Polars_Example1_3.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOKbuez7wK4CgaEFi+tRCHq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pea-sys/Til/blob/master/Polars_Example1_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQe3ydTbEsb4",
        "outputId": "9c0e441e-88ff-4f02-8cd6-60f7431449c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting polars\n",
            "  Downloading polars-0.16.9-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.4/15.4 MB\u001b[0m \u001b[31m34.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing_extensions>=4.0.1 in /usr/local/lib/python3.8/dist-packages (from polars) (4.5.0)\n",
            "Installing collected packages: polars\n",
            "Successfully installed polars-0.16.9\n"
          ]
        }
      ],
      "source": [
        "!pip install polars"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### クイックスタート"
      ],
      "metadata": {
        "id": "p9rA_4SIE6Yk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import polars as pl\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "df = pl.read_csv(\"https://j.mp/iriscsv\")\n",
        "print(df.filter(pl.col(\"sepal_length\") > 5)\n",
        "      .groupby(\"species\", maintain_order=True)\n",
        "      .agg(pl.all().sum())\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tT6dW4SAE3OF",
        "outputId": "76996773-336c-4831-ac9a-41c3df7d7370"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (3, 5)\n",
            "┌────────────┬──────────────┬─────────────┬──────────────┬─────────────┐\n",
            "│ species    ┆ sepal_length ┆ sepal_width ┆ petal_length ┆ petal_width │\n",
            "│ ---        ┆ ---          ┆ ---         ┆ ---          ┆ ---         │\n",
            "│ str        ┆ f64          ┆ f64         ┆ f64          ┆ f64         │\n",
            "╞════════════╪══════════════╪═════════════╪══════════════╪═════════════╡\n",
            "│ setosa     ┆ 116.9        ┆ 81.7        ┆ 33.2         ┆ 6.1         │\n",
            "│ versicolor ┆ 281.9        ┆ 131.8       ┆ 202.9        ┆ 63.3        │\n",
            "│ virginica  ┆ 324.5        ┆ 146.2       ┆ 273.1        ┆ 99.6        │\n",
            "└────────────┴──────────────┴─────────────┴──────────────┴─────────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### レイジークイックスタート"
      ],
      "metadata": {
        "id": "lHl_BnZXFHVS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\n",
        "    pl.read_csv(\"https://j.mp/iriscsv\")\n",
        "    .lazy()\n",
        "    .filter(pl.col(\"sepal_length\") > 5)\n",
        "    .groupby(\"species\", maintain_order=True)\n",
        "    .agg(pl.all().sum())\n",
        "    .collect()\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kA3fagSyE8_5",
        "outputId": "87bbb5d9-a03f-4df4-df0d-304dcacc93c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (3, 5)\n",
            "┌────────────┬──────────────┬─────────────┬──────────────┬─────────────┐\n",
            "│ species    ┆ sepal_length ┆ sepal_width ┆ petal_length ┆ petal_width │\n",
            "│ ---        ┆ ---          ┆ ---         ┆ ---          ┆ ---         │\n",
            "│ str        ┆ f64          ┆ f64         ┆ f64          ┆ f64         │\n",
            "╞════════════╪══════════════╪═════════════╪══════════════╪═════════════╡\n",
            "│ setosa     ┆ 116.9        ┆ 81.7        ┆ 33.2         ┆ 6.1         │\n",
            "│ versicolor ┆ 281.9        ┆ 131.8       ┆ 202.9        ┆ 63.3        │\n",
            "│ virginica  ┆ 324.5        ┆ 146.2       ┆ 273.1        ┆ 99.6        │\n",
            "└────────────┴──────────────┴─────────────┴──────────────┴─────────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### レイジー API"
      ],
      "metadata": {
        "id": "hbYy_EtwFTfa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(\n",
        "    df.lazy()\n",
        "    .filter(pl.col(\"sepal_length\") > 5)\n",
        "    .groupby(\"species\", maintain_order=True)\n",
        "    .agg(pl.all().sum())\n",
        "    .collect()\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "vFTjxSrJFKXO",
        "outputId": "4f5998c3-f7c5-454a-8968-09c7feb7b0c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (3, 5)\n",
              "┌────────────┬──────────────┬─────────────┬──────────────┬─────────────┐\n",
              "│ species    ┆ sepal_length ┆ sepal_width ┆ petal_length ┆ petal_width │\n",
              "│ ---        ┆ ---          ┆ ---         ┆ ---          ┆ ---         │\n",
              "│ str        ┆ f64          ┆ f64         ┆ f64          ┆ f64         │\n",
              "╞════════════╪══════════════╪═════════════╪══════════════╪═════════════╡\n",
              "│ setosa     ┆ 116.9        ┆ 81.7        ┆ 33.2         ┆ 6.1         │\n",
              "│ versicolor ┆ 281.9        ┆ 131.8       ┆ 202.9        ┆ 63.3        │\n",
              "│ virginica  ┆ 324.5        ┆ 146.2       ┆ 273.1        ┆ 99.6        │\n",
              "└────────────┴──────────────┴─────────────┴──────────────┴─────────────┘"
            ],
            "text/html": [
              "<div>\n",
              "<style>\n",
              ".pl-dataframe > thead > tr > th {\n",
              "  text-align: right;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<table border=\"1\" class=\"dataframe pl-dataframe\">\n",
              "<small>shape: (3, 5)</small>\n",
              "<thead>\n",
              "<tr>\n",
              "<th>\n",
              "species\n",
              "</th>\n",
              "<th>\n",
              "sepal_length\n",
              "</th>\n",
              "<th>\n",
              "sepal_width\n",
              "</th>\n",
              "<th>\n",
              "petal_length\n",
              "</th>\n",
              "<th>\n",
              "petal_width\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",
              "<td>\n",
              "f64\n",
              "</td>\n",
              "<td>\n",
              "f64\n",
              "</td>\n",
              "</tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;setosa&quot;\n",
              "</td>\n",
              "<td>\n",
              "116.9\n",
              "</td>\n",
              "<td>\n",
              "81.7\n",
              "</td>\n",
              "<td>\n",
              "33.2\n",
              "</td>\n",
              "<td>\n",
              "6.1\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;versicolor&quot;\n",
              "</td>\n",
              "<td>\n",
              "281.9\n",
              "</td>\n",
              "<td>\n",
              "131.8\n",
              "</td>\n",
              "<td>\n",
              "202.9\n",
              "</td>\n",
              "<td>\n",
              "63.3\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;virginica&quot;\n",
              "</td>\n",
              "<td>\n",
              "324.5\n",
              "</td>\n",
              "<td>\n",
              "146.2\n",
              "</td>\n",
              "<td>\n",
              "273.1\n",
              "</td>\n",
              "<td>\n",
              "99.6\n",
              "</td>\n",
              "</tr>\n",
              "</tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Expressions"
      ],
      "metadata": {
        "id": "_Y4nN6uZFe36"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pl.col(\"foo\").sort().head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "Zglw_I0nFQlm",
        "outputId": "1366d625-def0-43c5-8cbf-8f9b2d3eadbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<polars.internals.expr.expr.Expr at 0x7f6fe84e6790>"
            ],
            "text/html": [
              "col(\"foo\") ASC.slice(offset=0i32, length=2u64)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pl.read_csv(\"https://j.mp/iriscsv\")\n",
        "df.select([\n",
        "    pl.col(\"sepal_length\").sort().head(2),\n",
        "    pl.col(\"sepal_width\").filter(pl.col(\"species\") == \"setosa\").sum()\n",
        "])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "BJO1tWiAFhTm",
        "outputId": "4d14db7b-77f7-43df-cfb7-c2e3665d8ff3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (2, 2)\n",
              "┌──────────────┬─────────────┐\n",
              "│ sepal_length ┆ sepal_width │\n",
              "│ ---          ┆ ---         │\n",
              "│ f64          ┆ f64         │\n",
              "╞══════════════╪═════════════╡\n",
              "│ 4.3          ┆ 170.9       │\n",
              "│ 4.4          ┆ 170.9       │\n",
              "└──────────────┴─────────────┘"
            ],
            "text/html": [
              "<div>\n",
              "<style>\n",
              ".pl-dataframe > thead > tr > th {\n",
              "  text-align: right;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<table border=\"1\" class=\"dataframe pl-dataframe\">\n",
              "<small>shape: (2, 2)</small>\n",
              "<thead>\n",
              "<tr>\n",
              "<th>\n",
              "sepal_length\n",
              "</th>\n",
              "<th>\n",
              "sepal_width\n",
              "</th>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "f64\n",
              "</td>\n",
              "<td>\n",
              "f64\n",
              "</td>\n",
              "</tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr>\n",
              "<td>\n",
              "4.3\n",
              "</td>\n",
              "<td>\n",
              "170.9\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "4.4\n",
              "</td>\n",
              "<td>\n",
              "170.9\n",
              "</td>\n",
              "</tr>\n",
              "</tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(12)\n",
        "\n",
        "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",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUogSuIiFsl0",
        "outputId": "8d7b2ae8-c448-4bc5-9f0e-0cf597aa7471"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (5, 4)\n",
            "┌──────┬───────┬──────────┬────────┐\n",
            "│ nrs  ┆ names ┆ random   ┆ groups │\n",
            "│ ---  ┆ ---   ┆ ---      ┆ ---    │\n",
            "│ i64  ┆ str   ┆ f64      ┆ str    │\n",
            "╞══════╪═══════╪══════════╪════════╡\n",
            "│ 1    ┆ foo   ┆ 0.154163 ┆ A      │\n",
            "│ 2    ┆ ham   ┆ 0.74005  ┆ A      │\n",
            "│ 3    ┆ spam  ┆ 0.263315 ┆ B      │\n",
            "│ null ┆ egg   ┆ 0.533739 ┆ C      │\n",
            "│ 5    ┆ null  ┆ 0.014575 ┆ B      │\n",
            "└──────┴───────┴──────────┴────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Count unique values"
      ],
      "metadata": {
        "id": "-jwfokUXGozr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "out = df.select(\n",
        "    [\n",
        "        pl.col(\"names\").n_unique().alias(\"unique_names_1\"),\n",
        "        pl.col(\"names\").unique().count().alias(\"unique_names_2\"),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNhigRFkGEaU",
        "outputId": "19fed44d-234f-4b07-b789-a4a505d0f725"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (1, 2)\n",
            "┌────────────────┬────────────────┐\n",
            "│ unique_names_1 ┆ unique_names_2 │\n",
            "│ ---            ┆ ---            │\n",
            "│ u32            ┆ u32            │\n",
            "╞════════════════╪════════════════╡\n",
            "│ 5              ┆ 5              │\n",
            "└────────────────┴────────────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Various aggregations"
      ],
      "metadata": {
        "id": "btwn-cnuGy06"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "out = df.select(\n",
        "    [\n",
        "        pl.sum(\"random\").alias(\"sum\"),\n",
        "        pl.min(\"random\").alias(\"min\"),\n",
        "        pl.max(\"random\").alias(\"max\"),\n",
        "        pl.col(\"random\").max().alias(\"other_max\"),\n",
        "        pl.std(\"random\").alias(\"std dev\"),\n",
        "        pl.var(\"random\").alias(\"variance\"),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StfXqcRcGrHN",
        "outputId": "7f8e92b6-cec4-4f1f-9e2e-e7f5301d66ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (1, 6)\n",
            "┌──────────┬──────────┬─────────┬───────────┬──────────┬──────────┐\n",
            "│ sum      ┆ min      ┆ max     ┆ other_max ┆ std dev  ┆ variance │\n",
            "│ ---      ┆ ---      ┆ ---     ┆ ---       ┆ ---      ┆ ---      │\n",
            "│ f64      ┆ f64      ┆ f64     ┆ f64       ┆ f64      ┆ f64      │\n",
            "╞══════════╪══════════╪═════════╪═══════════╪══════════╪══════════╡\n",
            "│ 1.705842 ┆ 0.014575 ┆ 0.74005 ┆ 0.74005   ┆ 0.293209 ┆ 0.085971 │\n",
            "└──────────┴──────────┴─────────┴───────────┴──────────┴──────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Filter and conditionals"
      ],
      "metadata": {
        "id": "uAh1-frEG7Tr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "out = df.select(\n",
        "    [\n",
        "        pl.col(\"names\").filter(pl.col(\"names\").str.contains(r\"am$\")).count(),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GX3StH1CG2IY",
        "outputId": "f260af59-6513-41ad-b88d-5135b0fca195"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (1, 1)\n",
            "┌───────┐\n",
            "│ names │\n",
            "│ ---   │\n",
            "│ u32   │\n",
            "╞═══════╡\n",
            "│ 2     │\n",
            "└───────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Binary functions and modification"
      ],
      "metadata": {
        "id": "qUuOFfoGHD0F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "out = df.select(\n",
        "    [\n",
        "        pl.when(pl.col(\"random\") > 0.5).then(0).otherwise(pl.col(\"random\")) * pl.sum(\"nrs\"),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6isyy_DLHFal",
        "outputId": "e2f1ee8c-019a-4548-944f-3551c5ba0685"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (5, 1)\n",
            "┌──────────┐\n",
            "│ literal  │\n",
            "│ ---      │\n",
            "│ f64      │\n",
            "╞══════════╡\n",
            "│ 1.695791 │\n",
            "│ 0.0      │\n",
            "│ 2.896465 │\n",
            "│ 0.0      │\n",
            "│ 0.160325 │\n",
            "└──────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Window expressions"
      ],
      "metadata": {
        "id": "S5HPa2TPHaIe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.select(\n",
        "    [\n",
        "        pl.col(\"*\"),  # select all\n",
        "        pl.col(\"random\").sum().over(\"groups\").alias(\"sum[random]/groups\"),\n",
        "        pl.col(\"random\").list().over(\"names\").alias(\"random/name\"),\n",
        "    ]\n",
        ")\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9v-nxs2JHckL",
        "outputId": "2f37d2e3-d3b0-4c57-9115-2641bf6e6774"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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   ┆ 0.154163 ┆ A      ┆ 0.894213           ┆ [0.154163]  │\n",
            "│ 2    ┆ ham   ┆ 0.74005  ┆ A      ┆ 0.894213           ┆ [0.74005]   │\n",
            "│ 3    ┆ spam  ┆ 0.263315 ┆ B      ┆ 0.27789            ┆ [0.263315]  │\n",
            "│ null ┆ egg   ┆ 0.533739 ┆ C      ┆ 0.533739           ┆ [0.533739]  │\n",
            "│ 5    ┆ null  ┆ 0.014575 ┆ B      ┆ 0.27789            ┆ [0.014575]  │\n",
            "└──────┴───────┴──────────┴────────┴────────────────────┴─────────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### シンタックスシュガー"
      ],
      "metadata": {
        "id": "yrzOe9eGIvAq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"groups\").agg([pl.col(\"sum[random]/groups\").sum()])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "cxHlEBaEHeY4",
        "outputId": "0d048011-1bf6-4731-c3b2-ff58af211491"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (3, 2)\n",
              "┌────────┬────────────────────┐\n",
              "│ groups ┆ sum[random]/groups │\n",
              "│ ---    ┆ ---                │\n",
              "│ str    ┆ f64                │\n",
              "╞════════╪════════════════════╡\n",
              "│ A      ┆ 1.788425           │\n",
              "│ C      ┆ 0.533739           │\n",
              "│ B      ┆ 0.55578            │\n",
              "└────────┴────────────────────┘"
            ],
            "text/html": [
              "<div>\n",
              "<style>\n",
              ".pl-dataframe > thead > tr > th {\n",
              "  text-align: right;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<table border=\"1\" class=\"dataframe pl-dataframe\">\n",
              "<small>shape: (3, 2)</small>\n",
              "<thead>\n",
              "<tr>\n",
              "<th>\n",
              "groups\n",
              "</th>\n",
              "<th>\n",
              "sum[random]/groups\n",
              "</th>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "str\n",
              "</td>\n",
              "<td>\n",
              "f64\n",
              "</td>\n",
              "</tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;A&quot;\n",
              "</td>\n",
              "<td>\n",
              "1.788425\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;C&quot;\n",
              "</td>\n",
              "<td>\n",
              "0.533739\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;B&quot;\n",
              "</td>\n",
              "<td>\n",
              "0.55578\n",
              "</td>\n",
              "</tr>\n",
              "</tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(df.lazy().groupby(\"groups\").agg([pl.col(\"sum[random]/groups\").sum()])).collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "OQ9feCs1IiT5",
        "outputId": "854eb789-5435-4ce2-ba9b-23b1bd19308f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (3, 2)\n",
              "┌────────┬────────────────────┐\n",
              "│ groups ┆ sum[random]/groups │\n",
              "│ ---    ┆ ---                │\n",
              "│ str    ┆ f64                │\n",
              "╞════════╪════════════════════╡\n",
              "│ A      ┆ 1.788425           │\n",
              "│ B      ┆ 0.55578            │\n",
              "│ C      ┆ 0.533739           │\n",
              "└────────┴────────────────────┘"
            ],
            "text/html": [
              "<div>\n",
              "<style>\n",
              ".pl-dataframe > thead > tr > th {\n",
              "  text-align: right;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<table border=\"1\" class=\"dataframe pl-dataframe\">\n",
              "<small>shape: (3, 2)</small>\n",
              "<thead>\n",
              "<tr>\n",
              "<th>\n",
              "groups\n",
              "</th>\n",
              "<th>\n",
              "sum[random]/groups\n",
              "</th>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "str\n",
              "</td>\n",
              "<td>\n",
              "f64\n",
              "</td>\n",
              "</tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;A&quot;\n",
              "</td>\n",
              "<td>\n",
              "1.788425\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;B&quot;\n",
              "</td>\n",
              "<td>\n",
              "0.55578\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;C&quot;\n",
              "</td>\n",
              "<td>\n",
              "0.533739\n",
              "</td>\n",
              "</tr>\n",
              "</tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### コンテキストを選択"
      ],
      "metadata": {
        "id": "HYDtqbfhJAYc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "out = df.select(\n",
        "    [\n",
        "        pl.sum(\"nrs\"),\n",
        "        pl.col(\"names\").sort(),\n",
        "        pl.col(\"names\").first().alias(\"first name\"),\n",
        "        (pl.mean(\"nrs\") * 10).alias(\"10xnrs\"),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3z_wOSnI0bO",
        "outputId": "77cfa2c8-5c46-4711-cef7-759a2468b20a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (5, 4)\n",
            "┌─────┬───────┬────────────┬────────┐\n",
            "│ nrs ┆ names ┆ first name ┆ 10xnrs │\n",
            "│ --- ┆ ---   ┆ ---        ┆ ---    │\n",
            "│ i64 ┆ str   ┆ str        ┆ f64    │\n",
            "╞═════╪═══════╪════════════╪════════╡\n",
            "│ 11  ┆ null  ┆ foo        ┆ 27.5   │\n",
            "│ 11  ┆ egg   ┆ foo        ┆ 27.5   │\n",
            "│ 11  ┆ foo   ┆ foo        ┆ 27.5   │\n",
            "│ 11  ┆ ham   ┆ foo        ┆ 27.5   │\n",
            "│ 11  ┆ spam  ┆ foo        ┆ 27.5   │\n",
            "└─────┴───────┴────────────┴────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.with_columns(\n",
        "    [\n",
        "        pl.sum(\"nrs\").alias(\"nrs_sum\"),\n",
        "        pl.col(\"random\").count().alias(\"count\"),\n",
        "    ]\n",
        ")\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLAlEuRmKByD",
        "outputId": "a98a3552-4783-473b-eb8c-0d41f3f80024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (5, 8)\n",
            "┌──────┬───────┬──────────┬────────┬────────────────────┬─────────────┬─────────┬───────┐\n",
            "│ nrs  ┆ names ┆ random   ┆ groups ┆ sum[random]/groups ┆ random/name ┆ nrs_sum ┆ count │\n",
            "│ ---  ┆ ---   ┆ ---      ┆ ---    ┆ ---                ┆ ---         ┆ ---     ┆ ---   │\n",
            "│ i64  ┆ str   ┆ f64      ┆ str    ┆ f64                ┆ list[f64]   ┆ i64     ┆ u32   │\n",
            "╞══════╪═══════╪══════════╪════════╪════════════════════╪═════════════╪═════════╪═══════╡\n",
            "│ 1    ┆ foo   ┆ 0.154163 ┆ A      ┆ 0.894213           ┆ [0.154163]  ┆ 11      ┆ 5     │\n",
            "│ 2    ┆ ham   ┆ 0.74005  ┆ A      ┆ 0.894213           ┆ [0.74005]   ┆ 11      ┆ 5     │\n",
            "│ 3    ┆ spam  ┆ 0.263315 ┆ B      ┆ 0.27789            ┆ [0.263315]  ┆ 11      ┆ 5     │\n",
            "│ null ┆ egg   ┆ 0.533739 ┆ C      ┆ 0.533739           ┆ [0.533739]  ┆ 11      ┆ 5     │\n",
            "│ 5    ┆ null  ┆ 0.014575 ┆ B      ┆ 0.27789            ┆ [0.014575]  ┆ 11      ┆ 5     │\n",
            "└──────┴───────┴──────────┴────────┴────────────────────┴─────────────┴─────────┴───────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out = df.groupby(\"groups\").agg(\n",
        "    [\n",
        "        pl.sum(\"nrs\"),  # sum nrs by groups\n",
        "        pl.col(\"random\").count().alias(\"count\"),  # count group members\n",
        "        # sum random where name != null\n",
        "        pl.col(\"random\").filter(pl.col(\"names\").is_not_null()).sum().suffix(\"_sum\"),\n",
        "        pl.col(\"names\").reverse().alias((\"reversed names\")),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQsduhb3OjPw",
        "outputId": "94fed105-636d-4ea2-ef92-0c1a469b6951"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (3, 5)\n",
            "┌────────┬──────┬───────┬────────────┬────────────────┐\n",
            "│ groups ┆ nrs  ┆ count ┆ random_sum ┆ reversed names │\n",
            "│ ---    ┆ ---  ┆ ---   ┆ ---        ┆ ---            │\n",
            "│ str    ┆ i64  ┆ u32   ┆ f64        ┆ list[str]      │\n",
            "╞════════╪══════╪═══════╪════════════╪════════════════╡\n",
            "│ C      ┆ null ┆ 1     ┆ 0.533739   ┆ [\"egg\"]        │\n",
            "│ A      ┆ 3    ┆ 2     ┆ 0.894213   ┆ [\"ham\", \"foo\"] │\n",
            "│ B      ┆ 8    ┆ 2     ┆ 0.263315   ┆ [null, \"spam\"] │\n",
            "└────────┴──────┴───────┴────────────┴────────────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://theunitedstates.io/congress-legislators/legislators-historical.csv\"\n",
        "dtypes = {\n",
        "    \"first_name\": pl.Categorical,\n",
        "    \"gender\": pl.Categorical,\n",
        "    \"type\": pl.Categorical,\n",
        "    \"state\": pl.Categorical,\n",
        "    \"party\": pl.Categorical,\n",
        "}\n",
        "\n",
        "dataset = pl.read_csv(url, dtypes=dtypes).with_columns(pl.col(\"birthday\").str.strptime(pl.Date, strict=False))"
      ],
      "metadata": {
        "id": "x1BAXcAdOvqY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Basic aggregations"
      ],
      "metadata": {
        "id": "1Hp4-3bgA8sF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q = (\n",
        "    dataset.lazy()\n",
        "    .groupby(\"first_name\")\n",
        "    .agg(\n",
        "        [\n",
        "            pl.count(),\n",
        "            pl.col(\"gender\"),\n",
        "            pl.first(\"last_name\"),\n",
        "        ]\n",
        "    )\n",
        "    .sort(\"count\", descending=True)\n",
        "    .limit(5)\n",
        ")\n",
        "\n",
        "df = q.collect()"
      ],
      "metadata": {
        "id": "9rLTncRgPKU_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "q = (\n",
        "    dataset.lazy()\n",
        "    .groupby(\"first_name\")\n",
        "    .agg(\n",
        "        [\n",
        "            pl.count(),\n",
        "            pl.col(\"gender\"),\n",
        "            pl.first(\"last_name\"),\n",
        "        ]\n",
        "    )\n",
        "    .sort(\"count\", descending=True)\n",
        "    .limit(5)\n",
        ")\n",
        "\n",
        "df = q.collect()"
      ],
      "metadata": {
        "id": "jdQrEk2oA339"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conditionals"
      ],
      "metadata": {
        "id": "UpjmAl_0BIU9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q = (\n",
        "    dataset.lazy()\n",
        "    .groupby(\"state\")\n",
        "    .agg(\n",
        "        [\n",
        "            (pl.col(\"party\") == \"Anti-Administration\").sum().alias(\"anti\"),\n",
        "            (pl.col(\"party\") == \"Pro-Administration\").sum().alias(\"pro\"),\n",
        "        ]\n",
        "    )\n",
        "    .sort(\"pro\", descending=True)\n",
        "    .limit(5)\n",
        ")\n",
        "\n",
        "df = q.collect()"
      ],
      "metadata": {
        "id": "T797kpKQBFSu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "q = (\n",
        "    dataset.lazy()\n",
        "    .groupby([\"state\", \"party\"])\n",
        "    .agg([pl.count(\"party\").alias(\"count\")])\n",
        "    .filter((pl.col(\"party\") == \"Anti-Administration\") | (pl.col(\"party\") == \"Pro-Administration\"))\n",
        "    .sort(\"count\", descending=True)\n",
        "    .limit(5)\n",
        ")\n",
        "\n",
        "df = q.collect()"
      ],
      "metadata": {
        "id": "yO_tFZhBBMiE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ManualSum"
      ],
      "metadata": {
        "id": "Na3zWuwEBTZt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pl.DataFrame(\n",
        "    {\n",
        "        \"a\": [1, 2, 3],\n",
        "        \"b\": [10, 20, 30],\n",
        "    }\n",
        ")\n",
        "\n",
        "out = df.select(\n",
        "    pl.fold(acc=pl.lit(0), f=lambda acc, x: acc + x, exprs=pl.col(\"*\")).alias(\"sum\"),\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wr9c8L2-BYmB",
        "outputId": "5e430e5f-3134-4975-9330-0992273791c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (3, 1)\n",
            "┌─────┐\n",
            "│ sum │\n",
            "│ --- │\n",
            "│ i64 │\n",
            "╞═════╡\n",
            "│ 11  │\n",
            "│ 22  │\n",
            "│ 33  │\n",
            "└─────┘\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-30-b5be6099a7ee>:9: DeprecationWarning: `f` is deprecated as an argument to `fold`; use `function` instead.\n",
            "  pl.fold(acc=pl.lit(0), f=lambda acc, x: acc + x, exprs=pl.col(\"*\")).alias(\"sum\"),\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conditional"
      ],
      "metadata": {
        "id": "6P3Wh-LfE-rr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pl.DataFrame(\n",
        "    {\n",
        "        \"a\": [1, 2, 3],\n",
        "        \"b\": [0, 1, 2],\n",
        "    }\n",
        ")\n",
        "\n",
        "out = df.filter(\n",
        "    pl.fold(\n",
        "        acc=pl.lit(True),\n",
        "        f=lambda acc, x: acc & x,\n",
        "        exprs=pl.col(\"*\") > 1,\n",
        "    )\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ch55hgbE5du",
        "outputId": "663746c4-5047-49f4-ac8a-f00a8d7a8794"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (1, 2)\n",
            "┌─────┬─────┐\n",
            "│ a   ┆ b   │\n",
            "│ --- ┆ --- │\n",
            "│ i64 ┆ i64 │\n",
            "╞═════╪═════╡\n",
            "│ 3   ┆ 2   │\n",
            "└─────┴─────┘\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-b1e209061ac9>:9: DeprecationWarning: `f` is deprecated as an argument to `fold`; use `function` instead.\n",
            "  pl.fold(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Folds and string data"
      ],
      "metadata": {
        "id": "ZruUCI7nFE1f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pl.DataFrame(\n",
        "    {\n",
        "        \"a\": [\"a\", \"b\", \"c\"],\n",
        "        \"b\": [1, 2, 3],\n",
        "    }\n",
        ")\n",
        "\n",
        "out = df.select(\n",
        "    [\n",
        "        pl.concat_str([\"a\", \"b\"]),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiEP2slcFA9N",
        "outputId": "ecfb1379-61a2-4248-d4ac-39b5fd9876b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (3, 1)\n",
            "┌─────┐\n",
            "│ a   │\n",
            "│ --- │\n",
            "│ str │\n",
            "╞═════╡\n",
            "│ a1  │\n",
            "│ b2  │\n",
            "│ c3  │\n",
            "└─────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Window functions"
      ],
      "metadata": {
        "id": "boRXU3TmFfnU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# then let's load some csv data with information about pokemon\n",
        "df = pl.read_csv(\n",
        "    \"https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv\"\n",
        ")\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 882
        },
        "id": "1rgKk75eFHYs",
        "outputId": "e98e93eb-131d-45c7-a2ff-9bac97ea6c66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (163, 13)\n",
              "┌─────┬───────────────────────┬─────────┬────────┬─────┬─────────┬───────┬────────────┬───────────┐\n",
              "│ #   ┆ Name                  ┆ Type 1  ┆ Type 2 ┆ ... ┆ Sp. Def ┆ Speed ┆ Generation ┆ Legendary │\n",
              "│ --- ┆ ---                   ┆ ---     ┆ ---    ┆     ┆ ---     ┆ ---   ┆ ---        ┆ ---       │\n",
              "│ i64 ┆ str                   ┆ str     ┆ str    ┆     ┆ i64     ┆ i64   ┆ i64        ┆ bool      │\n",
              "╞═════╪═══════════════════════╪═════════╪════════╪═════╪═════════╪═══════╪════════════╪═══════════╡\n",
              "│ 1   ┆ Bulbasaur             ┆ Grass   ┆ Poison ┆ ... ┆ 65      ┆ 45    ┆ 1          ┆ false     │\n",
              "│ 2   ┆ Ivysaur               ┆ Grass   ┆ Poison ┆ ... ┆ 80      ┆ 60    ┆ 1          ┆ false     │\n",
              "│ 3   ┆ Venusaur              ┆ Grass   ┆ Poison ┆ ... ┆ 100     ┆ 80    ┆ 1          ┆ false     │\n",
              "│ 3   ┆ VenusaurMega Venusaur ┆ Grass   ┆ Poison ┆ ... ┆ 120     ┆ 80    ┆ 1          ┆ false     │\n",
              "│ ... ┆ ...                   ┆ ...     ┆ ...    ┆ ... ┆ ...     ┆ ...   ┆ ...        ┆ ...       │\n",
              "│ 147 ┆ Dratini               ┆ Dragon  ┆ null   ┆ ... ┆ 50      ┆ 50    ┆ 1          ┆ false     │\n",
              "│ 148 ┆ Dragonair             ┆ Dragon  ┆ null   ┆ ... ┆ 70      ┆ 70    ┆ 1          ┆ false     │\n",
              "│ 149 ┆ Dragonite             ┆ Dragon  ┆ Flying ┆ ... ┆ 100     ┆ 80    ┆ 1          ┆ false     │\n",
              "│ 150 ┆ Mewtwo                ┆ Psychic ┆ null   ┆ ... ┆ 90      ┆ 130   ┆ 1          ┆ true      │\n",
              "└─────┴───────────────────────┴─────────┴────────┴─────┴─────────┴───────┴────────────┴───────────┘"
            ],
            "text/html": [
              "<div>\n",
              "<style>\n",
              ".pl-dataframe > thead > tr > th {\n",
              "  text-align: right;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<table border=\"1\" class=\"dataframe pl-dataframe\">\n",
              "<small>shape: (163, 13)</small>\n",
              "<thead>\n",
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              "<th>\n",
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              "<th>\n",
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              "<td>\n",
              "&quot;Fire&quot;\n",
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              "534\n",
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              "78\n",
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              "84\n",
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              "109\n",
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              "314\n",
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              "48\n",
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              "<td>\n",
              "&quot;Water&quot;\n",
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              "405\n",
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              "65\n",
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              "70\n",
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              "<td>\n",
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              "515\n",
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              "60\n",
              "</td>\n",
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              "75\n",
              "</td>\n",
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              "<tr>\n",
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              "142\n",
              "</td>\n",
              "<td>\n",
              "&quot;AerodactylMega...\n",
              "</td>\n",
              "<td>\n",
              "&quot;Rock&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
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              "<td>\n",
              "615\n",
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              "80\n",
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              "135\n",
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              "70\n",
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              "<td>\n",
              "110\n",
              "</td>\n",
              "<td>\n",
              "30\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "144\n",
              "</td>\n",
              "<td>\n",
              "&quot;Articuno&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Ice&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "580\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "85\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "95\n",
              "</td>\n",
              "<td>\n",
              "125\n",
              "</td>\n",
              "<td>\n",
              "85\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "true\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "145\n",
              "</td>\n",
              "<td>\n",
              "&quot;Zapdos&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Electric&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "580\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "85\n",
              "</td>\n",
              "<td>\n",
              "125\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "true\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "146\n",
              "</td>\n",
              "<td>\n",
              "&quot;Moltres&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Fire&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "580\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "125\n",
              "</td>\n",
              "<td>\n",
              "85\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "true\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "147\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dratini&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dragon&quot;\n",
              "</td>\n",
              "<td>\n",
              "null\n",
              "</td>\n",
              "<td>\n",
              "300\n",
              "</td>\n",
              "<td>\n",
              "41\n",
              "</td>\n",
              "<td>\n",
              "64\n",
              "</td>\n",
              "<td>\n",
              "45\n",
              "</td>\n",
              "<td>\n",
              "50\n",
              "</td>\n",
              "<td>\n",
              "50\n",
              "</td>\n",
              "<td>\n",
              "50\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "148\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dragonair&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dragon&quot;\n",
              "</td>\n",
              "<td>\n",
              "null\n",
              "</td>\n",
              "<td>\n",
              "420\n",
              "</td>\n",
              "<td>\n",
              "61\n",
              "</td>\n",
              "<td>\n",
              "84\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "70\n",
              "</td>\n",
              "<td>\n",
              "70\n",
              "</td>\n",
              "<td>\n",
              "70\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "149\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dragonite&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dragon&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "600\n",
              "</td>\n",
              "<td>\n",
              "91\n",
              "</td>\n",
              "<td>\n",
              "134\n",
              "</td>\n",
              "<td>\n",
              "95\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "80\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "150\n",
              "</td>\n",
              "<td>\n",
              "&quot;Mewtwo&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Psychic&quot;\n",
              "</td>\n",
              "<td>\n",
              "null\n",
              "</td>\n",
              "<td>\n",
              "680\n",
              "</td>\n",
              "<td>\n",
              "106\n",
              "</td>\n",
              "<td>\n",
              "110\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "154\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "130\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "true\n",
              "</td>\n",
              "</tr>\n",
              "</tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Groupby Aggregations in selection"
      ],
      "metadata": {
        "id": "GGbzfn6hGiLk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "out = df.select(\n",
        "    [\n",
        "        \"Type 1\",\n",
        "        \"Type 2\",\n",
        "        pl.col(\"Attack\").mean().over(\"Type 1\").alias(\"avg_attack_by_type\"),\n",
        "        pl.col(\"Defense\").mean().over([\"Type 1\", \"Type 2\"]).alias(\"avg_defense_by_type_combination\"),\n",
        "        pl.col(\"Attack\").mean().alias(\"avg_attack\"),\n",
        "    ]\n",
        ")\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 882
        },
        "id": "P3Dpgt5gFiVV",
        "outputId": "92752191-6f27-473b-bd7a-040ae4209734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (163, 13)\n",
              "┌─────┬───────────────────────┬─────────┬────────┬─────┬─────────┬───────┬────────────┬───────────┐\n",
              "│ #   ┆ Name                  ┆ Type 1  ┆ Type 2 ┆ ... ┆ Sp. Def ┆ Speed ┆ Generation ┆ Legendary │\n",
              "│ --- ┆ ---                   ┆ ---     ┆ ---    ┆     ┆ ---     ┆ ---   ┆ ---        ┆ ---       │\n",
              "│ i64 ┆ str                   ┆ str     ┆ str    ┆     ┆ i64     ┆ i64   ┆ i64        ┆ bool      │\n",
              "╞═════╪═══════════════════════╪═════════╪════════╪═════╪═════════╪═══════╪════════════╪═══════════╡\n",
              "│ 1   ┆ Bulbasaur             ┆ Grass   ┆ Poison ┆ ... ┆ 65      ┆ 45    ┆ 1          ┆ false     │\n",
              "│ 2   ┆ Ivysaur               ┆ Grass   ┆ Poison ┆ ... ┆ 80      ┆ 60    ┆ 1          ┆ false     │\n",
              "│ 3   ┆ Venusaur              ┆ Grass   ┆ Poison ┆ ... ┆ 100     ┆ 80    ┆ 1          ┆ false     │\n",
              "│ 3   ┆ VenusaurMega Venusaur ┆ Grass   ┆ Poison ┆ ... ┆ 120     ┆ 80    ┆ 1          ┆ false     │\n",
              "│ ... ┆ ...                   ┆ ...     ┆ ...    ┆ ... ┆ ...     ┆ ...   ┆ ...        ┆ ...       │\n",
              "│ 147 ┆ Dratini               ┆ Dragon  ┆ null   ┆ ... ┆ 50      ┆ 50    ┆ 1          ┆ false     │\n",
              "│ 148 ┆ Dragonair             ┆ Dragon  ┆ null   ┆ ... ┆ 70      ┆ 70    ┆ 1          ┆ false     │\n",
              "│ 149 ┆ Dragonite             ┆ Dragon  ┆ Flying ┆ ... ┆ 100     ┆ 80    ┆ 1          ┆ false     │\n",
              "│ 150 ┆ Mewtwo                ┆ Psychic ┆ null   ┆ ... ┆ 90      ┆ 130   ┆ 1          ┆ true      │\n",
              "└─────┴───────────────────────┴─────────┴────────┴─────┴─────────┴───────┴────────────┴───────────┘"
            ],
            "text/html": [
              "<div>\n",
              "<style>\n",
              ".pl-dataframe > thead > tr > th {\n",
              "  text-align: right;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<table border=\"1\" class=\"dataframe pl-dataframe\">\n",
              "<small>shape: (163, 13)</small>\n",
              "<thead>\n",
              "<tr>\n",
              "<th>\n",
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              "<th>\n",
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              "<th>\n",
              "Type 2\n",
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              "<th>\n",
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              "<th>\n",
              "HP\n",
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              "<th>\n",
              "Attack\n",
              "</th>\n",
              "<th>\n",
              "Defense\n",
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              "<th>\n",
              "Sp. Atk\n",
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              "<th>\n",
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              "<th>\n",
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              "</td>\n",
              "<td>\n",
              "&quot;Poison&quot;\n",
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              "318\n",
              "</td>\n",
              "<td>\n",
              "45\n",
              "</td>\n",
              "<td>\n",
              "49\n",
              "</td>\n",
              "<td>\n",
              "49\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "45\n",
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              "<td>\n",
              "&quot;Grass&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Poison&quot;\n",
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              "405\n",
              "</td>\n",
              "<td>\n",
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              "<td>\n",
              "63\n",
              "</td>\n",
              "<td>\n",
              "80\n",
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              "80\n",
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              "<tr>\n",
              "<td>\n",
              "3\n",
              "</td>\n",
              "<td>\n",
              "&quot;Venusaur&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Grass&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Poison&quot;\n",
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              "<td>\n",
              "525\n",
              "</td>\n",
              "<td>\n",
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              "83\n",
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              "100\n",
              "</td>\n",
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              "100\n",
              "</td>\n",
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              "80\n",
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              "<tr>\n",
              "<td>\n",
              "3\n",
              "</td>\n",
              "<td>\n",
              "&quot;VenusaurMega V...\n",
              "</td>\n",
              "<td>\n",
              "&quot;Grass&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Poison&quot;\n",
              "</td>\n",
              "<td>\n",
              "625\n",
              "</td>\n",
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              "80\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "123\n",
              "</td>\n",
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              "122\n",
              "</td>\n",
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              "120\n",
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              "80\n",
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              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "4\n",
              "</td>\n",
              "<td>\n",
              "&quot;Charmander&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Fire&quot;\n",
              "</td>\n",
              "<td>\n",
              "null\n",
              "</td>\n",
              "<td>\n",
              "309\n",
              "</td>\n",
              "<td>\n",
              "39\n",
              "</td>\n",
              "<td>\n",
              "52\n",
              "</td>\n",
              "<td>\n",
              "43\n",
              "</td>\n",
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              "60\n",
              "</td>\n",
              "<td>\n",
              "50\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "5\n",
              "</td>\n",
              "<td>\n",
              "&quot;Charmeleon&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Fire&quot;\n",
              "</td>\n",
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              "64\n",
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              "<tr>\n",
              "<td>\n",
              "6\n",
              "</td>\n",
              "<td>\n",
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              "<td>\n",
              "&quot;Fire&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "534\n",
              "</td>\n",
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              "78\n",
              "</td>\n",
              "<td>\n",
              "84\n",
              "</td>\n",
              "<td>\n",
              "78\n",
              "</td>\n",
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              "109\n",
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              "85\n",
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              "&quot;CharizardMega ...\n",
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              "<td>\n",
              "&quot;Fire&quot;\n",
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              "634\n",
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              "78\n",
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              "130\n",
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              "634\n",
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              "78\n",
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              "<td>\n",
              "104\n",
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              "78\n",
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              "<td>\n",
              "159\n",
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              "<td>\n",
              "115\n",
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              "<td>\n",
              "7\n",
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              "<td>\n",
              "&quot;Water&quot;\n",
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              "null\n",
              "</td>\n",
              "<td>\n",
              "314\n",
              "</td>\n",
              "<td>\n",
              "44\n",
              "</td>\n",
              "<td>\n",
              "48\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "50\n",
              "</td>\n",
              "<td>\n",
              "64\n",
              "</td>\n",
              "<td>\n",
              "43\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "8\n",
              "</td>\n",
              "<td>\n",
              "&quot;Wartortle&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Water&quot;\n",
              "</td>\n",
              "<td>\n",
              "null\n",
              "</td>\n",
              "<td>\n",
              "405\n",
              "</td>\n",
              "<td>\n",
              "59\n",
              "</td>\n",
              "<td>\n",
              "63\n",
              "</td>\n",
              "<td>\n",
              "80\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "80\n",
              "</td>\n",
              "<td>\n",
              "58\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "9\n",
              "</td>\n",
              "<td>\n",
              "&quot;Blastoise&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Water&quot;\n",
              "</td>\n",
              "<td>\n",
              "null\n",
              "</td>\n",
              "<td>\n",
              "530\n",
              "</td>\n",
              "<td>\n",
              "79\n",
              "</td>\n",
              "<td>\n",
              "83\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "85\n",
              "</td>\n",
              "<td>\n",
              "105\n",
              "</td>\n",
              "<td>\n",
              "78\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "<td>\n",
              "...\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "140\n",
              "</td>\n",
              "<td>\n",
              "&quot;Kabuto&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Rock&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Water&quot;\n",
              "</td>\n",
              "<td>\n",
              "355\n",
              "</td>\n",
              "<td>\n",
              "30\n",
              "</td>\n",
              "<td>\n",
              "80\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "55\n",
              "</td>\n",
              "<td>\n",
              "45\n",
              "</td>\n",
              "<td>\n",
              "55\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "141\n",
              "</td>\n",
              "<td>\n",
              "&quot;Kabutops&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Rock&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Water&quot;\n",
              "</td>\n",
              "<td>\n",
              "495\n",
              "</td>\n",
              "<td>\n",
              "60\n",
              "</td>\n",
              "<td>\n",
              "115\n",
              "</td>\n",
              "<td>\n",
              "105\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "70\n",
              "</td>\n",
              "<td>\n",
              "80\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "142\n",
              "</td>\n",
              "<td>\n",
              "&quot;Aerodactyl&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Rock&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "515\n",
              "</td>\n",
              "<td>\n",
              "80\n",
              "</td>\n",
              "<td>\n",
              "105\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "60\n",
              "</td>\n",
              "<td>\n",
              "75\n",
              "</td>\n",
              "<td>\n",
              "130\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "142\n",
              "</td>\n",
              "<td>\n",
              "&quot;AerodactylMega...\n",
              "</td>\n",
              "<td>\n",
              "&quot;Rock&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "615\n",
              "</td>\n",
              "<td>\n",
              "80\n",
              "</td>\n",
              "<td>\n",
              "135\n",
              "</td>\n",
              "<td>\n",
              "85\n",
              "</td>\n",
              "<td>\n",
              "70\n",
              "</td>\n",
              "<td>\n",
              "95\n",
              "</td>\n",
              "<td>\n",
              "150\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "143\n",
              "</td>\n",
              "<td>\n",
              "&quot;Snorlax&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Normal&quot;\n",
              "</td>\n",
              "<td>\n",
              "null\n",
              "</td>\n",
              "<td>\n",
              "540\n",
              "</td>\n",
              "<td>\n",
              "160\n",
              "</td>\n",
              "<td>\n",
              "110\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "110\n",
              "</td>\n",
              "<td>\n",
              "30\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "144\n",
              "</td>\n",
              "<td>\n",
              "&quot;Articuno&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Ice&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "580\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "85\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "95\n",
              "</td>\n",
              "<td>\n",
              "125\n",
              "</td>\n",
              "<td>\n",
              "85\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "true\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "145\n",
              "</td>\n",
              "<td>\n",
              "&quot;Zapdos&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Electric&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "580\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "85\n",
              "</td>\n",
              "<td>\n",
              "125\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "true\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "146\n",
              "</td>\n",
              "<td>\n",
              "&quot;Moltres&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Fire&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "580\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "125\n",
              "</td>\n",
              "<td>\n",
              "85\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "true\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "147\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dratini&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dragon&quot;\n",
              "</td>\n",
              "<td>\n",
              "null\n",
              "</td>\n",
              "<td>\n",
              "300\n",
              "</td>\n",
              "<td>\n",
              "41\n",
              "</td>\n",
              "<td>\n",
              "64\n",
              "</td>\n",
              "<td>\n",
              "45\n",
              "</td>\n",
              "<td>\n",
              "50\n",
              "</td>\n",
              "<td>\n",
              "50\n",
              "</td>\n",
              "<td>\n",
              "50\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "148\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dragonair&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dragon&quot;\n",
              "</td>\n",
              "<td>\n",
              "null\n",
              "</td>\n",
              "<td>\n",
              "420\n",
              "</td>\n",
              "<td>\n",
              "61\n",
              "</td>\n",
              "<td>\n",
              "84\n",
              "</td>\n",
              "<td>\n",
              "65\n",
              "</td>\n",
              "<td>\n",
              "70\n",
              "</td>\n",
              "<td>\n",
              "70\n",
              "</td>\n",
              "<td>\n",
              "70\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "149\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dragonite&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Dragon&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Flying&quot;\n",
              "</td>\n",
              "<td>\n",
              "600\n",
              "</td>\n",
              "<td>\n",
              "91\n",
              "</td>\n",
              "<td>\n",
              "134\n",
              "</td>\n",
              "<td>\n",
              "95\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "100\n",
              "</td>\n",
              "<td>\n",
              "80\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "false\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "150\n",
              "</td>\n",
              "<td>\n",
              "&quot;Mewtwo&quot;\n",
              "</td>\n",
              "<td>\n",
              "&quot;Psychic&quot;\n",
              "</td>\n",
              "<td>\n",
              "null\n",
              "</td>\n",
              "<td>\n",
              "680\n",
              "</td>\n",
              "<td>\n",
              "106\n",
              "</td>\n",
              "<td>\n",
              "110\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "154\n",
              "</td>\n",
              "<td>\n",
              "90\n",
              "</td>\n",
              "<td>\n",
              "130\n",
              "</td>\n",
              "<td>\n",
              "1\n",
              "</td>\n",
              "<td>\n",
              "true\n",
              "</td>\n",
              "</tr>\n",
              "</tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Operations per group"
      ],
      "metadata": {
        "id": "GN7kCHGvHSgI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filtered = df.filter(pl.col(\"Type 2\") == \"Psychic\").select(\n",
        "    [\n",
        "        \"Name\",\n",
        "        \"Type 1\",\n",
        "        \"Speed\",\n",
        "    ]\n",
        ")\n",
        "print(filtered)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nZOVkVemGkqT",
        "outputId": "f2cd35be-bf21-405b-8867-6eb1190c5086"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (7, 3)\n",
            "┌─────────────────────┬────────┬───────┐\n",
            "│ Name                ┆ Type 1 ┆ Speed │\n",
            "│ ---                 ┆ ---    ┆ ---   │\n",
            "│ str                 ┆ str    ┆ i64   │\n",
            "╞═════════════════════╪════════╪═══════╡\n",
            "│ Slowpoke            ┆ Water  ┆ 15    │\n",
            "│ Slowbro             ┆ Water  ┆ 30    │\n",
            "│ SlowbroMega Slowbro ┆ Water  ┆ 30    │\n",
            "│ Exeggcute           ┆ Grass  ┆ 40    │\n",
            "│ Exeggutor           ┆ Grass  ┆ 55    │\n",
            "│ Starmie             ┆ Water  ┆ 115   │\n",
            "│ Jynx                ┆ Ice    ┆ 95    │\n",
            "└─────────────────────┴────────┴───────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out = filtered.with_columns(\n",
        "    [\n",
        "        pl.col([\"Name\", \"Speed\"]).sort(descending=True).over(\"Type 1\"),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNOpbNx1HVJI",
        "outputId": "b7b8c8b4-60e5-4771-c9e7-cce334312617"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (7, 3)\n",
            "┌─────────────────────┬────────┬───────┐\n",
            "│ Name                ┆ Type 1 ┆ Speed │\n",
            "│ ---                 ┆ ---    ┆ ---   │\n",
            "│ str                 ┆ str    ┆ i64   │\n",
            "╞═════════════════════╪════════╪═══════╡\n",
            "│ Starmie             ┆ Water  ┆ 115   │\n",
            "│ Slowpoke            ┆ Water  ┆ 30    │\n",
            "│ SlowbroMega Slowbro ┆ Water  ┆ 30    │\n",
            "│ Exeggutor           ┆ Grass  ┆ 55    │\n",
            "│ Exeggcute           ┆ Grass  ┆ 40    │\n",
            "│ Slowbro             ┆ Water  ┆ 15    │\n",
            "│ Jynx                ┆ Ice    ┆ 95    │\n",
            "└─────────────────────┴────────┴───────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Window expression rules"
      ],
      "metadata": {
        "id": "thmqoJe3HqE1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# aggregate and broadcast within a group\n",
        "# output type: -> Int32\n",
        "pl.sum(\"foo\").over(\"groups\")\n",
        "\n",
        "# sum within a group and multiply with group elements\n",
        "# output type: -> Int32\n",
        "(pl.col(\"x\").sum() * pl.col(\"y\")).over(\"groups\")\n",
        "\n",
        "# sum within a group and multiply with group elements \n",
        "# and aggregate the group to a list\n",
        "# output type: -> List(Int32)\n",
        "(pl.col(\"x\").sum() * pl.col(\"y\")).list().over(\"groups\")\n",
        "\n",
        "# note that it will require an explicit `list()` call\n",
        "# sum within a group and multiply with group elements \n",
        "# and aggregate the group to a list\n",
        "# the flatten call explodes that list\n",
        "\n",
        "# This is the fastest method to do things over groups when the groups are sorted\n",
        "(pl.col(\"x\").sum() * pl.col(\"y\")).list().over(\"groups\").flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "F5Aq1HMjHYP3",
        "outputId": "ee2d676a-ba7b-47fe-acbf-5cb614c078c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<polars.internals.expr.expr.Expr at 0x7f6fbb239280>"
            ],
            "text/html": [
              "[(col(\"x\").sum()) * (col(\"y\"))].list().over([col(\"groups\")]).explode()"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out = df.sort(\"Type 1\").select(\n",
        "    [\n",
        "        pl.col(\"Type 1\").head(3).list().over(\"Type 1\").flatten(),\n",
        "        pl.col(\"Name\").sort_by(pl.col(\"Speed\")).head(3).list().over(\"Type 1\").flatten().alias(\"fastest/group\"),\n",
        "        pl.col(\"Name\").sort_by(pl.col(\"Attack\")).head(3).list().over(\"Type 1\").flatten().alias(\"strongest/group\"),\n",
        "        pl.col(\"Name\").sort().head(3).list().over(\"Type 1\").flatten().alias(\"sorted_by_alphabet\"),\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "-Wa8zv02HiMB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Row wise computations"
      ],
      "metadata": {
        "id": "91owcz-6NVCu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grades = pl.DataFrame(\n",
        "    {\n",
        "        \"student\": [\"bas\", \"laura\", \"tim\", \"jenny\"],\n",
        "        \"arithmetic\": [10, 5, 6, 8],\n",
        "        \"biology\": [4, 6, 2, 7],\n",
        "        \"geography\": [8, 4, 9, 7],\n",
        "    }\n",
        ")\n",
        "print(grades)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h78u15xYH2He",
        "outputId": "cf3d32c9-1880-4ef5-baf1-19ee6af82e5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (4, 4)\n",
            "┌─────────┬────────────┬─────────┬───────────┐\n",
            "│ student ┆ arithmetic ┆ biology ┆ geography │\n",
            "│ ---     ┆ ---        ┆ ---     ┆ ---       │\n",
            "│ str     ┆ i64        ┆ i64     ┆ i64       │\n",
            "╞═════════╪════════════╪═════════╪═══════════╡\n",
            "│ bas     ┆ 10         ┆ 4       ┆ 8         │\n",
            "│ laura   ┆ 5          ┆ 6       ┆ 4         │\n",
            "│ tim     ┆ 6          ┆ 2       ┆ 9         │\n",
            "│ jenny   ┆ 8          ┆ 7       ┆ 7         │\n",
            "└─────────┴────────────┴─────────┴───────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out = grades.select([pl.concat_list(pl.all().exclude(\"student\")).alias(\"all_grades\")])\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uxG8-3HFNXg5",
        "outputId": "42527b2b-8274-43c3-bcec-6e84bfa94ada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (4, 1)\n",
            "┌────────────┐\n",
            "│ all_grades │\n",
            "│ ---        │\n",
            "│ list[i64]  │\n",
            "╞════════════╡\n",
            "│ [10, 4, 8] │\n",
            "│ [5, 6, 4]  │\n",
            "│ [6, 2, 9]  │\n",
            "│ [8, 7, 7]  │\n",
            "└────────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Running polars expression on list elements"
      ],
      "metadata": {
        "id": "Ob2xR8JoNkn2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# the percentage rank expression\n",
        "rank_pct = pl.element().rank(descending=True) / pl.col(\"\").count()\n",
        "\n",
        "\n",
        "grades.with_columns(\n",
        "    # create the list of homogeneous data\n",
        "    pl.concat_list(pl.all().exclude(\"student\")).alias(\"all_grades\")\n",
        ").select([\n",
        "    # select all columns except the intermediate list\n",
        "    pl.all().exclude(\"all_grades\"),\n",
        "    # compute the rank by calling `arr.eval`\n",
        "    pl.col(\"all_grades\").arr.eval(rank_pct, parallel=True).alias(\"grades_rank\")\n",
        "])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "xnJOef_ONdR1",
        "outputId": "0bd7fda1-9698-4d21-cfe2-f7e1ab11352e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (4, 5)\n",
              "┌─────────┬────────────┬─────────┬───────────┬────────────────────────────────┐\n",
              "│ student ┆ arithmetic ┆ biology ┆ geography ┆ grades_rank                    │\n",
              "│ ---     ┆ ---        ┆ ---     ┆ ---       ┆ ---                            │\n",
              "│ str     ┆ i64        ┆ i64     ┆ i64       ┆ list[f64]                      │\n",
              "╞═════════╪════════════╪═════════╪═══════════╪════════════════════════════════╡\n",
              "│ bas     ┆ 10         ┆ 4       ┆ 8         ┆ [0.333333, 1.0, 0.666667]      │\n",
              "│ laura   ┆ 5          ┆ 6       ┆ 4         ┆ [0.666667, 0.333333, 1.0]      │\n",
              "│ tim     ┆ 6          ┆ 2       ┆ 9         ┆ [0.666667, 1.0, 0.333333]      │\n",
              "│ jenny   ┆ 8          ┆ 7       ┆ 7         ┆ [0.333333, 0.833333, 0.833333] │\n",
              "└─────────┴────────────┴─────────┴───────────┴────────────────────────────────┘"
            ],
            "text/html": [
              "<div>\n",
              "<style>\n",
              ".pl-dataframe > thead > tr > th {\n",
              "  text-align: right;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<table border=\"1\" class=\"dataframe pl-dataframe\">\n",
              "<small>shape: (4, 5)</small>\n",
              "<thead>\n",
              "<tr>\n",
              "<th>\n",
              "student\n",
              "</th>\n",
              "<th>\n",
              "arithmetic\n",
              "</th>\n",
              "<th>\n",
              "biology\n",
              "</th>\n",
              "<th>\n",
              "geography\n",
              "</th>\n",
              "<th>\n",
              "grades_rank\n",
              "</th>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "str\n",
              "</td>\n",
              "<td>\n",
              "i64\n",
              "</td>\n",
              "<td>\n",
              "i64\n",
              "</td>\n",
              "<td>\n",
              "i64\n",
              "</td>\n",
              "<td>\n",
              "list[f64]\n",
              "</td>\n",
              "</tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;bas&quot;\n",
              "</td>\n",
              "<td>\n",
              "10\n",
              "</td>\n",
              "<td>\n",
              "4\n",
              "</td>\n",
              "<td>\n",
              "8\n",
              "</td>\n",
              "<td>\n",
              "[0.333333, 1.0, 0.666667]\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;laura&quot;\n",
              "</td>\n",
              "<td>\n",
              "5\n",
              "</td>\n",
              "<td>\n",
              "6\n",
              "</td>\n",
              "<td>\n",
              "4\n",
              "</td>\n",
              "<td>\n",
              "[0.666667, 0.333333, 1.0]\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;tim&quot;\n",
              "</td>\n",
              "<td>\n",
              "6\n",
              "</td>\n",
              "<td>\n",
              "2\n",
              "</td>\n",
              "<td>\n",
              "9\n",
              "</td>\n",
              "<td>\n",
              "[0.666667, 1.0, 0.333333]\n",
              "</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<td>\n",
              "&quot;jenny&quot;\n",
              "</td>\n",
              "<td>\n",
              "8\n",
              "</td>\n",
              "<td>\n",
              "7\n",
              "</td>\n",
              "<td>\n",
              "7\n",
              "</td>\n",
              "<td>\n",
              "[0.333333, 0.833333, 0.833333]\n",
              "</td>\n",
              "</tr>\n",
              "</tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Numpy interop"
      ],
      "metadata": {
        "id": "acHibCPWN3L3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pl.DataFrame({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\n",
        "\n",
        "out = df.select(\n",
        "    [\n",
        "        np.log(pl.all()).suffix(\"_log\"),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WlFMoNXgNoJk",
        "outputId": "80f015dd-dc7c-4d3d-fd50-ae2436d1cb59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (3, 2)\n",
            "┌──────────┬──────────┐\n",
            "│ a_log    ┆ b_log    │\n",
            "│ ---      ┆ ---      │\n",
            "│ f64      ┆ f64      │\n",
            "╞══════════╪══════════╡\n",
            "│ 0.0      ┆ 1.386294 │\n",
            "│ 0.693147 ┆ 1.609438 │\n",
            "│ 1.098612 ┆ 1.791759 │\n",
            "└──────────┴──────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pl.DataFrame(\n",
        "    {\n",
        "        \"keys\": [\"a\", \"a\", \"b\"],\n",
        "        \"values\": [10, 7, 1],\n",
        "    }\n",
        ")\n",
        "\n",
        "out = df.groupby(\"keys\", maintain_order=True).agg(\n",
        "    [\n",
        "        pl.col(\"values\").map(lambda s: s.shift()).alias(\"shift_map\"),\n",
        "        pl.col(\"values\").shift().alias(\"shift_expression\"),\n",
        "    ]\n",
        ")\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3ZuaoopN5Vw",
        "outputId": "179d6eff-86e5-46ee-9f10-4c7f30b2ef11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (3, 2)\n",
            "┌──────┬────────┐\n",
            "│ keys ┆ values │\n",
            "│ ---  ┆ ---    │\n",
            "│ str  ┆ i64    │\n",
            "╞══════╪════════╡\n",
            "│ a    ┆ 10     │\n",
            "│ a    ┆ 7      │\n",
            "│ b    ┆ 1      │\n",
            "└──────┴────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### カウンターの追加"
      ],
      "metadata": {
        "id": "mSYzC9TfXWn5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counter = 0\n",
        "\n",
        "\n",
        "def add_counter(val: int) -> int:\n",
        "    global counter\n",
        "    counter += 1\n",
        "    return counter + val\n",
        "\n",
        "\n",
        "out = df.select(\n",
        "    [\n",
        "        pl.col(\"values\").apply(add_counter).alias(\"solution_apply\"),\n",
        "        (pl.col(\"values\") + pl.arange(1, pl.count() + 1)).alias(\"solution_expr\"),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Bcs6PFPWvgJ",
        "outputId": "55b53307-f961-4c08-93e6-66f52e4c6314"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (3, 2)\n",
            "┌────────────────┬───────────────┐\n",
            "│ solution_apply ┆ solution_expr │\n",
            "│ ---            ┆ ---           │\n",
            "│ i64            ┆ i64           │\n",
            "╞════════════════╪═══════════════╡\n",
            "│ 11             ┆ 11            │\n",
            "│ 9              ┆ 9             │\n",
            "│ 4              ┆ 4             │\n",
            "└────────────────┴───────────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 複数の列値の結合"
      ],
      "metadata": {
        "id": "XQ38tOhRXaTa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "[\n",
        "    {\"keys\": \"a\", \"values\": 10},\n",
        "    {\"keys\": \"a\", \"values\": 7},\n",
        "    {\"keys\": \"b\", \"values\": 1},\n",
        "]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TtZ9JBtyXUTz",
        "outputId": "2c0d3c70-520b-4ab9-9a61-86dd04c7eca8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'keys': 'a', 'values': 10},\n",
              " {'keys': 'a', 'values': 7},\n",
              " {'keys': 'b', 'values': 1}]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out = df.select(\n",
        "    [\n",
        "        pl.struct([\"keys\", \"values\"]).apply(lambda x: len(x[\"keys\"]) + x[\"values\"]).alias(\"solution_apply\"),\n",
        "        (pl.col(\"keys\").str.lengths() + pl.col(\"values\")).alias(\"solution_expr\"),\n",
        "    ]\n",
        ")\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5lDJrcNwXcgA",
        "outputId": "851a0772-f000-43df-b6c9-395a9dc75b9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape: (3, 2)\n",
            "┌────────────────┬───────────────┐\n",
            "│ solution_apply ┆ solution_expr │\n",
            "│ ---            ┆ ---           │\n",
            "│ i64            ┆ i64           │\n",
            "╞════════════════╪═══════════════╡\n",
            "│ 11             ┆ 11            │\n",
            "│ 8              ┆ 8             │\n",
            "│ 2              ┆ 2             │\n",
            "└────────────────┴───────────────┘\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZsC7WwVkXfHp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}