{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Polars\n", "\n", "Testing out [Polars](https://pola.rs/)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Defaulting to user installation because normal site-packages is not writeable\n", "Requirement already satisfied: polars[all] in /Users/cmc/Library/Python/3.9/lib/python/site-packages (0.20.3)\n", "Collecting connectorx>=0.3.2\n", " Downloading connectorx-0.3.2-cp39-cp39-macosx_11_0_arm64.whl (43.7 MB)\n", "\u001b[K |████████████████████████████████| 43.7 MB 709 kB/s eta 0:00:01\n", "\u001b[?25hCollecting pydantic\n", " Downloading pydantic-2.5.3-py3-none-any.whl (381 kB)\n", "\u001b[K |████████████████████████████████| 381 kB 31.7 MB/s eta 0:00:01\n", "\u001b[?25hCollecting pandas\n", " Downloading pandas-2.1.4-cp39-cp39-macosx_11_0_arm64.whl (11.0 MB)\n", "\u001b[K |████████████████████████████████| 11.0 MB 46.7 MB/s eta 0:00:01\n", "\u001b[?25hCollecting sqlalchemy\n", " Downloading SQLAlchemy-2.0.25-cp39-cp39-macosx_11_0_arm64.whl (2.1 MB)\n", "\u001b[K |████████████████████████████████| 2.1 MB 8.0 MB/s eta 0:00:01\n", "\u001b[?25hCollecting pyiceberg>=0.5.0\n", " Downloading pyiceberg-0.5.1.tar.gz (418 kB)\n", "\u001b[K |████████████████████████████████| 418 kB 14.7 MB/s eta 0:00:01\n", "\u001b[?25h\u001b[33m WARNING: Value for prefixed-purelib does not match. 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(from importlib-metadata>=4.4->markdown->panel>=0.11.0->hvplot>=0.9.1->polars[all]) (3.17.0)\n", "Requirement already satisfied: setuptools in /Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages (from zope.event->gevent->polars[all]) (58.0.4)\n", "Building wheels for collected packages: pyiceberg\n", " Building wheel for pyiceberg (PEP 517) ... \u001b[?25ldone\n", "\u001b[?25h Created wheel for pyiceberg: filename=pyiceberg-0.5.1-cp39-cp39-macosx_14_0_arm64.whl size=527582 sha256=460f2fb33d28df351dd1f65841874e740e9e5f1d4953f861d877ec4b0e94911c\n", " Stored in directory: /Users/cmc/Library/Caches/pip/wheels/fb/8c/3f/f25c02bddafeae467122e8757c9275c5981cf708f1d182f494\n", "Successfully built pyiceberg\n", "Installing collected packages: tzdata, pytz, mdurl, xyzservices, uc-micro-py, pillow, param, pandas, markdown-it-py, contourpy, tqdm, pyviz-comms, pyct, mdit-py-plugins, markdown, linkify-it-py, bokeh, pydantic-core, panel, colorcet, annotated-types, zope.interface, zope.event, strictyaml, sortedcontainers, rich, pyparsing, pydantic, pyarrow-hotfix, pyarrow, mmhash3, holoviews, greenlet, click, adbc-driver-manager, xlsxwriter, xlsx2csv, sqlalchemy, pyiceberg, hvplot, gevent, deltalake, connectorx, cloudpickle, adbc-driver-sqlite\n", "Successfully installed adbc-driver-manager-0.9.0 adbc-driver-sqlite-0.9.0 annotated-types-0.6.0 bokeh-3.3.3 click-8.1.7 cloudpickle-3.0.0 colorcet-3.0.1 connectorx-0.3.2 contourpy-1.2.0 deltalake-0.15.1 gevent-23.9.1 greenlet-3.0.3 holoviews-1.18.1 hvplot-0.9.1 linkify-it-py-2.0.2 markdown-3.5.1 markdown-it-py-3.0.0 mdit-py-plugins-0.4.0 mdurl-0.1.2 mmhash3-3.0.1 pandas-2.1.4 panel-1.3.6 param-2.0.1 pillow-10.2.0 pyarrow-14.0.2 pyarrow-hotfix-0.6 pyct-0.5.0 pydantic-2.5.3 pydantic-core-2.14.6 pyiceberg-0.5.1 pyparsing-3.1.1 pytz-2023.3.post1 pyviz-comms-3.0.0 rich-13.7.0 sortedcontainers-2.4.0 sqlalchemy-2.0.25 strictyaml-1.7.3 tqdm-4.66.1 tzdata-2023.4 uc-micro-py-1.0.2 xlsx2csv-0.8.1 xlsxwriter-3.1.9 xyzservices-2023.10.1 zope.event-5.0 zope.interface-6.1\n", "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 23.3.2 is available.\n", "You should consider upgrading via the '/Applications/Xcode.app/Contents/Developer/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install 'polars[all]'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import polars as pl\n", "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "shape: (3, 3)\n", "┌─────────┬─────────────────────┬───────┐\n", "│ integer ┆ date ┆ float │\n", "│ --- ┆ --- ┆ --- │\n", "│ i64 ┆ datetime[μs] ┆ f64 │\n", "╞═════════╪═════════════════════╪═══════╡\n", "│ 1 ┆ 2022-01-01 00:00:00 ┆ 4.0 │\n", "│ 2 ┆ 2022-01-02 00:00:00 ┆ 5.0 │\n", "│ 3 ┆ 2022-01-03 00:00:00 ┆ 6.0 │\n", "└─────────┴─────────────────────┴───────┘\n" ] } ], "source": [ "df = pl.DataFrame(\n", " {\n", " \"integer\": [1, 2, 3],\n", " \"date\": [\n", " datetime(2022, 1, 1),\n", " datetime(2022, 1, 2),\n", " datetime(2022, 1, 3),\n", " ],\n", " \"float\": [4.0, 5.0, 6.0],\n", " }\n", ")\n", "\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "shape: (16_598, 11)\n", "┌───────┬─────────────────┬──────────┬──────┬───┬──────────┬──────────┬─────────────┬──────────────┐\n", "│ Rank ┆ Name ┆ Platform ┆ Year ┆ … ┆ EU_Sales ┆ JP_Sales ┆ Other_Sales ┆ Global_Sales │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ i64 ┆ str ┆ str ┆ i64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", "╞═══════╪═════════════════╪══════════╪══════╪═══╪══════════╪══════════╪═════════════╪══════════════╡\n", "│ 1 ┆ Wii Sports ┆ Wii ┆ 2006 ┆ … ┆ 29.02 ┆ 3.77 ┆ 8.46 ┆ 82.74 │\n", "│ 2 ┆ Super Mario ┆ NES ┆ 1985 ┆ … ┆ 3.58 ┆ 6.81 ┆ 0.77 ┆ 40.24 │\n", "│ ┆ Bros. ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ 3 ┆ Mario Kart Wii ┆ Wii ┆ 2008 ┆ … ┆ 12.88 ┆ 3.79 ┆ 3.31 ┆ 35.82 │\n", "│ 4 ┆ Wii Sports ┆ Wii ┆ 2009 ┆ … ┆ 11.01 ┆ 3.28 ┆ 2.96 ┆ 33.0 │\n", "│ ┆ Resort ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ 16597 ┆ Men in Black ┆ GC ┆ 2003 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.01 │\n", "│ ┆ II: Alien ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ Escape ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ 16598 ┆ SCORE ┆ PS2 ┆ 2008 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.01 │\n", "│ ┆ International ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ ┆ Baja 1000: T… ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", "│ 16599 ┆ Know How 2 ┆ DS ┆ 2010 ┆ … ┆ 0.01 ┆ 0.0 ┆ 0.0 ┆ 0.01 │\n", "│ 16600 ┆ Spirits & ┆ GBA ┆ 2003 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.01 │\n", "│ ┆ Spells ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", "└───────┴─────────────────┴──────────┴──────┴───┴──────────┴──────────┴─────────────┴──────────────┘\n" ] } ], "source": [ "df = pl.read_csv(\"../data_files/video_game_sales.csv\", ignore_errors=True)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearGenre
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" ], "text/plain": [ "shape: (2, 2)\n", "┌──────┬───────┐\n", "│ Year ┆ Genre │\n", "│ --- ┆ --- │\n", "│ i64 ┆ str │\n", "╞══════╪═══════╡\n", "│ null ┆ null │\n", "│ null ┆ null │\n", "└──────┴───────┘" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.select(pl.col(\"Year\").sort().head(2), pl.col(\"Genre\").filter(pl.col(\"Year\") == 1).sum())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 2 }