",
       "\"foo\"\n",
       "</td>\n",
       "<td>\n",
       "0.27\n",
       "</td>\n",
       "<td>\n",
       "\"A\"\n",
       "</td>\n",
       "<td>\n",
       "0.791\n",
       "</td>\n",
       "<td>\n",
       "\"[0.2702920925095984]\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "2\n",
       "</td>\n",
       "<td>\n",
       "\"ham\"\n",
       "</td>\n",
       "<td>\n",
       "0.521\n",
       "</td>\n",
       "<td>\n",
       "\"A\"\n",
       "</td>\n",
       "<td>\n",
       "0.791\n",
       "</td>\n",
       "<td>\n",
       "\"[0.5210243165448514]\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "3\n",
       "</td>\n",
       "<td>\n",
       "\"spam\"\n",
       "</td>\n",
       "<td>\n",
       "0.825\n",
       "</td>\n",
       "<td>\n",
       "\"B\"\n",
       "</td>\n",
       "<td>\n",
       "1.716\n",
       "</td>\n",
       "<td>\n",
       "\"[0.8248294839356618]\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "null\n",
       "</td>\n",
       "<td>\n",
       "\"egg\"\n",
       "</td>\n",
       "<td>\n",
       "0.621\n",
       "</td>\n",
       "<td>\n",
       "\"C\"\n",
       "</td>\n",
       "<td>\n",
       "0.621\n",
       "</td>\n",
       "<td>\n",
       "\"[0.6213192138749672]\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "5\n",
       "</td>\n",
       "<td>\n",
       "null\n",
       "</td>\n",
       "<td>\n",
       "0.891\n",
       "</td>\n",
       "<td>\n",
       "\"B\"\n",
       "</td>\n",
       "<td>\n",
       "1.716\n",
       "</td>\n",
       "<td>\n",
       "\"[0.8909539423562371]\"\n",
       "</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "shape: (5, 6)\n",
       "╭──────┬────────┬────────┬────────┬────────────────────┬────────────────────────╮\n",
       "│ nrs  ┆ names  ┆ random ┆ groups ┆ sum[random]/groups ┆ random/name            │\n",
       "│ ---  ┆ ---    ┆ ---    ┆ ---    ┆ ---                ┆ ---                    │\n",
       "│ i64  ┆ str    ┆ f64    ┆ str    ┆ f64                ┆ list [f64]             │\n",
       "╞══════╪════════╪════════╪════════╪════════════════════╪════════════════════════╡\n",
       "│ 1    ┆ \"foo\"  ┆ 0.27   ┆ \"A\"    ┆ 0.791              ┆ \"[0.2702920925095984]\" │\n",
       "├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 2    ┆ \"ham\"  ┆ 0.521  ┆ \"A\"    ┆ 0.791              ┆ \"[0.5210243165448514]\" │\n",
       "├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 3    ┆ \"spam\" ┆ 0.825  ┆ \"B\"    ┆ 1.716              ┆ \"[0.8248294839356618]\" │\n",
       "├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ null ┆ \"egg\"  ┆ 0.621  ┆ \"C\"    ┆ 0.621              ┆ \"[0.6213192138749672]\" │\n",
       "├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 5    ┆ null   ┆ 0.891  ┆ \"B\"    ┆ 1.716              ┆ \"[0.8909539423562371]\" │\n",
       "╰──────┴────────┴────────┴────────┴────────────────────┴────────────────────────╯"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select(\n",
    "    [\n",
    "        col(\"*\"),  # select all\n",
    "        col(\"random\").sum().over(\"groups\").alias(\"sum[random]/groups\"),\n",
    "        col(\"random\").list().over(\"names\").alias(\"random/name\"),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c3c07e4-6efa-44a7-98c2-fd2c758f16f8",
   "metadata": {},
   "source": [
    "Doing the same in Pandas is a bit more complex. Also note that there is an unexpected `NaN` in the last row. This is due to the fact that when inserting `pdf.groupby(['names'], dropna=False)['random'].apply(list)` we compare `NaN` to `NaN` which is false by definition. This is just another subtle problem caused by the fact that Pandas uses `NaN` to express `NA`.\n",
    "Also note that Polars needs no explicit index like Pandas to do operations like this, just like Spark has no way to set an index explicitely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "225ab8a8-c2f9-4b80-8b81-4b5907e4d4b9",
   "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>names</th>\n",
       "      <th>nrs</th>\n",
       "      <th>random</th>\n",
       "      <th>sum[random]/groups</th>\n",
       "      <th>random/name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.270292</td>\n",
       "      <td>0.791316</td>\n",
       "      <td>[0.2702920925095984]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.521024</td>\n",
       "      <td>0.791316</td>\n",
       "      <td>[0.5210243165448514]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.824829</td>\n",
       "      <td>1.715783</td>\n",
       "      <td>[0.8248294839356618]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>egg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.621319</td>\n",
       "      <td>0.621319</td>\n",
       "      <td>[0.6213192138749672]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.890954</td>\n",
       "      <td>1.715783</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  names  nrs    random  sum[random]/groups           random/name\n",
       "0   foo  1.0  0.270292            0.791316  [0.2702920925095984]\n",
       "1   ham  2.0  0.521024            0.791316  [0.5210243165448514]\n",
       "2  spam  3.0  0.824829            1.715783  [0.8248294839356618]\n",
       "3   egg  NaN  0.621319            0.621319  [0.6213192138749672]\n",
       "4  None  5.0  0.890954            1.715783                   NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pdf.set_index(\"groups\")\n",
    "    .assign(**{\"sum[random]/groups\": pdf.groupby(['groups'])['random'].sum()})\n",
    "    .set_index(\"names\")\n",
    "    .assign(**{\"random/name\": pdf.groupby(['names'], dropna=False)['random'].apply(list)})\n",
    "    .reset_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4538580f-beae-47b8-a894-c27345a65e02",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nrs</th>\n",
       "      <th>names</th>\n",
       "      <th>random</th>\n",
       "      <th>groups</th>\n",
       "      <th>sum[random]/groups</th>\n",
       "      <th>random/name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.270292</td>\n",
       "      <td>A</td>\n",
       "      <td>0.791316</td>\n",
       "      <td>[0.2702920925095984]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>ham</td>\n",
       "      <td>0.521024</td>\n",
       "      <td>A</td>\n",
       "      <td>0.791316</td>\n",
       "      <td>[0.5210243165448514]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>spam</td>\n",
       "      <td>0.824829</td>\n",
       "      <td>B</td>\n",
       "      <td>1.715783</td>\n",
       "      <td>[0.8248294839356618]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>egg</td>\n",
       "      <td>0.621319</td>\n",
       "      <td>C</td>\n",
       "      <td>0.621319</td>\n",
       "      <td>[0.6213192138749672]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.890954</td>\n",
       "      <td>B</td>\n",
       "      <td>1.715783</td>\n",
       "      <td>[0.8909539423562371]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nrs names    random groups  sum[random]/groups           random/name\n",
       "0  1.0   foo  0.270292      A            0.791316  [0.2702920925095984]\n",
       "1  2.0   ham  0.521024      A            0.791316  [0.5210243165448514]\n",
       "2  3.0  spam  0.824829      B            1.715783  [0.8248294839356618]\n",
       "3  NaN   egg  0.621319      C            0.621319  [0.6213192138749672]\n",
       "4  5.0  None  0.890954      B            1.715783  [0.8909539423562371]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or alternatively using `join` which is also avoiding the NaN problem\n",
    "pdf.join(\n",
    "    pdf.groupby('groups').random.sum().rename(\"sum[random]/groups\"), on=\"groups\"\n",
    ").join(\n",
    "    pdf.groupby('names', dropna=False).random.apply(list).rename(\"random/name\"), on=\"names\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03215211-5084-4858-b27e-0f559c383d50",
   "metadata": {},
   "source": [
    "# GroupBy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "94d1cfb0-8c37-43dc-9ef4-2fd6a6ae9315",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pl.read_csv(\"https://theunitedstates.io/congress-legislators/legislators-current.csv\")\n",
    "pdf = df.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7118da67-e6bd-4064-9b54-c485c9c4e59e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1 \"class=\"dataframe \">\n",
       "<thead>\n",
       "<tr>\n",
       "<th>\n",
       "first_name\n",
       "</th>\n",
       "<th>\n",
       "n_party\n",
       "</th>\n",
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       "gender_agg_list\n",
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       "str\n",
       "</td>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td>\n",
       "\"John\"\n",
       "</td>\n",
       "<td>\n",
       "19\n",
       "</td>\n",
       "<td>\n",
       "\"[M, M, ... M]\"\n",
       "</td>\n",
       "<td>\n",
       "\"Barrasso\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"Mike\"\n",
       "</td>\n",
       "<td>\n",
       "12\n",
       "</td>\n",
       "<td>\n",
       "\"[M, M, ... M]\"\n",
       "</td>\n",
       "<td>\n",
       "\"Kelly\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"Michael\"\n",
       "</td>\n",
       "<td>\n",
       "11\n",
       "</td>\n",
       "<td>\n",
       "\"[M, M, ... M]\"\n",
       "</td>\n",
       "<td>\n",
       "\"Bennet\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"David\"\n",
       "</td>\n",
       "<td>\n",
       "11\n",
       "</td>\n",
       "<td>\n",
       "\"[M, M, ... M]\"\n",
       "</td>\n",
       "<td>\n",
       "\"Cicilline\"\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"James\"\n",
       "</td>\n",
       "<td>\n",
       "9\n",
       "</td>\n",
       "<td>\n",
       "\"[M, M, ... M]\"\n",
       "</td>\n",
       "<td>\n",
       "\"Inhofe\"\n",
       "</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "shape: (5, 4)\n",
       "╭────────────┬─────────┬─────────────────┬─────────────────╮\n",
       "│ first_name ┆ n_party ┆ gender_agg_list ┆ last_name_first │\n",
       "│ ---        ┆ ---     ┆ ---             ┆ ---             │\n",
       "│ str        ┆ u32     ┆ list [str]      ┆ str             │\n",
       "╞════════════╪═════════╪═════════════════╪═════════════════╡\n",
       "│ \"John\"     ┆ 19      ┆ \"[M, M, ... M]\" ┆ \"Barrasso\"      │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ \"Mike\"     ┆ 12      ┆ \"[M, M, ... M]\" ┆ \"Kelly\"         │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ \"Michael\"  ┆ 11      ┆ \"[M, M, ... M]\" ┆ \"Bennet\"        │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ \"David\"    ┆ 11      ┆ \"[M, M, ... M]\" ┆ \"Cicilline\"     │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ \"James\"    ┆ 9       ┆ \"[M, M, ... M]\" ┆ \"Inhofe\"        │\n",
       "╰────────────┴─────────┴─────────────────┴─────────────────╯"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.lazy()  # allows for working only on a subset using limit\n",
    "   .groupby(\"first_name\")\n",
    "   .agg(\n",
    "       [\n",
    "           col(\"party\").count().alias(\"n_party\"),  # renaming an aggregated column is a bliss\n",
    "           col(\"gender\").list(),\n",
    "           col(\"last_name\").first(),\n",
    "       ]\n",
    "   )\n",
    "   .sort(\"n_party\", reverse=True)\n",
    "   .limit(5)\n",
    "   .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c43e600-24ee-4a51-8028-1aee4bbb690a",
   "metadata": {},
   "source": [
    "Note how easily we can deal with lists of strings by aggregating over gender using `list()`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8027f99f-b072-490a-adc0-0c7568c4f90b",
   "metadata": {},
   "source": [
    "In Pandas the same operation feels more like string hacking and renaming happens as a separate step having unnecessary repetitions of the column names. Everything is of course eagerly evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "608458dd-b992-4b95-834e-92b23bf25eb0",
   "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>first_name</th>\n",
       "      <th>n_party</th>\n",
       "      <th>gender_agg_list</th>\n",
       "      <th>last_name_first</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>19</td>\n",
       "      <td>[M, M, M, M, M, M, M, M, M, M, M, M, M, M, M, ...</td>\n",
       "      <td>Barrasso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mike</td>\n",
       "      <td>12</td>\n",
       "      <td>[M, M, M, M, M, M, M, M, M, M, M, M]</td>\n",
       "      <td>Kelly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael</td>\n",
       "      <td>11</td>\n",
       "      <td>[M, M, M, M, M, M, M, M, M, M, M]</td>\n",
       "      <td>Bennet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>11</td>\n",
       "      <td>[M, M, M, M, M, M, M, M, M, M, M]</td>\n",
       "      <td>Cicilline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James</td>\n",
       "      <td>9</td>\n",
       "      <td>[M, M, M, M, M, M, M, M, M]</td>\n",
       "      <td>Inhofe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name  n_party                                    gender_agg_list  \\\n",
       "0       John       19  [M, M, M, M, M, M, M, M, M, M, M, M, M, M, M, ...   \n",
       "1       Mike       12               [M, M, M, M, M, M, M, M, M, M, M, M]   \n",
       "2    Michael       11                  [M, M, M, M, M, M, M, M, M, M, M]   \n",
       "3      David       11                  [M, M, M, M, M, M, M, M, M, M, M]   \n",
       "4      James        9                        [M, M, M, M, M, M, M, M, M]   \n",
       "\n",
       "  last_name_first  \n",
       "0        Barrasso  \n",
       "1           Kelly  \n",
       "2          Bennet  \n",
       "3       Cicilline  \n",
       "4          Inhofe  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pdf.groupby(\"first_name\")\n",
    "    .agg({\"party\": \"count\", \n",
    "          \"gender\": lambda grp: grp.to_list(), \n",
    "          \"last_name\": \"first\"})\n",
    "    .rename(columns={\"party\": \"n_party\", \n",
    "                     \"gender\": \"gender_agg_list\", \n",
    "                     \"last_name\": \"last_name_first\"})\n",
    "    .sort_values(by=\"n_party\", ascending=False)\n",
    "    .reset_index()\n",
    "    .head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c21836b0-be41-416b-beee-28b65754b350",
   "metadata": {},
   "source": [
    "#### Conditionals in aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6b909fb7-0420-4350-a7fc-5afbe8f5c09b",
   "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>\n",
       "<th>\n",
       "state\n",
       "</th>\n",
       "<th>\n",
       "demo\n",
       "</th>\n",
       "<th>\n",
       "rep\n",
       "</th>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "str\n",
       "</td>\n",
       "<td>\n",
       "u32\n",
       "</td>\n",
       "<td>\n",
       "u32\n",
       "</td>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td>\n",
       "\"CA\"\n",
       "</td>\n",
       "<td>\n",
       "44\n",
       "</td>\n",
       "<td>\n",
       "11\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"NY\"\n",
       "</td>\n",
       "<td>\n",
       "21\n",
       "</td>\n",
       "<td>\n",
       "8\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"IL\"\n",
       "</td>\n",
       "<td>\n",
       "15\n",
       "</td>\n",
       "<td>\n",
       "5\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"TX\"\n",
       "</td>\n",
       "<td>\n",
       "13\n",
       "</td>\n",
       "<td>\n",
       "24\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"NJ\"\n",
       "</td>\n",
       "<td>\n",
       "12\n",
       "</td>\n",
       "<td>\n",
       "2\n",
       "</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "╭───────┬──────┬─────╮\n",
       "│ state ┆ demo ┆ rep │\n",
       "│ ---   ┆ ---  ┆ --- │\n",
       "│ str   ┆ u32  ┆ u32 │\n",
       "╞═══════╪══════╪═════╡\n",
       "│ \"CA\"  ┆ 44   ┆ 11  │\n",
       "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ \"NY\"  ┆ 21   ┆ 8   │\n",
       "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ \"IL\"  ┆ 15   ┆ 5   │\n",
       "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ \"TX\"  ┆ 13   ┆ 24  │\n",
       "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ \"NJ\"  ┆ 12   ┆ 2   │\n",
       "╰───────┴──────┴─────╯"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.lazy()\n",
    "   .groupby(\"state\")\n",
    "   .agg(\n",
    "       [\n",
    "           (col(\"party\") == \"Democrat\").sum().alias(\"demo\"),\n",
    "           (col(\"party\") == \"Republican\").sum().alias(\"rep\"),\n",
    "       ]\n",
    "   )\n",
    "   .sort(\"demo\", reverse=True)\n",
    "   .limit(5)\n",
    "   .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f71e5c8-6c82-4878-96f5-12eff13fd3dc",
   "metadata": {},
   "source": [
    "The translation to Pandas is \"somewhat\" more complicated... kudos to everyone able to solve this without looking it up on StackOverflow :-) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0e189c80-5969-4627-98e9-0325cbf30f3a",
   "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>state</th>\n",
       "      <th>demo</th>\n",
       "      <th>rep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CA</td>\n",
       "      <td>44</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NY</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>IL</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>TX</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NJ</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state  demo  rep\n",
       "5     CA    44   11\n",
       "37    NY    21    8\n",
       "16    IL    15    5\n",
       "47    TX    13   24\n",
       "34    NJ    12    2"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pdf.groupby(\"state\")\n",
    "    .agg({\"party\": [(\"demo\", lambda grp: np.sum(grp == \"Democrat\")), \n",
    "                    (\"rep\", lambda grp:  np.sum(grp == \"Republican\"))]})\n",
    "    .droplevel(0, axis=1) \n",
    "    .reset_index()\n",
    "    .sort_values(by=\"demo\", ascending=False)\n",
    "    .head(5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5eb199-1445-4a32-9b18-0c2944a72964",
   "metadata": {},
   "source": [
    "#### Composition and reuse of more complex operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "36be36bf-0781-4918-b92c-5c40efa7f7e1",
   "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>\n",
       "<th>\n",
       "state\n",
       "</th>\n",
       "<th>\n",
       "avg M age\n",
       "</th>\n",
       "<th>\n",
       "avg F age\n",
       "</th>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "str\n",
       "</td>\n",
       "<td>\n",
       "f64\n",
       "</td>\n",
       "<td>\n",
       "f64\n",
       "</td>\n",
       "</tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr>\n",
       "<td>\n",
       "\"AK\"\n",
       "</td>\n",
       "<td>\n",
       "71.899\n",
       "</td>\n",
       "<td>\n",
       "63.657\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"AL\"\n",
       "</td>\n",
       "<td>\n",
       "65.167\n",
       "</td>\n",
       "<td>\n",
       "56.038\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"AR\"\n",
       "</td>\n",
       "<td>\n",
       "58.325\n",
       "</td>\n",
       "<td>\n",
       "null\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"AS\"\n",
       "</td>\n",
       "<td>\n",
       "null\n",
       "</td>\n",
       "<td>\n",
       "73.06\n",
       "</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>\n",
       "\"AZ\"\n",
       "</td>\n",
       "<td>\n",
       "60.004\n",
       "</td>\n",
       "<td>\n",
       "59.168\n",
       "</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "╭───────┬───────────┬───────────╮\n",
       "│ state ┆ avg M age ┆ avg F age │\n",
       "│ ---   ┆ ---       ┆ ---       │\n",
       "│ str   ┆ f64       ┆ f64       │\n",
       "╞═══════╪═══════════╪═══════════╡\n",
       "│ \"AK\"  ┆ 71.899    ┆ 63.657    │\n",
       "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ \"AL\"  ┆ 65.167    ┆ 56.038    │\n",
       "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ \"AR\"  ┆ 58.325    ┆ null      │\n",
       "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ \"AS\"  ┆ null      ┆ 73.06     │\n",
       "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ \"AZ\"  ┆ 60.004    ┆ 59.168    │\n",
       "╰───────┴───────────┴───────────╯"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_age() -> pl.Expr:\n",
    "    # Date64 is time in ms\n",
    "    ms_to_year = 1e3 * 3600 * 24 * 365\n",
    "    return (\n",
    "        lit(datetime(2021, 1, 1)) - col(\"birthday\")\n",
    "    ) / (ms_to_year)\n",
    "\n",
    "\n",
    "def avg_age(gender: str) -> pl.Expr:\n",
    "    return (\n",
    "        compute_age()\n",
    "        .filter(col(\"gender\") == gender)\n",
    "        .mean()\n",
    "        .alias(f\"avg {gender} age\")\n",
    "    )\n",
    "\n",
    "\n",
    "(df.lazy()\n",
    "   .groupby([\"state\"])\n",
    "   .agg(\n",
    "       [\n",
    "            avg_age(\"M\"),\n",
    "            avg_age(\"F\"),\n",
    "       ]\n",
    "   )\n",
    "   .sort(\"state\")\n",
    "   .limit(5)\n",
    "   .collect()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a0c2e8b-ccc2-4dbd-acc6-2a1460c11107",
   "metadata": {},
   "source": [
    "Translating this to Pandas is really hard since we have no way to refer to a column. Also with Pandas' `agg` we have only access to the aggregation column and cannot filter by another, thus we have to use `apply`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e71e3ab0-f0a3-4aad-bf93-b7176fd9cc38",
   "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>state</th>\n",
       "      <th>avg M age</th>\n",
       "      <th>avg F age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>71.898630</td>\n",
       "      <td>63.657534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>65.167466</td>\n",
       "      <td>56.038356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>58.324658</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.060274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>60.003767</td>\n",
       "      <td>59.168037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  avg M age  avg F age\n",
       "0    AK  71.898630  63.657534\n",
       "1    AL  65.167466  56.038356\n",
       "2    AR  58.324658        NaN\n",
       "3    AS        NaN  73.060274\n",
       "4    AZ  60.003767  59.168037"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def p_compute_age(grp: pd.DataFrame):\n",
    "    # Date64 is time in ms\n",
    "    s_to_year = 3600 * 24 * 365\n",
    "    return (\n",
    "        (datetime(2021, 1, 1) - grp[\"birthday\"]).dt.total_seconds()\n",
    "    ) / (s_to_year)\n",
    "\n",
    "\n",
    "def p_avg_age(grp: pd.DataFrame, gender: str):\n",
    "    age = p_compute_age(grp)\n",
    "    mean_age = age[grp[\"gender\"] == gender].mean()\n",
    "    return pd.Series([mean_age], index=[f\"avg {gender} age\"])\n",
    "\n",
    "\n",
    "(pdf.groupby(\"state\")\n",
    "    .apply(\n",
    "        lambda grp: pd.concat(\n",
    "            [p_avg_age(grp, gender=\"M\"),\n",
    "             p_avg_age(grp, gender=\"F\")]\n",
    "        )\n",
    "    )\n",
    "    .reset_index()\n",
    "    .sort_values(by=\"state\")\n",
    "    .head(5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e44563-1135-4e6d-a815-0aad5a4edd6d",
   "metadata": {},
   "source": [
    "The same code in Pandas just feels not as clean as in Polars, thus showing nicely the power that comes with Polars' composable expressions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f085096-72aa-4a64-bb9b-e7a1b98ca9d3",
   "metadata": {},
   "source": [
    "# User-Defined (Aggregation) Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b6132b27-39f4-4404-9c3b-813a3a7b1517",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pl.DataFrame({\"foo\": np.arange(10), \n",
    "                   \"bar\": np.random.rand(10), \n",
    "                   \"cls\": np.random.randint(2, size=10)})\n",
    "pdf = df.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d8647da2-3721-4949-9c8b-ff05911a9c0d",
   "metadata": {},
   "outputs": [
    {
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       "6\n",
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       "<td>\n",
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       "<td>\n",
       "1\n",
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       "<tr>\n",
       "<td>\n",
       "7\n",
       "</td>\n",
       "<td>\n",
       "0.656\n",
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       "<td>\n",
       "0\n",
       "</td>\n",
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       "<tr>\n",
       "<td>\n",
       "8\n",
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       "<td>\n",
       "0.999\n",
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       "<td>\n",
       "9\n",
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       "shape: (10, 3)\n",
       "╭─────┬───────┬─────╮\n",
       "│ foo ┆ bar   ┆ cls │\n",
       "│ --- ┆ ---   ┆ --- │\n",
       "│ i64 ┆ f64   ┆ i64 │\n",
       "╞═════╪═══════╪═════╡\n",
       "│ 0   ┆ 0.232 ┆ 0   │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ 1   ┆ 0.359 ┆ 0   │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ 2   ┆ 0.404 ┆ 1   │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ 3   ┆ 0.855 ┆ 0   │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ ... ┆ ...   ┆ ... │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ 5   ┆ 0.598 ┆ 1   │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ 6   ┆ 0.747 ┆ 1   │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ 7   ┆ 0.656 ┆ 0   │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ 8   ┆ 0.999 ┆ 0   │\n",
       "├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┤\n",
       "│ 9   ┆ 0.769 ┆ 1   │\n",
       "╰─────┴───────┴─────╯"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11981d6e-a0a8-456c-ab34-62c86a170b77",
   "metadata": {},
   "source": [
    "#### Vector Operations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e93e065e-e088-4b8a-9390-5a1f04fe5ff0",
   "metadata": {},
   "source": [
    "Use `map` for vector operations on a whole column, i.e. series -> series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e376a61a-8677-486f-ad94-14ab3737e766",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_custom_func(s: pl.Series) -> pl.Series:\n",
    "    return np.exp(s) / np.log(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "39d1a96f-9baf-4f4a-8979-66803c867fb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "shape: (1, 3)\n",
       "╭─────┬───────┬─────╮\n",
       "│ foo ┆ bar   ┆ cls │\n",
       "│ --- ┆ ---   ┆ --- │\n",
       "│ i64 ┆ f64   ┆ i64 │\n",
       "╞═════╪═══════╪═════╡\n",
       "│ 0   ┆ 0.232 ┆ 0   │\n",
       "╰─────┴───────┴─────╯"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.filter(col(\"bar\").map(my_custom_func, return_dtype=pl.Float64) > -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93532781-a8b1-44c8-b6de-2287f38200bd",
   "metadata": {},
   "source": [
    "Use `apply` for scalar operations on a cell or group level but returning a scalar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3fe6a6c9-b6d7-4de9-bebe-f942cdf3e055",
   "metadata": {},
   "outputs": [
    {
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       "<thead>\n",
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       "<tr>\n",
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       "f64\n",
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       "1.078\n",
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       "1.211\n",
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       "<tr>\n",
       "<td>\n",
       "2.566\n",
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       "<tr>\n",
       "<td>\n",
       "2.383\n",
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       "<tr>\n",
       "<td>\n",
       "1.794\n",
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       "<tr>\n",
       "<td>\n",
       "2.24\n",
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       "<tr>\n",
       "<td>\n",
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       "<tr>\n",
       "<td>\n",
       "2.996\n",
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       "<tr>\n",
       "<td>\n",
       "2.308\n",
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       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "shape: (10, 1)\n",
       "╭───────╮\n",
       "│ bar   │\n",
       "│ ---   │\n",
       "│ f64   │\n",
       "╞═══════╡\n",
       "│ 0.696 │\n",
       "├╌╌╌╌╌╌╌┤\n",
       "│ 1.078 │\n",
       "├╌╌╌╌╌╌╌┤\n",
       "│ 1.211 │\n",
       "├╌╌╌╌╌╌╌┤\n",
       "│ 2.566 │\n",
       "├╌╌╌╌╌╌╌┤\n",
       "│ ...   │\n",
       "├╌╌╌╌╌╌╌┤\n",
       "│ 1.794 │\n",
       "├╌╌╌╌╌╌╌┤\n",
       "│ 2.24  │\n",
       "├╌╌╌╌╌╌╌┤\n",
       "│ 1.967 │\n",
       "├╌╌╌╌╌╌╌┤\n",
       "│ 2.996 │\n",
       "├╌╌╌╌╌╌╌┤\n",
       "│ 2.308 │\n",
       "╰───────╯"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select(col(\"bar\").apply(lambda x: 3*x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da1e796-7874-467f-a986-0b5b423f9d84",
   "metadata": {},
   "source": [
    "In Pandas, you have `apply` and `applymap` working quite similarily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "715069ec-4042-4621-8952-96a85c09fbe3",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "      <th>bar</th>\n",
       "      <th>cls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.232159</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   foo       bar  cls\n",
       "0    0  0.232159    0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf[pdf[\"bar\"].apply(my_custom_func) > -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c9e1f58a-74d0-4b51-a762-51f61d7894ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.696478</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.078346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.211216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.566302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.382630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.793900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.239719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.966602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.995891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.307976</td>\n",
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       "        bar\n",
       "0  0.696478\n",
       "1  1.078346\n",
       "2  1.211216\n",
       "3  2.566302\n",
       "4  2.382630\n",
       "5  1.793900\n",
       "6  2.239719\n",
       "7  1.966602\n",
       "8  2.995891\n",
       "9  2.307976"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf[[\"bar\"]].applymap(lambda x: 3*x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74984a8e-85c9-4548-90d2-df9775c5e7dc",
   "metadata": {},
   "source": [
    "#### Aggregation Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7218605b-d464-434e-81a9-2c9d1760ea25",
   "metadata": {},
   "outputs": [
    {
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       "9.935\n",
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      "text/plain": [
       "shape: (2, 2)\n",
       "╭─────┬───────╮\n",
       "│ cls ┆       │\n",
       "│ --- ┆ ---   │\n",
       "│ i64 ┆ f64   │\n",
       "╞═════╪═══════╡\n",
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       "│ 1   ┆ 9.935 │\n",
       "╰─────┴───────╯"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"cls\"]).agg([col(\"bar\").apply(lambda grp: 3*grp.sum())])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29e18a19-a2a3-44ad-8f7c-dea27b5ce9f5",
   "metadata": {},
   "source": [
    "Quite analogous in Pandas but of course you need to fight the multi-index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d7279cb8-e216-43ce-a194-cef2bc082c7e",
   "metadata": {},
   "outputs": [
    {
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       "   cls       bar\n",
       "0    0  9.303619\n",
       "1    1  9.935442"
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     "metadata": {},
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    "pdf.groupby(\"cls\", as_index=False).agg({\"bar\": lambda grp: 3 * grp.sum()})"
   ]
  }
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