├── .gitignore ├── .python-version ├── LICENSE ├── README.md ├── custom.css ├── polars_intro ├── __init__.py ├── download_data.py ├── layouts │ └── polars_intro.slides.json └── polars_intro.py ├── pyproject.toml ├── requirements.txt └── uv.lock /.gitignore: -------------------------------------------------------------------------------- 1 | # Data downloaded for this tutorial 2 | data/ 3 | 4 | # Byte-compiled / optimized / DLL files 5 | __pycache__/ 6 | *.py[cod] 7 | *$py.class 8 | 9 | # C extensions 10 | *.so 11 | 12 | # Distribution / packaging 13 | .Python 14 | build/ 15 | develop-eggs/ 16 | dist/ 17 | downloads/ 18 | eggs/ 19 | .eggs/ 20 | lib/ 21 | lib64/ 22 | parts/ 23 | sdist/ 24 | var/ 25 | wheels/ 26 | share/python-wheels/ 27 | *.egg-info/ 28 | .installed.cfg 29 | *.egg 30 | MANIFEST 31 | 32 | # PyInstaller 33 | # Usually these files are written by a python script from a template 34 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 35 | *.manifest 36 | *.spec 37 | 38 | # Installer logs 39 | pip-log.txt 40 | pip-delete-this-directory.txt 41 | 42 | # Unit test / coverage reports 43 | htmlcov/ 44 | .tox/ 45 | .nox/ 46 | .coverage 47 | .coverage.* 48 | .cache 49 | nosetests.xml 50 | coverage.xml 51 | *.cover 52 | *.py,cover 53 | .hypothesis/ 54 | .pytest_cache/ 55 | cover/ 56 | 57 | # Translations 58 | *.mo 59 | *.pot 60 | 61 | # Django stuff: 62 | *.log 63 | local_settings.py 64 | db.sqlite3 65 | db.sqlite3-journal 66 | 67 | # Flask stuff: 68 | instance/ 69 | .webassets-cache 70 | 71 | # Scrapy stuff: 72 | .scrapy 73 | 74 | # Sphinx documentation 75 | docs/_build/ 76 | 77 | # PyBuilder 78 | .pybuilder/ 79 | target/ 80 | 81 | # Jupyter Notebook 82 | .ipynb_checkpoints 83 | 84 | # IPython 85 | profile_default/ 86 | ipython_config.py 87 | 88 | # pyenv 89 | # For a library or package, you might want to ignore these files since the code is 90 | # intended to run in multiple environments; otherwise, check them in: 91 | # .python-version 92 | 93 | # pipenv 94 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 95 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 96 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 97 | # install all needed dependencies. 98 | #Pipfile.lock 99 | 100 | # poetry 101 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 102 | # This is especially recommended for binary packages to ensure reproducibility, and is more 103 | # commonly ignored for libraries. 104 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 105 | #poetry.lock 106 | 107 | # pdm 108 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 109 | #pdm.lock 110 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 111 | # in version control. 112 | # https://pdm.fming.dev/latest/usage/project/#working-with-version-control 113 | .pdm.toml 114 | .pdm-python 115 | .pdm-build/ 116 | 117 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 118 | __pypackages__/ 119 | 120 | # Celery stuff 121 | celerybeat-schedule 122 | celerybeat.pid 123 | 124 | # SageMath parsed files 125 | *.sage.py 126 | 127 | # Environments 128 | .env 129 | .venv 130 | env/ 131 | venv/ 132 | ENV/ 133 | env.bak/ 134 | venv.bak/ 135 | 136 | # Spyder project settings 137 | .spyderproject 138 | .spyproject 139 | 140 | # Rope project settings 141 | .ropeproject 142 | 143 | # mkdocs documentation 144 | /site 145 | 146 | # mypy 147 | .mypy_cache/ 148 | .dmypy.json 149 | dmypy.json 150 | 151 | # Pyre type checker 152 | .pyre/ 153 | 154 | # pytype static type analyzer 155 | .pytype/ 156 | 157 | # Cython debug symbols 158 | cython_debug/ 159 | 160 | # PyCharm 161 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 162 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 163 | # and can be added to the global gitignore or merged into this file. For a more nuclear 164 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 165 | #.idea/ 166 | -------------------------------------------------------------------------------- /.python-version: -------------------------------------------------------------------------------- 1 | 3.12 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Ryan Parker 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Intro to [polars](https://pola.rs) 2 | A short demo to introduce the polars dataframe library. 3 | 4 | > [!NOTE] 5 | > You can also [view a static version of this notebook from your browser](https://rparkr.github.io/polars-intro/). 6 | 7 | # Getting started 8 | 1. Clone the repo and move into the folder 9 | 10 | ```shell 11 | git clone https://github.com/rparkr/polars-intro.git 12 | cd polars-intro 13 | ``` 14 | 15 | 2. Create a virtual environment and install requirements. I recommend using [uv](https://docs.astral.sh/uv/), but you can also use Python's built-in `venv` module and `pip`: 16 | 17 |
18 | Using uv 19 | 20 | ```shell 21 | uv venv 22 | uv pip install -e . 23 | ``` 24 | 25 |
26 | 27 |
28 | Using `venv` and `pip` 29 | 30 | ```shell 31 | python -m venv .venv 32 | source .venv/bin/activate 33 | pip install -e . 34 | 35 | # Or: 36 | # pip install -r requirements.txt 37 | ``` 38 |
39 | 40 | 3. Run the app... 41 | ```shell 42 | marimo run polars_intro/polars_intro.py 43 | ``` 44 | 45 | 4. Or edit the notebook: 46 | ```shell 47 | marimo edit polars_intro/polars_intro.py 48 | ``` 49 | -------------------------------------------------------------------------------- /custom.css: -------------------------------------------------------------------------------- 1 | /* Custom CSS for theming marimo notebooks */ 2 | @import url('https://fonts.googleapis.com/css2?family=Noto+Sans:ital,wght@0,100..900;1,100..900&display=swap'); 3 | @import url('https://fonts.googleapis.com/css2?family=Noto+Serif:ital,wght@0,100..900;1,100..900&display=swap'); 4 | @import url('https://fonts.googleapis.com/css2?family=Noto+Sans+Mono:wght@100..900&family=Noto+Sans:ital,wght@0,100..900;1,100..900&display=swap'); 5 | 6 | :root { 7 | --marimo-heading-font: 'Noto Serif', 'sans-serif'; 8 | --marimo-text-font: 'Noto Sans', 'sans-serif'; 9 | --marimo-monospace-font: 'Noto Sans Mono', 'monospace'; 10 | } 11 | 12 | /* Increase paragraph font size and change color */ 13 | /* .paragraph { 14 | font-size: 1.2rem; 15 | color: light-dark(rgb(28, 43, 72), rgb(194, 213, 244)); 16 | } 17 | 18 | .h1 { 19 | font-weight: bold; 20 | } 21 | 22 | .h2 { 23 | font-weight: lighter; 24 | } 25 | 26 | .h3 { 27 | font-size: smaller; 28 | font-weight: bold; 29 | } 30 | 31 | .h4 { 32 | font-size: smaller; 33 | font-weight: lighter; 34 | } */ -------------------------------------------------------------------------------- /polars_intro/__init__.py: -------------------------------------------------------------------------------- 1 | def main() -> None: 2 | print("Hello from polars-intro!") 3 | -------------------------------------------------------------------------------- /polars_intro/download_data.py: -------------------------------------------------------------------------------- 1 | """ 2 | Asynchronously download data used in this tutorial. 3 | """ 4 | 5 | import asyncio 6 | import datetime as dt 7 | import urllib.parse 8 | from pathlib import Path 9 | from zoneinfo import ZoneInfo 10 | 11 | import httpx 12 | 13 | 14 | # Files are published monthly, with a 2-month delay. For simplicity, 15 | # I use a 3-month delay to ensure that the data is available. 16 | def get_data_urls(year: int = None, **kwargs) -> list[str]: 17 | """Get the URLs for all months of Yellow Taxi data in a given year.""" 18 | if not year: 19 | year = dt.date.today().year 20 | today = dt.date.today() 21 | assert (year >= 2009) and (year <= today.year), ( 22 | f"year must be >= 2009 and <= {today.year}, but {year} was given" 23 | ) 24 | if year == today.year: 25 | if today.month <= 3: 26 | print( 27 | "The current year was requested, but data may not yet " 28 | f"be available. Using last year ({today.year - 1}) instead." 29 | ) 30 | year = today.year - 1 31 | # If today is Jan set to Oct; if Feb set to Nov; if Mar set to Dec 32 | end_month = today.month + 9 33 | else: 34 | end_month = dt.date.today().month - 3 35 | data_urls = [ 36 | f"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_{year}-{month:0>2d}.parquet" 37 | for month in range(1, end_month + 1) 38 | ] 39 | return data_urls 40 | 41 | 42 | async def download_taxi_data( 43 | urls: list[str] | None = None, save_dir: str = "data", **kwargs 44 | ) -> None: 45 | """ 46 | Download NYC Yellow Cab taxi dataset to the "data/" directory in the current working directory. 47 | 48 | Parameters 49 | ---------- 50 | urls: list of str 51 | The URLs for which data will be downloaded. 52 | 53 | save_dir: str 54 | The directory where the downloaded data will be saved. 55 | If a relative filepath is provided, this is assumed to be 56 | relative to the current working directory. 57 | 58 | kwargs: keyword-only arguments 59 | `year`: the year for which data will be downloaded, 60 | passed along to get_data_urls() 61 | 62 | Returns 63 | ------- 64 | None 65 | """ 66 | if not urls: 67 | urls = get_data_urls(year=kwargs.get("year")) 68 | # Ensure the urls variable is a list for consistency 69 | if isinstance(urls, str): 70 | urls = [urls] 71 | # Create the folder for saving the data 72 | if not save_dir: 73 | save_dir = "data" 74 | Path(save_dir).mkdir(parents=True, exist_ok=True) 75 | 76 | async def download_single_file(client, url, save_dir) -> str: 77 | """Download a file from a URL, save it, and return its filepath.""" 78 | filepath = Path(save_dir) / Path(url).name 79 | with open(filepath, mode="ba" if not filepath.exists else "bw") as data_file: 80 | async with client.stream("GET", url) as response: 81 | async for chunk in response.aiter_bytes(): 82 | data_file.write(chunk) 83 | return filepath 84 | 85 | async with httpx.AsyncClient() as client: 86 | results = await asyncio.gather( 87 | *[download_single_file(client, url, save_dir) for url in urls] 88 | ) 89 | total_files = len(urls) 90 | max_digits = len(str(total_files)) 91 | for i, result in enumerate(results): 92 | print( 93 | f"{i + 1:>0{max_digits}}/{total_files:>0{max_digits}} | Downloaded file: {result}" 94 | ) 95 | 96 | 97 | def download_weather_data( 98 | start_date: dt.date | None = None, 99 | end_date: dt.date | None = None, 100 | latitude: float = 40.7128, 101 | longitude: float = 74.006, 102 | time_zone: str | ZoneInfo = "America/New_York", 103 | save_dir: str = "data", 104 | filename: str = "weather.json", 105 | ) -> str: 106 | """ 107 | Download hourly weather data from Open-Meteo.com 108 | 109 | Parameters 110 | ---------- 111 | start_date: datetime.date 112 | The start date for historical weather data. 113 | If not set, the first day of the current year 114 | will be used if the current date is after the 115 | third month of the year, otherwise, the start 116 | date will be the first day of the previous year. 117 | This aligns the weather data to the NYC Yellow 118 | Cab Taxi dataset, which has a 2-month delay in 119 | availability (I use a 3-month delay as a buffer). 120 | 121 | end_date: datetime.date 122 | The end date for historical weather data. 123 | If not set, this defaults to the current date if 124 | the current date is after the third month of the 125 | year, otherwise it is the last day of the previous 126 | year. 127 | 128 | latitude: float 129 | The latitude (North/South) coordinate for the location 130 | of interest. Defaults to the coordinate for New York City. 131 | 132 | longitude: float 133 | The longitude (East/West) coordinate for the location 134 | of interest. Defaults to the coordinate for New York City. 135 | 136 | time_zone: str or ZoneInfo object, default = "America/New_York" 137 | The time zone in which the data will be returned. If set to 138 | an emptry string or None, then UTC will be used. 139 | 140 | save_dir: str, default = "data" 141 | The directory (relative to the current working directory) 142 | where the data will be saved. 143 | 144 | filename: str, default = "weather.json" 145 | 146 | Returns 147 | ------- 148 | str: filepath to downloaded data 149 | 150 | Notes 151 | ----- 152 | Data source: https://open-meteo.com/. Open-Meteo's 153 | license permits non-commercial use for up to 154 | 10,000 API calls per day. Since this repository is 155 | used [for educational purposes](https://open-meteo.com/en/terms), it qualifies as 156 | non-commercial use. This function uses the 157 | equivalent of 6-8 API calls because of its long 158 | time horizon. Please respect Open-Meteo's restrictions 159 | and do not repeatedly run this function beyond the 160 | limits set by Open-Meteo, including ensuring that 161 | your use of this API call is non-commercial. 162 | 163 | See also: 164 | - [Open-Meteo's terms of use](https://open-meteo.com/en/terms) 165 | - [Open-Meteo's license]() 166 | - [Open-Meteo's historical data API docs](https://open-meteo.com/en/docs/historical-weather-api) 167 | """ 168 | # If the start date is within the first three months of the year, 169 | # set the date range to the previous year; otherwise, set the range to 170 | # the current year. This aligns with the data publishing timelines for 171 | # the NYC Yellow Taxi dataset. 172 | today = dt.date.today() 173 | if not start_date: 174 | if today.month >= 3: 175 | start_date = dt.date(today.year, 1, 1) 176 | else: 177 | start_date = dt.date(today.year - 1, 1, 1) 178 | if not end_date: 179 | if today.month >= 3: 180 | end_date = today 181 | else: 182 | end_date = dt.date(today.year - 1, 12, 31) 183 | 184 | api_url = ( 185 | "https://archive-api.open-meteo.com/v1/archive?" 186 | f"latitude={latitude}" 187 | f"&longitude={longitude}" 188 | f"&start_date={start_date}" # must be ISO-8601: yyyy-mm-dd, i.e., %Y-%m-%d 189 | f"&end_date={end_date}" 190 | "&hourly=temperature_2m,weather_code,is_day" 191 | ) 192 | if time_zone: 193 | tz_url_encoded = urllib.parse.quote(str(time_zone), safe="") 194 | api_url += f"&timezone={tz_url_encoded}" 195 | # Create the folder if it does not exist 196 | Path(save_dir).mkdir(parents=True, exist_ok=True) 197 | filepath = Path(save_dir) / filename 198 | 199 | response = httpx.get(api_url) 200 | 201 | with open(filepath, mode="wt", encoding="utf8") as data_file: 202 | data_file.write(response.text) 203 | print(f"Saved weather data to: {filepath}") 204 | return str(filepath) 205 | 206 | 207 | def download_weather_codes(save_dir: str = "data") -> str: 208 | """ 209 | Download WMO weather interpretation codes from: https://gist.github.com/stellasphere/9490c195ed2b53c707087c8c2db4ec0c 210 | 211 | Parameters 212 | ---------- 213 | save_dir: str, default = "data" 214 | The directory (relative to the current working directory) 215 | where the data will be saved. 216 | 217 | Returns 218 | ------- 219 | str: filepath to downloaded data 220 | """ 221 | url = "https://gist.githubusercontent.com/stellasphere/9490c195ed2b53c707087c8c2db4ec0c/raw/76b0cb0ef0bfd8a2ec988aa54e30ecd1b483495d/descriptions.json" 222 | filepath = Path(save_dir) / "weather_codes.json" 223 | 224 | response = httpx.get(url) 225 | 226 | with open(filepath, mode="wt", encoding="utf8") as data_file: 227 | data_file.write(response.text) 228 | print(f"Saved weather codes to: {filepath}") 229 | return str(filepath) 230 | 231 | 232 | def download_all(): 233 | asyncio.run(download_taxi_data()) 234 | _ = download_weather_data() 235 | _ = download_weather_codes() 236 | 237 | 238 | # Download data 239 | if __name__ == "__main__": 240 | download_all() 241 | -------------------------------------------------------------------------------- /polars_intro/layouts/polars_intro.slides.json: -------------------------------------------------------------------------------- 1 | { 2 | "type": "slides", 3 | "data": {} 4 | } -------------------------------------------------------------------------------- /polars_intro/polars_intro.py: -------------------------------------------------------------------------------- 1 | import marimo 2 | 3 | __generated_with = "0.10.15" 4 | app = marimo.App(app_title="Polars intro", css_file="../custom.css") 5 | 6 | 7 | @app.cell 8 | def _(mo): 9 | mo.md( 10 | r""" 11 | # Intro to [polars](https://pola.rs) 12 | 13 | A brief introduction to the incredible `polars` dataframe library. 14 | 15 | ![polars logo](https://raw.githubusercontent.com/pola-rs/polars-static/master/banner/polars_github_banner.svg) 16 | 17 | Created by: [Ryan Parker](https://github.com/rparkr), August 2024. Last updated in October 2024. 18 | 19 | This demo is a [marimo notebook](https://marimo.io/), so it is interactive and reactive -- try experimenting with the widgets later on! 20 | """ 21 | ) 22 | return 23 | 24 | 25 | @app.cell 26 | async def _(mo): 27 | from pathlib import Path 28 | 29 | import polars as pl 30 | import download_data 31 | 32 | callout_download = None 33 | # Download data 34 | if not Path("data").exists(): 35 | callout_download = mo.callout( 36 | kind="info", value="Downloading NYC Taxi and weather data" 37 | ) 38 | await download_data.download_taxi_data() 39 | download_data.download_weather_data() 40 | download_data.download_weather_codes() 41 | callout_download 42 | return Path, callout_download, download_data, pl 43 | 44 | 45 | @app.cell(hide_code=True) 46 | def _(mo): 47 | mo.md( 48 | r""" 49 | # Data analysis in Python 50 | As an interpreted language with an easy-to-read syntax, Python is fantastic for data analysis, where rapid iteration enables exploration and accelerates development. 51 | 52 | Since its first release in 2008, [pandas](https://pandas.pydata.org/docs/) has been the de-facto standard for data analysis in Python, but in recent years other libraries have been created which offer distinct advantages. Some of those include: 53 | 54 | - [cuDF](https://docs.rapids.ai/api/cudf/stable/): GPU-accelerated dataframe operations with pandas API support 55 | - [modin](https://modin.readthedocs.io/en/stable/): pandas API running on distributed compute using [Ray](https://www.ray.io/) or [Dask](https://www.dask.org/) as a backend 56 | - [ibis](https://ibis-project.org/): dataframe library supporting dozens of backends (including pandas, polars, DuckDB, and many SQL databases) 57 | - [DuckDB](https://duckdb.org/): in-process database engine for running SQL queries on local or remote data 58 | - [temporian](https://temporian.readthedocs.io/en/stable/): efficient data processing for timeseries data 59 | - [polars](https://pola.rs/): ultra-fast dataframe library written in Rust 60 | - and others... 61 | """ 62 | ) 63 | return 64 | 65 | 66 | @app.cell 67 | def _(mo): 68 | mo.md( 69 | rf""" 70 | # Polars advantages 71 | - Easy to use 72 | - Parallelized across all CPU cores 73 | - Zero dependencies 74 | - Built on the Apache Arrow in-memory data format: enables zero-copy interoperability with other libraries (e.g., DuckDB, Snowflake) 75 | - Handles datasets larger than RAM 76 | - Powerful query optimizer 77 | - Fully compatible with scikit-learn and a growing ecosystem of other libraries, thanks to the [Dataframe Interchange Protocol](https://data-apis.org/dataframe-protocol/latest/) and [narwhals](https://github.com/narwhals-dev/narwhals) 78 | 79 | - {mo.icon('fluent-mdl2:rust-language-logo')} written in [Rust](https://rust-lang.org), a compiled language that has experienced rapid adoption since its first stable release in 2015 thanks to its C/C++ performance, concurrency, and memory safety 80 | """ 81 | ) 82 | return 83 | 84 | 85 | @app.cell 86 | def _(mo): 87 | mo.md( 88 | r""" 89 | # Key concepts 90 | 91 | Polars uses the Apache Arrow in-memory data format, which is column-oriented. The primary data structures for polars are Series and DataFrames, similar to pandas. 92 | 93 | Apache Arrow supports many useful data types (many more than those which are supported by NumPy), so you can perform fast, vectorized operations on all kinds of data (nested JSON `structs`, strings, datetimes, etc.) 94 | """ 95 | ) 96 | return 97 | 98 | 99 | @app.cell 100 | def _(mo): 101 | mo.md( 102 | r""" 103 | ## Contexts 104 | In Polars, a _context_ refers to the data available to operate on. 105 | 106 | The primary contexts are: 107 | 108 | **Selection**: 109 | 110 | - `.select()`: choose a subset of columns and perform operations on them 111 | - `.with_columns()`: add to the columns already available 112 | 113 | **Filtering**: 114 | 115 | - `.filter()`: filter the data using boolean conditions on row values 116 | 117 | **Aggregation**: 118 | 119 | - `.group_by()`: perform aggregations on groups of values 120 | """ 121 | ) 122 | return 123 | 124 | 125 | @app.cell 126 | def _(mo): 127 | mo.md( 128 | r""" 129 | ## Expressions 130 | 131 | _Expressions_ are the operations performed in Polars, things like: 132 | 133 | - `.sum()` 134 | - `.len()` 135 | - `.mean().over()...` 136 | - `when().then().otherwise()` 137 | - `.str.replace()` 138 | """ 139 | ) 140 | return 141 | 142 | 143 | @app.cell 144 | def _(mo): 145 | mo.md( 146 | r""" 147 | ## Lazy vs. Eager mode 148 | - `scan_csv()` vs. `read_csv()` 149 | 150 | ### Recommendation: use Lazy mode 151 | - In Lazy mode, Polars will optimize the query plan 152 | """ 153 | ) 154 | return 155 | 156 | 157 | @app.cell 158 | def _(mo): 159 | mo.vstack( 160 | [ 161 | mo.md( 162 | r""" 163 | # Plugin ecosystem 164 | You can create custom expressions to use in Polars, which will also be vectorized and run in parallel like standard Polars expressions. If there's an operation you'd like to run on your data, chances are someone has already implemented it and it's just a `pip install` away. Here are [some examples](https://docs.pola.rs/user-guide/expressions/plugins/#community-plugins)... 165 | """ 166 | ), 167 | mo.accordion( 168 | { 169 | "### [`polars_ds`](https://github.com/abstractqqq/polars_ds_extension)": ( 170 | r""" 171 | Polars extension for data science tasks 172 | 173 | - A combination of functions and operations from scikit-learn, SciPy, and edit distance 174 | - Polars is the only dependency (unless you want to create plots; that adds Plotly as a dependency) 175 | - Can create bar plots within dataframe outputs (HTML `__repr__` in a notebook) -- like sparklines, and similar to what is available in pandas' advanced dataframe styling options 176 | """ 177 | ), 178 | "### [`polars_distance`](https://github.com/ion-elgreco/polars-distance)": ( 179 | r""" 180 | Distance calculations (e.g., word similarity) in polars. Also includes haversine distance (lat/lon), cosine similarity, etc. 181 | """ 182 | ), 183 | "### [`polars_reverse_geocode`](https://github.com/MarcoGorelli/polars-reverse-geocode)": ( 184 | r""" 185 | Offline reverse geocoding: find a city based on provided lat/lon; using an offline lookup table 186 | """ 187 | ), 188 | "### Tutorial: [how to create a polars plugin](https://marcogorelli.github.io/polars-plugins-tutorial/)": ( 189 | r""" 190 | You can create your own plugin! This tutorial teaches you enough Rust to write a polars plugin, which can published to PyPI and installed by other Polars users. 191 | """ 192 | ), 193 | } 194 | ), 195 | ] 196 | ) 197 | return 198 | 199 | 200 | @app.cell 201 | def _(mo): 202 | mo.md( 203 | r""" 204 | # Final thoughts 205 | 206 | ## Upgrade weekly 207 | ⭐ Polars development [advances rapidly](https://github.com/pola-rs/polars/releases), so I recommend upgrading often (weekly) to get the latest features 208 | 209 | ## Try it out 210 | The best way to learn is by doing. Try using Polars any time you create a new notebook or start a new project. 211 | """ 212 | ) 213 | return 214 | 215 | 216 | @app.cell 217 | def _(mo): 218 | mo.md( 219 | r""" 220 | # Resources 221 | - [Polars user guide](https://docs.pola.rs/user-guide/migration/pandas/): fantastic guide to learning Polars alongside helpful explanations 222 | - [Coming from `pandas`](https://docs.pola.rs/user-guide/migration/pandas/): are you familiar with `pandas` and want to learn the differences you'll notice when switching to polars? This guide translates common concepts to help you. 223 | - [This series of articles from 2022](https://kevinheavey.github.io/modern-polars/) demonstrates some operations in pandas and polars, side-by-side. _Polars development advances rapidly, so many of the concepts covered in that series are already different. Still it will help you get a general feel for the flow of using Polars compared to pandas._ 224 | - [Polars Python API](https://docs.pola.rs/api/python/stable/reference/index.html): detailed info on every expression, method, and function in Polars. I recommend browsing this list to get a feel for what Polars can do. 225 | """ 226 | ) 227 | return 228 | 229 | 230 | @app.cell(hide_code=True) 231 | def _(mo): 232 | mo.vstack( 233 | [ 234 | mo.md(r""" 235 | # Demo 236 | In this section, I demonstrate basic Polars usage on the NYC Taxi Yellow Cab dataset. You can find more information about that dataset on the [NYC Trip Record Data page](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page). 237 | """), 238 | mo.accordion( 239 | { 240 | "## Data dictionary (from the [PDF file published by NYC Trip Record Data](https://www.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf))": r""" 241 | 1. **VendorID**: A code indicating the TPEP provider that provided the record. 1= Creative Mobile Technologies, LLC; 2= VeriFone Inc. 242 | 2. **tpep_pickup_datetime**: The date and time when the meter was engaged 243 | 3. **tpep_dropoff_datetime**: The date and time when the meter was disengaged 244 | 4. **Passenger_count**: The number of passengers in the vehicle. This is a driver-entered value. 245 | 5. **Trip_distance**: The elapsed trip distance in miles reported by the taximeter 246 | 6. **PULocationID**: TLC Taxi Zone in which the taximeter was engaged 247 | - [See here](http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml) for a map of the TLC Taxi Zones 248 | 7. **DOLocationID**: TLC Taxi Zone in which the taximeter was disengaged 249 | 8. **RateCodeID**: The final rate code in effect at the end of the trip. 250 | - 1 = Standard rate 251 | - 2 = JFK 252 | - 3 = Newark 253 | - 4 = Nassau or Westchester 254 | - 5 = Negotiated fare 255 | - 6 = Group ride 256 | 9. **Store_and_fwd_flag**: This flag indicates whether the trip record was held in vehicle memory before sending to the vendor, aka “store and forward,” because the vehicle did not have a connection to the server 257 | - Y = store and forward trip 258 | - N = not a store and forward trip 259 | 10. **Payment_type**: A numeric code signifying how the passenger paid for the trip 260 | - 1 = Credit card 261 | - 2 = Cash 262 | - 3 = No charge 263 | - 4 = Dispute 264 | - 5 = Unknown 265 | - 6 = Voided trip 266 | 11. **Fare_amount**: The time-and-distance fare calculated by the meter 267 | 12. **Extra**: Miscellaneous extras and surcharges. Currently, this only includes the $0.50 and $1 rush hour and overnight charges. 268 | 13. **MTA_tax**: $0.50 MTA tax that is automatically triggered based on the metered rate in use 269 | 14. **Improvement_surcharge**: $0.30 improvement surcharge assessed trips at the flag drop. The improvement surcharge began being levied in 2015. 270 | 15. **Tip_amount**: Tip amount – This field is automatically populated for credit card tips. Cash tips are not included. 271 | 16. **Tolls_amount**: Total amount of all tolls paid in trip. 272 | 17. **Total_amount**: The total amount charged to passengers. Does not include cash tips 273 | 18. **Congestion_Surcharge**: Total amount collected in trip for NYS congestion surcharge. 274 | 19. **Airport_fee**: $1.25 for pick up only at LaGuardia and John F. Kennedy Airports 275 | """ 276 | } 277 | ), 278 | ] 279 | ) 280 | return 281 | 282 | 283 | @app.cell 284 | def _(mo): 285 | mo.md( 286 | r""" 287 | ## Lazy-load the data 288 | Polars can read Parquet files (local or hosted on a network), determine their schema (columns and data types), apply filter pushdowns, and download only the data that is needed for the operations being performed. 289 | 290 | ```python 291 | import polars as pl 292 | 293 | # Create a dataframe from a collection of parquet files 294 | df = pl.scan_parquet("data/yellow_tripdata_*.parquet) 295 | ``` 296 | """ 297 | ) 298 | return 299 | 300 | 301 | @app.cell 302 | def _(mo, pl): 303 | _md = mo.md( 304 | """ 305 | Let's check the schema: 306 | 307 | ```python 308 | # Polars will scan the data and return 309 | # the column names and datatypes 310 | df.collect_schema() 311 | 312 | # If the file is stored locally, you can 313 | # also read the schema without collecting fist 314 | pl.read_parquet_schema("path/to/a/local/file.parquet") 315 | ``` 316 | """ 317 | ) 318 | 319 | # Create a LazyFrame that will use the data from all the files specified above 320 | df = pl.scan_parquet("data/yellow_tripdata_*.parquet") 321 | _output = mo.plain(df.collect_schema()) 322 | mo.vstack([_md, _output]) 323 | return (df,) 324 | 325 | 326 | @app.cell 327 | def _(df, mo, pl): 328 | _md = mo.md( 329 | """ 330 | **Preview the first few rows:** 331 | 332 | ```python 333 | df.head(n=10).collect() 334 | ``` 335 | """ 336 | ) 337 | 338 | with pl.Config(tbl_cols=20, tbl_width_chars=1000, thousands_separator=True): 339 | _output = mo.plain_text(df.head(n=10).collect()) 340 | 341 | mo.vstack([_md, _output]) 342 | return 343 | 344 | 345 | @app.cell 346 | def _(df, mo, pl): 347 | _md = mo.md( 348 | """ 349 | **You can also preview the first few rows like this:** 350 | 351 | ```python 352 | df.collect().glimpse() 353 | ``` 354 | """ 355 | ) 356 | 357 | with mo.capture_stdout() as buffer: 358 | with pl.Config(thousands_separator=True): 359 | df.collect().glimpse() 360 | _output = mo.plain_text(buffer.getvalue()) 361 | print(buffer.getvalue()) 362 | mo.vstack([_md, _output, "Full list:", buffer.getvalue().strip().split("\n")]) 363 | return (buffer,) 364 | 365 | 366 | @app.cell 367 | def _(df, pl): 368 | with pl.Config(thousands_separator=True): 369 | df.collect().glimpse() 370 | return 371 | 372 | 373 | @app.cell 374 | def _(df, mo, pl): 375 | _month_list = ( 376 | df.select(month=pl.col("tpep_pickup_datetime").dt.strftime("%Y-%m")) 377 | .group_by("month") 378 | .agg(num_trips=pl.len()) 379 | .filter(pl.col("num_trips") > 100) # Remove erroneous timestamps 380 | .unique() 381 | .sort(by="month") 382 | .collect() 383 | .to_series() 384 | .to_list() 385 | ) 386 | print(_month_list) 387 | month_selection = mo.ui.multiselect(value=_month_list, options=_month_list) 388 | return (month_selection,) 389 | 390 | 391 | @app.cell 392 | def _(df, mo, month_selection, pl): 393 | _md = mo.md( 394 | f""" 395 | ## Explore the data 396 | 397 | **Find the average cost per trip, by month** 398 | 399 | Month selection: {month_selection} 400 | 401 | Note that the operations below are performed in parallel across all available CPU cores, and that only the data needed will be downloaded. 402 | 403 | In this case, since I have filtered to {len(month_selection.value)} months, only the files with those months of data will be accessed. Also notice that only 5 columns are accessed, since those are the ones I have requested. 404 | """ 405 | ) 406 | 407 | query_plan = ( 408 | df.with_columns(month=pl.col("tpep_pickup_datetime").dt.strftime("%Y-%m")) 409 | .filter(pl.col("month").is_in(month_selection.value)) 410 | .group_by(pl.col("month")) 411 | .agg( 412 | num_trips=pl.len(), # count the number of trips 413 | cost_per_trip=pl.col("total_amount").mean(), 414 | avg_passengers_per_trip=pl.col("passenger_count").mean(), 415 | avg_distance=pl.col("trip_distance").mean(), 416 | num_airport_trips=(pl.col("Airport_fee") > 0).sum(), 417 | ) 418 | ) 419 | _output = mo.plain_text( 420 | query_plan.explain(format="plain") # see also: format="tree" 421 | ) 422 | _output_with_streaming = mo.plain_text(query_plan.explain(streaming=True)) 423 | 424 | _accordion = mo.accordion( 425 | { 426 | "Here's a way we could answer this question in Polars": mo.md( 427 | rf""" 428 | Let's see this in Polars: 429 | 430 | ```python 431 | query_plan = ( 432 | df.with_columns(month=pl.col("tpep_pickup_datetime").dt.strftime("%Y-%m")) 433 | .filter(pl.col("month").is_in({month_selection.value})) 434 | .group_by(pl.col("month")) 435 | .agg( 436 | num_trips=pl.len(), # count the number of trips 437 | cost_per_trip=pl.col("total_amount").mean(), 438 | avg_passengers_per_trip=pl.col("passenger_count").mean(), 439 | avg_distance=pl.col("trip_distance").mean(), 440 | num_airport_trips=(pl.col("Airport_fee") > 0).sum(), 441 | ) 442 | ) 443 | ``` 444 | """ 445 | ), 446 | "Let's see how Polars will optimize this query": mo.md( 447 | r""" 448 | ```python 449 | # Show the optimized query plan: 450 | query_plan.explain() 451 | ``` 452 | """ 453 | ), 454 | "Query plan": _output, 455 | "You can also run this in streaming mode for memory-constrained environments": mo.md( 456 | r""" 457 | ```python 458 | query_plan.explain(streaming=True) 459 | ``` 460 | """ 461 | ), 462 | "Query plan with streaming": _output_with_streaming, 463 | } 464 | ) 465 | 466 | 467 | mo.vstack([_md, _accordion]) 468 | return (query_plan,) 469 | 470 | 471 | @app.cell 472 | def _(mo, month_selection, pl, query_plan): 473 | _md = mo.md( 474 | rf""" 475 | ### Perform the calculation ("collect") 476 | 477 | Month selection: {month_selection} 478 | 479 | ```python 480 | df_avg = query_plan.collect().sort(by=pl.col("month")) 481 | ``` 482 | 483 | Some options to `.collect()`: `engine="cpu"`, `streaming=False`, `background=False` 484 | """ 485 | ) 486 | 487 | df_avg = query_plan.collect().sort(by=pl.col("month")) 488 | 489 | with pl.Config(tbl_cols=20, tbl_width_chars=1000, thousands_separator=True): 490 | _output = mo.plain_text(df_avg) 491 | 492 | mo.vstack([_md, _output]) 493 | return (df_avg,) 494 | 495 | 496 | @app.cell 497 | def _(df, mo, month_selection, pl): 498 | _md = mo.md( 499 | f""" 500 | ## How does trip count vary by hour of the day? 501 | 502 | Months: {month_selection} 503 | """ 504 | ) 505 | 506 | _result = ( 507 | df.with_columns( 508 | month=pl.col("tpep_pickup_datetime").dt.strftime("%Y-%m"), 509 | hour=pl.col("tpep_pickup_datetime").dt.hour(), 510 | ) 511 | .filter(pl.col("month").is_in(month_selection.value)) 512 | .group_by(["month", "hour"]) 513 | .agg( 514 | num_trips=pl.len(), # count the number of trips 515 | ) 516 | .filter(pl.col("num_trips") > 100) # exclude erroneous timestamps 517 | .sort(by=["month", "hour"], descending=[False, False]) 518 | ) 519 | 520 | _plot = _result.collect().plot.line(x="hour", y="num_trips", color="month") 521 | 522 | _accordion = mo.accordion( 523 | { 524 | "Let's see this in Polars:": mo.md( 525 | rf""" 526 | ```python 527 | result = ( 528 | df.with_columns( 529 | month=pl.col("tpep_pickup_datetime").dt.strftime("%Y-%m"), 530 | hour=pl.col("tpep_pickup_datetime").dt.hour(), 531 | ) 532 | .filter(pl.col("month").is_in({month_selection.value})) 533 | .group_by(["month", "hour"]) 534 | .agg( 535 | num_trips=pl.len(), # count the number of trips 536 | ) 537 | .filter(pl.col("num_trips") > 100) # exclude erroneous timestamps 538 | .sort(by=["month", "hour"], descending=[False, False]) 539 | ) 540 | 541 | # Plot with Altair 542 | plot = ( 543 | result 544 | .collect() 545 | .plot.line( 546 | x="hour", 547 | y="num_trips", 548 | color="month" 549 | ) 550 | ) 551 | ``` 552 | """ 553 | ), 554 | } 555 | ) 556 | 557 | mo.vstack([_md, _accordion, _plot]) 558 | return 559 | 560 | 561 | @app.cell 562 | def _(): 563 | # Explore the weather codes: 564 | import json 565 | 566 | with open("data/weather_codes.json", mode="rt", encoding="utf8") as json_file: 567 | weather_codes_dict = json.load(json_file) 568 | 569 | with open("data/weather.json", mode="rt", encoding="utf8") as json_file: 570 | weather_data_dict = json.load(json_file) 571 | return json, json_file, weather_codes_dict, weather_data_dict 572 | 573 | 574 | @app.cell 575 | def _(df, pl): 576 | # import polars.selectors as cs # for "column selectors" 577 | 578 | # Load the weather codes data and convert to a LazyFrame 579 | weather_codes = ( 580 | pl.read_json("data/weather_codes.json") 581 | .unpivot() 582 | .select(pl.col("variable").alias("weather_code"), pl.col("value")) 583 | .unnest("value") 584 | # Expand the descriptions for night only, 585 | # which uses "clear" rather than "sunny" 586 | .unnest("night") 587 | .select( 588 | [ 589 | # Must be the same data type (Int64) for joining 590 | pl.col("weather_code").cast(pl.Int64), 591 | pl.col("description").alias("weather_description"), 592 | ] 593 | ) 594 | ).lazy() # must be a LazyFrame to join with another LazyFrame 595 | 596 | # Get the names of the weather variables, so their lists can be "exploded" 597 | # down the rows of the LazyFrame 598 | data_fields = ( 599 | pl.scan_ndjson("data/weather.json") 600 | .select("hourly_units") 601 | .collect() # you have to .collect() to access a Series 602 | .to_series() # the .struct accessor is available only for Series 603 | .struct.fields # .fields holds the keys (column names, after unnesting) 604 | ) 605 | 606 | # LazyFrame with weather data and weather descriptions 607 | df_weather = ( 608 | pl.scan_ndjson("data/weather.json") 609 | .unnest("hourly") # expand a dict into columns 610 | .explode( 611 | columns=data_fields, # expand a list into rows 612 | # Alternatively, using column selectors: 613 | # columns=cs.by_name(data_fields) 614 | ) 615 | .select(data_fields) 616 | .join(weather_codes, on="weather_code") 617 | .with_columns( 618 | # Replace the "time" column and update its 619 | # datatype so it can be joined to the trip data 620 | pl.col("time").cast(pl.Datetime(time_unit="ns")), 621 | ) 622 | .sort(by="time") 623 | ) 624 | 625 | # Combine with the Taxi data 626 | # for a join_asof, both DataFrames need to 627 | # be sorted by the join_asof key 628 | df_combined = df.sort("tpep_pickup_datetime").join_asof( 629 | df_weather, 630 | left_on="tpep_pickup_datetime", 631 | right_on="time", 632 | # Use the weather data closest to the time of pickup 633 | strategy="nearest", # alternatives: "backward", "forward" 634 | tolerance="2h", # weather data must be within 2 hours of trip time 635 | ) 636 | return data_fields, df_combined, df_weather, weather_codes 637 | 638 | 639 | @app.cell 640 | def _(df_combined, df_weather, mo, weather_codes_dict, weather_data_dict): 641 | _md = mo.md( 642 | r""" 643 | ## How does weather impact trips? 644 | 645 | For this analysis, we'll take advantage of Polar's ability to load and join multiple data types and join them together using approximate timestamp matching -- all using "lazy," optimized computations. 646 | 647 | The time zone for both taxi trip data and weather is **America/New_York**. 648 | 649 | > [Weather data from Open-Meteo.com](https://open-meteo.com/), weather codes from [stellasphere](https://gist.github.com/stellasphere/9490c195ed2b53c707087c8c2db4ec0c). 650 | """ 651 | ) 652 | 653 | 654 | _accordion = mo.accordion( 655 | { 656 | "Here's the plain JSON weather data": weather_data_dict, 657 | "And here are the weather codes": weather_codes_dict, 658 | "Let's load the weather data in Polars": ( 659 | """ 660 | ```python 661 | # This is also a LazyFrame (you can tell from `scan_`) 662 | # LazyFrame with weather data and weather descriptions 663 | df_weather = ( 664 | pl.scan_ndjson("data/weather.json") 665 | .unnest("hourly") # expand a dict into columns 666 | .explode( 667 | columns=data_fields, # expand a list into rows 668 | ) 669 | .select(data_fields) 670 | .join(weather_codes, on="weather_code") 671 | .with_columns( 672 | # Replace the "time" column and update its 673 | # datatype so it can be joined to the trip data 674 | pl.col("time").cast(pl.Datetime(time_unit="ns")), 675 | ) 676 | .sort(by="time") 677 | ) 678 | ``` 679 | """ 680 | ), 681 | "Here's what the data looks like": df_weather.head(100).collect(), 682 | "Combine with the Taxi trips data": ( 683 | """ 684 | ```python 685 | # for a join_asof, both DataFrames need to 686 | # be sorted by the join_asof key 687 | df_combined = df.sort("tpep_pickup_datetime").join_asof( 688 | df_weather, 689 | left_on="tpep_pickup_datetime", 690 | right_on="time", 691 | # Use the weather data closest to the time of pickup 692 | strategy="nearest", # alternatives: "backward", "forward" 693 | tolerance="2h", # weather data must be within 2 hours of trip time 694 | ) 695 | ``` 696 | """ 697 | ), 698 | "Preview the combined dataset": df_combined.head(100).collect(), 699 | "Here's the Polars code for the weather codes and data fields": ( 700 | """ 701 | ```python 702 | # Load the weather codes data and convert to a LazyFrame 703 | weather_codes = ( 704 | pl.read_json("data/weather_codes.json") 705 | .unpivot() 706 | .select(pl.col("variable").alias("weather_code"), pl.col("value")) 707 | .unnest("value") 708 | # Expand the descriptions for night only, 709 | # which uses "clear" rather than "sunny" 710 | .unnest("night") 711 | .select( 712 | [ 713 | # Must be the same data type (Int64) for joining 714 | pl.col("weather_code").cast(pl.Int64), 715 | pl.col("description").alias("weather_description"), 716 | ] 717 | ) 718 | ).lazy() # must be a LazyFrame to join with another LazyFrame 719 | 720 | # Get the names of the weather variables, so their lists can be "exploded" 721 | # down the rows of the LazyFrame 722 | data_fields = ( 723 | pl.scan_ndjson("data/weather.json") 724 | .select("hourly_units") 725 | .collect() # you have to .collect() to access a Series 726 | .to_series() # the .struct accessor is available only for Series 727 | .struct.fields # .fields holds the keys (column names, after unnesting) 728 | ) 729 | ``` 730 | """ 731 | ), 732 | } 733 | ) 734 | 735 | mo.vstack([_md, _accordion]) 736 | return 737 | 738 | 739 | @app.cell 740 | def _(mo, weather_codes): 741 | weather_selection = mo.ui.multiselect.from_series( 742 | weather_codes.select("weather_description").collect().to_series(), 743 | label="", 744 | value=["Clear", "Rain"], 745 | ) 746 | return (weather_selection,) 747 | 748 | 749 | @app.cell 750 | def _(df_combined, mo, pl, weather_selection): 751 | _md = mo.md( 752 | f""" 753 | ### How much does weather impact average cost? 754 | 755 | Weather to compare: {weather_selection} 756 | """ 757 | ) 758 | 759 | _result = ( 760 | df_combined.with_columns( 761 | month=pl.col("tpep_pickup_datetime").dt.strftime("%Y-%m") 762 | ) 763 | .group_by(["month", "weather_description"]) 764 | .agg( 765 | num_trips=pl.len(), 766 | cost_per_person=pl.col("total_amount").sum() 767 | / pl.col("passenger_count").sum(), 768 | ) 769 | .filter(pl.col("num_trips") > 100) 770 | # Filter based on our selection 771 | # Joins are highly optimized and can be faster than .is_in() 772 | .join( 773 | pl.LazyFrame({"weather_description": weather_selection.value}), 774 | on="weather_description", 775 | ) 776 | .sort(by=["month", "weather_description"]) 777 | ) 778 | 779 | _plot_bar = _result.collect().plot.bar( 780 | x="month", 781 | y="cost_per_person", 782 | color="weather_description", 783 | xOffset="weather_description", 784 | ) 785 | 786 | _plot_line = _result.collect().plot.line( 787 | x="month", 788 | y="num_trips", 789 | color="weather_description", 790 | ) 791 | # Show the plots side by side (use + to layer on top of one another) 792 | _plot = _plot_bar | _plot_line 793 | 794 | _accordion = mo.accordion( 795 | { 796 | "Polars operations": ( 797 | """ 798 | ```python 799 | result = ( 800 | df_combined.with_columns( 801 | month=pl.col("tpep_pickup_datetime").dt.strftime("%Y-%m") 802 | ) 803 | .group_by(["month", "weather_description"]) 804 | .agg( 805 | num_trips=pl.len(), 806 | cost_per_person=pl.col("total_amount").sum() 807 | / pl.col("passenger_count").sum(), 808 | ) 809 | .filter(pl.col("num_trips") > 100) 810 | # Filter based on our selection 811 | # Joins are highly optimized and can be faster than .is_in() 812 | .join( 813 | pl.LazyFrame({"weather_description": weather_selection.value}), 814 | on="weather_description", 815 | ) 816 | .sort(by=["month", "weather_description"]) 817 | ) 818 | ``` 819 | """ 820 | ), 821 | "Let's check the query plan (**`result.explain()`**)": mo.plain_text( 822 | _result.explain() 823 | ), 824 | "Now, we'll visualize this with an interactive plot": ( 825 | """ 826 | ```python 827 | plot_bar = result.collect().plot.bar( 828 | x="month", 829 | y="cost_per_person", 830 | color="weather_description", 831 | xOffset="weather_description", 832 | ) 833 | 834 | plot_line = result.collect().plot.line( 835 | x="month", 836 | y="num_trips", 837 | color="weather_description", 838 | ) 839 | # Show the plots side by side (use + to layer on top of one another) 840 | plot = plot_bar | plot_line 841 | ``` 842 | """ 843 | ), 844 | } 845 | ) 846 | 847 | mo.vstack([_md, _accordion, _plot]) 848 | return 849 | 850 | 851 | @app.cell 852 | def _(mo): 853 | mo.md( 854 | """ 855 | # Keep exploring! 856 | Polars has many additional powerful features. Try exploring with some of them through this dataset and what you find on Polars' [User Guide](https://docs.pola.rs/) and [API reference](https://docs.pola.rs/api/python/stable/reference/index.html). 857 | """ 858 | ) 859 | return 860 | 861 | 862 | @app.cell 863 | def _(): 864 | import marimo as mo 865 | return (mo,) 866 | 867 | 868 | if __name__ == "__main__": 869 | app.run() 870 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "polars-intro" 3 | version = "0.1.0" 4 | description = "A quick demo introducing the polars dataframe library through a marimo notebook." 5 | readme = "README.md" 6 | authors = [ 7 | { name = "Ryan Parker", email = "ryanparker.ml@gmail.com" } 8 | ] 9 | requires-python = ">=3.11" 10 | dependencies = [ 11 | "httpx>=0.27.2", 12 | "marimo>=0.9.4", 13 | # next-generation reactive notebook and app 14 | "polars[plot]>=1.9.0", 15 | # lightning-fast dataframe library, along with altair for plotting 16 | ] 17 | 18 | [project.scripts] 19 | polars-intro = "polars_intro:main" 20 | 21 | [build-system] 22 | requires = ["hatchling"] 23 | build-backend = "hatchling.build" 24 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | altair==5.4.1 2 | anyio==4.6.0 3 | attrs==24.2.0 4 | certifi==2024.8.30 5 | click==8.1.7 6 | docutils==0.21.2 7 | h11==0.14.0 8 | httpcore==1.0.6 9 | httpx==0.27.2 10 | idna==3.10 11 | itsdangerous==2.2.0 12 | jedi==0.19.1 13 | jinja2==3.1.4 14 | jsonschema==4.23.0 15 | jsonschema-specifications==2024.10.1 16 | marimo==0.9.4 17 | markdown==3.7 18 | markupsafe==3.0.1 19 | narwhals==1.9.2 20 | packaging==24.1 21 | parso==0.8.4 22 | polars==1.9.0 23 | -e file:///home/ryan/code/polars-intro 24 | psutil==6.0.0 25 | pygments==2.18.0 26 | 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