jack/dash-api
1
0
Fork 0
Code Issues 1 Pull Requests Packages Projects Releases Wiki Activity

done

Browse Source
This commit is contained in:
leene
2025-09-07 22:09:54 +02:00
parent e1b817252c
commit 2fc0d000b6
7796 changed files with 2159515 additions and 933 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
lib/python3.11/site-packages/narwhals-2.3.0.dist-info/INSTALLER Normal file
View File

@ -0,0 +1 @@
uv

293
lib/python3.11/site-packages/narwhals-2.3.0.dist-info/METADATA Normal file
View File

@ -0,0 +1,293 @@
Metadata-Version: 2.4
Name: narwhals
Version: 2.3.0
Summary: Extremely lightweight compatibility layer between dataframe libraries
Project-URL: Homepage, https://github.com/narwhals-dev/narwhals
Project-URL: Documentation, https://narwhals-dev.github.io/narwhals/
Project-URL: Repository, https://github.com/narwhals-dev/narwhals
Project-URL: Bug Tracker, https://github.com/narwhals-dev/narwhals/issues
Author-email: Marco Gorelli <hello_narwhals@proton.me>
License-File: LICENSE.md
Keywords: cudf,dask,dataframes,interoperability,modin,pandas,polars,pyarrow
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Typing :: Typed
Requires-Python: >=3.9
Provides-Extra: cudf
Requires-Dist: cudf>=24.10.0; extra == 'cudf'
Provides-Extra: dask
Requires-Dist: dask[dataframe]>=2024.8; extra == 'dask'
Provides-Extra: duckdb
Requires-Dist: duckdb>=1.0; extra == 'duckdb'
Provides-Extra: ibis
Requires-Dist: ibis-framework>=6.0.0; extra == 'ibis'
Requires-Dist: packaging; extra == 'ibis'
Requires-Dist: pyarrow-hotfix; extra == 'ibis'
Requires-Dist: rich; extra == 'ibis'
Provides-Extra: modin
Requires-Dist: modin; extra == 'modin'
Provides-Extra: pandas
Requires-Dist: pandas>=1.1.3; extra == 'pandas'
Provides-Extra: polars
Requires-Dist: polars>=0.20.4; extra == 'polars'
Provides-Extra: pyarrow
Requires-Dist: pyarrow>=13.0.0; extra == 'pyarrow'
Provides-Extra: pyspark
Requires-Dist: pyspark>=3.5.0; extra == 'pyspark'
Provides-Extra: pyspark-connect
Requires-Dist: pyspark[connect]>=3.5.0; extra == 'pyspark-connect'
Provides-Extra: sqlframe
Requires-Dist: sqlframe!=3.39.3,>=3.22.0; extra == 'sqlframe'
Description-Content-Type: text/markdown
# Narwhals
<h1 align="center">
<img
width="400"
alt="narwhals_small"
src="https://github.com/user-attachments/assets/968545af-ea0f-48bb-8377-144e93f7abf8">
</h1>
[![PyPI version](https://badge.fury.io/py/narwhals.svg)](https://badge.fury.io/py/narwhals)
[![Downloads](https://static.pepy.tech/badge/narwhals/month)](https://pepy.tech/project/narwhals)
[![Trusted publishing](https://img.shields.io/badge/Trusted_publishing-Provides_attestations-bright_green)](https://peps.python.org/pep-0740/)
[![PYPI - Types](https://img.shields.io/pypi/types/narwhals)](https://pypi.org/project/narwhals)
Extremely lightweight and extensible compatibility layer between dataframe libraries!
- **Full API support**: cuDF, Modin, pandas, Polars, PyArrow.
- **Lazy-only support**: Dask, DuckDB, Ibis, PySpark, SQLFrame. Work in progress: Daft.
Seamlessly support all, without depending on any!
- ✅ **Just use** [a subset of **the Polars API**](https://narwhals-dev.github.io/narwhals/api-reference/), no need to learn anything new
- ✅ **Zero dependencies**, Narwhals only uses what
the user passes in so your library can stay lightweight
- ✅ Separate **lazy** and eager APIs, use **expressions**
- ✅ Support pandas' complicated type system and index, without
either getting in the way
- ✅ **100% branch coverage**, tested against pandas and Polars nightly builds
- ✅ **Negligible overhead**, see [overhead](https://narwhals-dev.github.io/narwhals/overhead/)
- ✅ Let your IDE help you thanks to **full static typing**, see [typing](https://narwhals-dev.github.io/narwhals/api-reference/typing/)
- ✅ **Perfect backwards compatibility policy**,
see [stable api](https://narwhals-dev.github.io/narwhals/backcompat/) for how to opt-in
Get started!
- [Read the documentation](https://narwhals-dev.github.io/narwhals/)
- [Chat with us on Discord!](https://discord.gg/V3PqtB4VA4)
- [Join our community call](https://calendar.google.com/calendar/embed?src=27ff6dc5f598c1d94c1f6e627a1aaae680e2fac88f848bda1f2c7946ae74d5ab%40group.calendar.google.com)
- [Read the contributing guide](https://github.com/narwhals-dev/narwhals/blob/main/CONTRIBUTING.md)
<details>
<summary>Table of contents</summary>
- [Narwhals](#narwhals)
- [Installation](#installation)
- [Usage](#usage)
- [Example](#example)
- [Scope](#scope)
- [Roadmap](#roadmap)
- [Used by](#used-by)
- [Sponsors and institutional partners](#sponsors-and-institutional-partners)
- [Appears on](#appears-on)
- [Why "Narwhals"?](#why-narwhals)
</details>
## Installation
- pip (recommended, as it's the most up-to-date)
```
pip install narwhals
```
- conda-forge (also fine, but the latest version may take longer to appear)
```
conda install -c conda-forge narwhals
```
## Usage
There are three steps to writing dataframe-agnostic code using Narwhals:
1. use `narwhals.from_native` to wrap a pandas/Polars/Modin/cuDF/PyArrow
DataFrame/LazyFrame in a Narwhals class
2. use the [subset of the Polars API supported by Narwhals](https://narwhals-dev.github.io/narwhals/api-reference/)
3. use `narwhals.to_native` to return an object to the user in its original
dataframe flavour. For example:
- if you started with pandas, you'll get pandas back
- if you started with Polars, you'll get Polars back
- if you started with Modin, you'll get Modin back (and compute will be distributed)
- if you started with cuDF, you'll get cuDF back (and compute will happen on GPU)
- if you started with PyArrow, you'll get PyArrow back
<h1 align="left">
<img
width="600"
alt="narwhals_gif"
src="https://github.com/user-attachments/assets/88292d3c-6359-4155-973d-d0f8e3fbf5ac">
</h1>
## Example
Narwhals allows you to define dataframe-agnostic functions. For example:
```python
import narwhals as nw
from narwhals.typing import IntoFrameT
def agnostic_function(
df_native: IntoFrameT,
date_column: str,
price_column: str,
) -> IntoFrameT:
return (
nw.from_native(df_native)
.group_by(nw.col(date_column).dt.truncate("1mo"))
.agg(nw.col(price_column).mean())
.sort(date_column)
.to_native()
)
```
You can then pass `pandas.DataFrame`, `polars.DataFrame`, `polars.LazyFrame`, `duckdb.DuckDBPyRelation`,
`pyspark.sql.DataFrame`, `pyarrow.Table`, and more, to `agnostic_function`. In each case, no additional
dependencies will be required, and computation will stay native to the input library:
```python
import pandas as pd
import polars as pl
from datetime import datetime
data = {
"date": [datetime(2020, 1, 1), datetime(2020, 1, 8), datetime(2020, 2, 3)],
"price": [1, 4, 3],
}
print("pandas result:")
print(agnostic_function(pd.DataFrame(data), "date", "price"))
print()
print("Polars result:")
print(agnostic_function(pl.DataFrame(data), "date", "price"))
```
```terminal
pandas result:
date price
0 2020-01-01 2.5
1 2020-02-01 3.0
Polars result:
shape: (2, 2)
┌─────────────────────┬───────┐
│ date ┆ price │
│ --- ┆ --- │
│ datetime[μs] ┆ f64 │
╞═════════════════════╪═══════╡
│ 2020-01-01 00:00:00 ┆ 2.5 │
│ 2020-02-01 00:00:00 ┆ 3.0 │
└─────────────────────┴───────┘
```
See the [tutorial](https://narwhals-dev.github.io/narwhals/basics/dataframe/) for several examples!
## Scope
- Do you maintain a dataframe-consuming library?
- Do you have a specific Polars function in mind that you would like Narwhals to have in order to make your work easier?
If you said yes to both, we'd love to hear from you!
## Roadmap
See [roadmap discussion on GitHub](https://github.com/narwhals-dev/narwhals/discussions/1370)
for an up-to-date plan of future work.
## Used by
Join the party!
- [altair](https://github.com/vega/altair/)
- [bokeh](https://github.com/bokeh/bokeh)
- [darts](https://github.com/unit8co/darts)
- [formulaic](https://github.com/matthewwardrop/formulaic)
- [gt-extras](https://github.com/posit-dev/gt-extras)
- [hierarchicalforecast](https://github.com/Nixtla/hierarchicalforecast)
- [marimo](https://github.com/marimo-team/marimo)
- [metalearners](https://github.com/Quantco/metalearners)
- [mosaic](https://github.com/uwdata/mosaic)
- [panel-graphic-walker](https://github.com/panel-extensions/panel-graphic-walker)
- [plotly](https://plotly.com)
- [pointblank](https://github.com/posit-dev/pointblank)
- [pymarginaleffects](https://github.com/vincentarelbundock/pymarginaleffects)
- [pyreadstat](https://github.com/Roche/pyreadstat)
- [py-shiny](https://github.com/posit-dev/py-shiny)
- [rio](https://github.com/rio-labs/rio)
- [scikit-lego](https://github.com/koaning/scikit-lego)
- [scikit-playtime](https://github.com/koaning/scikit-playtime)
- [tabmat](https://github.com/Quantco/tabmat)
- [tea-tasting](https://github.com/e10v/tea-tasting)
- [timebasedcv](https://github.com/FBruzzesi/timebasedcv)
- [tubular](https://github.com/lvgig/tubular)
- [Validoopsie](https://github.com/akmalsoliev/Validoopsie)
- [vegafusion](https://github.com/vega/vegafusion)
- [wimsey](https://github.com/benrutter/wimsey)
Feel free to add your project to the list if it's missing, and/or
[chat with us on Discord](https://discord.gg/V3PqtB4VA4) if you'd like any support.
## Sponsors and institutional partners
Narwhals is 100% independent, community-driven, and community-owned.
We are extremely grateful to the following organisations for having
provided some funding / development time:
- [Quansight Labs](https://labs.quansight.org)
- [Quansight Futures](https://www.qi.ventures)
- [OpenTeams](https://www.openteams.com)
- [POSSEE initiative](https://possee.org)
- [BYU-Idaho](https://www.byui.edu)
If you contribute to Narwhals on your organization's time, please let us know. We'd be happy to add your employer
to this list!
## Appears on
Narwhals has been featured in several talks, podcasts, and blog posts:
- [Talk Python to me Podcast](https://youtu.be/FSH7BZ0tuE0)
Ahoy, Narwhals are bridging the data science APIs
- [Python Bytes Podcast](https://www.youtube.com/live/N7w_ESVW40I?si=y-wN1uCsAuJOKlOT&t=382)
Episode 402, topic #2
- [Super Data Science: ML & AI Podcast](https://www.youtube.com/watch?v=TeG4U8R0U8U)
Narwhals: For Pandas-to-Polars DataFrame Compatibility
- [Sample Space Podcast | probabl](https://youtu.be/8hYdq4sWbbQ?si=WG0QP1CZ6gkFf18b)
How Narwhals has many end users ... that never use it directly. - Marco Gorelli
- [The Real Python Podcast](https://www.youtube.com/watch?v=w5DFZbFYzCM)
Narwhals: Expanding DataFrame Compatibility Between Libraries
- [Pycon Lithuania](https://www.youtube.com/watch?v=-mdx7Cn6_6E)
Marco Gorelli - DataFrame interoperatiblity - what's been achieved, and what comes next?
- [Pycon Italy](https://www.youtube.com/watch?v=3IqUli9XsmQ)
How you can write a dataframe-agnostic library - Marco Gorelli
- [Polars Blog Post](https://pola.rs/posts/lightweight_plotting/)
Polars has a new lightweight plotting backend
- [Quansight Labs blog post (w/ Scikit-Lego)](https://labs.quansight.org/blog/scikit-lego-narwhals)
How Narwhals and scikit-lego came together to achieve dataframe-agnosticism
## Why "Narwhals"?
[Coz they are so awesome](https://youtu.be/ykwqXuMPsoc?si=A-i8LdR38teYsos4).
Thanks to [Olha Urdeichuk](https://www.fiverr.com/olhaurdeichuk) for the illustration!

162
lib/python3.11/site-packages/narwhals-2.3.0.dist-info/RECORD Normal file
View File

@ -0,0 +1,162 @@
narwhals-2.3.0.dist-info/INSTALLER,sha256=5hhM4Q4mYTT9z6QB6PGpUAW81PGNFrYrdXMj4oM_6ak,2
narwhals-2.3.0.dist-info/METADATA,sha256=9x2Yl_7U7W2hID3ThyfgYRvHI4l_unO8akp-3PiRrjI,11307
narwhals-2.3.0.dist-info/RECORD,,
narwhals-2.3.0.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
narwhals-2.3.0.dist-info/WHEEL,sha256=qtCwoSJWgHk21S1Kb4ihdzI2rlJ1ZKaIurTj_ngOhyQ,87
narwhals-2.3.0.dist-info/licenses/LICENSE.md,sha256=heMD6hta6RzeBucppx59AUCgr_ukRY0ABj0bcrN3mKs,1071
narwhals/__init__.py,sha256=FGVSkIo-2xnlr-PAnUz9_lUsoAVLmwSko7vvKt1dhwM,3206
narwhals/_arrow/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
narwhals/_arrow/dataframe.py,sha256=v2x502jBYsuRCsLZvVrvVDnAa_V75JA4joTj0MW60SI,29247
narwhals/_arrow/expr.py,sha256=j00YfKCWouDbNBVpcxQWUNu5s-ONGxeQz1EhLx-FkzA,6390
narwhals/_arrow/group_by.py,sha256=SkDRYpKaZXkwxtC-5s1yinBSgVgj2KoAiFFpjSvo9Fo,6458
narwhals/_arrow/namespace.py,sha256=9YWEgy-LN1CgfHHcR1oXTaL2rdsgXIhLWLNABoooLZg,11966
narwhals/_arrow/selectors.py,sha256=qIfCnMNlQ5svQzGaB-DV5YE4xSaUaVzElTPYJl_0BJc,1128
narwhals/_arrow/series.py,sha256=ykSo51a6YCViyd0cXY3u10t21Rb77hx3fDE4mMCe214,45006
narwhals/_arrow/series_cat.py,sha256=vvNlPaHHcA-ORzh_79-oY03wt6aIg1rLI0At8FXr2Ok,598
narwhals/_arrow/series_dt.py,sha256=tTJg3Kxde356LNmqfHOHseWkNofyigFu7s17EtEYTQs,8922
narwhals/_arrow/series_list.py,sha256=hhIE7wZGVQs-J9iX-RyP4sedZ413fStDDj2aW006ALI,647
narwhals/_arrow/series_str.py,sha256=RKtxW9FuZ2R-Qyj4iqP9qPF5u_QAXufzEYRJHImrRjI,4491
narwhals/_arrow/series_struct.py,sha256=85pQSUqOdeMyjsnjaSr_4YBC2HRGD-dsnNy2tPveJRM,410
narwhals/_arrow/typing.py,sha256=TmgG8eqF4uCRW5NFzWTiBvlUGvD46govtIC8gRyrkmA,2286
narwhals/_arrow/utils.py,sha256=KHNaGBGuA1wvrsCrPWR7xv9XaO0iXHgJPuaYrG-PfRI,16121
narwhals/_compliant/__init__.py,sha256=NIrlDmo6XGrEuJkHvxtPTntxJmVzn1pku31P06cI48c,2460
narwhals/_compliant/any_namespace.py,sha256=kXBY2yAN8Oz5E7k-vlq_aQHwHVEmJtYZbL_BgG089xo,3698
narwhals/_compliant/column.py,sha256=mQdztLvvfOi0Su8j2Fh03MOXcphjWL3K2wB9hSHEa-o,7449
narwhals/_compliant/dataframe.py,sha256=bTuQKeKCiNYdYEsrc16I9vFQ-_EK2q2125srzldu-p8,15233
narwhals/_compliant/expr.py,sha256=Zf4_h9D-MYGBcYt6oAIHE_uqzbXm9rIBZsD5_bcRXs0,40237
narwhals/_compliant/group_by.py,sha256=78xvZ3Zryh6EU25UHk5vrZbi9a6xCThBTXfBLgY0uHk,6885
narwhals/_compliant/namespace.py,sha256=wy1mEA221qlDoYZdRFC6luUAuaJHMJje1gqeEEJRGtw,8304
narwhals/_compliant/selectors.py,sha256=0TEz_n8rHv-0wdqs-WtdwvKUACPzMeH40hLhhV2xthw,11824
narwhals/_compliant/series.py,sha256=cJ-B-26UuT0oMx--29HCjp6_QSvGnTWjUwRGyMLJgsU,13629
narwhals/_compliant/typing.py,sha256=JZJJDBmffYf3il-ZXLyItM_sMhGsCTFZYow2ES0-DgM,7343
narwhals/_compliant/when_then.py,sha256=hY2O8dNYUaa-9OTUzYYfrzmQp0w13cEf0GtV1hKAiWs,4323
narwhals/_compliant/window.py,sha256=_ji4goVKkT4YPTyZa_I0N2yGmwBfB1_LDG0WSXGbmlo,505
narwhals/_constants.py,sha256=kE1KWsIky4ryabH-Z117ZtGW24ccTcreWOZJjpacO6I,1094
narwhals/_dask/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
narwhals/_dask/dataframe.py,sha256=zTLRoHh9zf4awiU3gZGWSb9-z15Dn_K7O0Ud3tWa8Oc,18146
narwhals/_dask/expr.py,sha256=arIOunREdPk_ceXcdl-Vl5M4WJGSBLZiMxLNVxhCWAA,26234
narwhals/_dask/expr_dt.py,sha256=7vaSQPZIWsyQsZVP0vN9_60hP6bOI0WP5UDF7jksl_Y,6886
narwhals/_dask/expr_str.py,sha256=vJpPB0EhYI7Edso2ILz8_wM0FV_nOdpWu2Bu76d-2pw,4375
narwhals/_dask/group_by.py,sha256=w-NNu2gclRKKiRDVxnDiIE6-Wm5nM7c5NMRFXxdLGoA,4906
narwhals/_dask/namespace.py,sha256=I8Kx-iJ9VI1Jrg00M-eZrDN4DsKLtCbYmClR5WWwBXk,13193
narwhals/_dask/selectors.py,sha256=FafFcfFWM6uTcKUsEeqfbmBUIgYVzH5XdN6sFUVLMKU,1148
narwhals/_dask/utils.py,sha256=qdsSkVId_G6i778nfWEl5xqb1Kaq4MjkhGmUGG0eBnY,5484
narwhals/_duckdb/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
narwhals/_duckdb/dataframe.py,sha256=erot6PCYdvmFYYAoGXJpFEBsK64UZVQPV80sPUY-4Kc,20334
narwhals/_duckdb/expr.py,sha256=wbjKERmHs-DitbIPBvYOhOAv25bBanTeIIvTQTtGR9E,10611
narwhals/_duckdb/expr_dt.py,sha256=QoNbABk0aIuRNyIa790HvisfB177_Ds1H_xMiZWNnHM,4990
narwhals/_duckdb/expr_list.py,sha256=gXPHQZ3oqliATMLye7JugEar-SKOTliCYkfjKv6KZBM,1326
narwhals/_duckdb/expr_str.py,sha256=M7UTLjnHI66I7XYGECORpsJwrrYaYUxyesK2NqGGuok,999
narwhals/_duckdb/expr_struct.py,sha256=eN06QA1JS6wjAt7_AZzW3xoztHM_hoadlFUl_hwsEiE,576
narwhals/_duckdb/group_by.py,sha256=nuueeiJYRcs31Ja973VvtLbWM2wnms0GYL7kAHDeju0,1123
narwhals/_duckdb/namespace.py,sha256=XyTCDc-sdSCdtasrqbkJhFviY375Y2fW_CvCNb0LtPo,5476
narwhals/_duckdb/selectors.py,sha256=yA16Z-MlJUJBjOu0XI9qVO4Zx7L_T5FN2DQqNAYhu-o,1033
narwhals/_duckdb/series.py,sha256=xBpuPUnSSIQ1vYEKjHQFZN7ix1ZyMwSchliDPpkf3Wk,1397
narwhals/_duckdb/typing.py,sha256=gO_Odyinkn4QZY_TU4uuzda6mbeo38glOOUUripcWgg,454
narwhals/_duckdb/utils.py,sha256=VRFYNhgrjsNwyY2Dv-iLH3S_5GJklr2ixxB2TsZHw1o,13718
narwhals/_duration.py,sha256=WGzj3FVcC2KogqRhNeim3YDIwUn8HkXQHAljtvHrjwQ,3139
narwhals/_enum.py,sha256=sUR-04yIwjAMsX5eelKnc1UKXc5dBoj1do0krubAE04,1192
narwhals/_exceptions.py,sha256=OhT0MiQbcw_wE85Bl1YYZJjvtlX0rJMNUoZtKNCjTq8,1928
narwhals/_expression_parsing.py,sha256=-cekls62DXha7elyCLu52X0erZeK1mNgQYOjNaoog0A,22904
narwhals/_ibis/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
narwhals/_ibis/dataframe.py,sha256=eQMS4ojRLH9ELPIJ94fIJROiJaODHGfi6MNbGGEz4u8,16634
narwhals/_ibis/expr.py,sha256=Nktb9wysqxf30oqlWVteIeogCaXKRbkJzKiI3Y0m_E8,13031
narwhals/_ibis/expr_dt.py,sha256=2sDgjR5HalXE3IBUc7LvIe4QPjFCnafJPs9ZikbW5xw,3314
narwhals/_ibis/expr_list.py,sha256=TSfb_4EKRdTFIbZ2VJ9zqXJl62ZDkivweK5BiUWFsBc,948
narwhals/_ibis/expr_str.py,sha256=K54Ch9veia8t-v9Gl_SZgL-eRynad1vufZWPAW-oKg8,2944
narwhals/_ibis/expr_struct.py,sha256=FDsa5MqcHhqPmpZIEfGBASdqxPkyImrlGTH7XUSw3cs,565
narwhals/_ibis/group_by.py,sha256=enNzAPUsA_LIwPNJ7jG_MJKyqG2HyCiesBEX3pJgJBg,1031
narwhals/_ibis/namespace.py,sha256=0hRod5QixeON0gr4XCqEJrHh3Wa3JK9_4Dz7MTJlFbI,5521
narwhals/_ibis/selectors.py,sha256=SkFxoukpKc_OjwKoKHRm8VwMaphCMUeWBJ2g_oWz3D0,961
narwhals/_ibis/series.py,sha256=CZDwDPsdELKtdr7OWmcFyGqexr33Ucfnv_RU95VJxIQ,1218
narwhals/_ibis/utils.py,sha256=C77hUPh-gl07T31XUHLy1YtSX7nlKvHfpjtQHn007w0,9288
narwhals/_interchange/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
narwhals/_interchange/dataframe.py,sha256=FC_2WuxxI4RPUBMqcau68rkdfsxhClou4zCErx1flcw,6034
narwhals/_interchange/series.py,sha256=nSxdlOZrw3wtavS42TMR_b_EGgPBv224ioZBMo5eoC8,1651
narwhals/_namespace.py,sha256=Iw-7TssbXM1vAmpYsB_S9y0mn0K-gwMzBX-LgH0foxM,13708
narwhals/_pandas_like/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
narwhals/_pandas_like/dataframe.py,sha256=GcGkaIh8WOE-AyQB0yRKQ9YCtlWAwEBtsnTq90Caoi8,42958
narwhals/_pandas_like/expr.py,sha256=9JhVxAzfhhrY2LpBhGWoWRrUTfEwlbO0btmfR3SgU2s,14787
narwhals/_pandas_like/group_by.py,sha256=T_o11xywuFCyKUA10wQfTyrh82wPHF3Aka29QL_d7vk,13544
narwhals/_pandas_like/namespace.py,sha256=a9PjhoTOBj2-iWz7RXLrC-smArTzC_HRpGQ10Z9_fSU,16916
narwhals/_pandas_like/selectors.py,sha256=Qf7r0H6R8cniwDwC2zlWxddsPx-AHFsZwDPQ9iCEiH8,1261
narwhals/_pandas_like/series.py,sha256=DRqqp20sREqwdsCsk2cKDJPYGc7Y9DjDkDyRVPi_lTw,43404
narwhals/_pandas_like/series_cat.py,sha256=MJwCnJ49hfnODh6JgMHOCQ2KBlTbmySU6_X4XWaqiz4,527
narwhals/_pandas_like/series_dt.py,sha256=EnNPp6StDmwDX9p3mlQw3m4pZWOp0UA1Zh5bJr-gOZE,11587
narwhals/_pandas_like/series_list.py,sha256=xc9m4c5ftCQPfiTy7EujhfNHn7KHbjBUNa-iXHdV9t8,1391
narwhals/_pandas_like/series_str.py,sha256=Wl871eCAw1X10bfreJw9Iw_pIkIpORRQMJq09ueRDlU,3982
narwhals/_pandas_like/series_struct.py,sha256=vX9HoO42vHackvVozUfp8odM9uJ4owct49ydKDnohdk,518
narwhals/_pandas_like/typing.py,sha256=Awm2YnewvdA3l_4SEwb_5AithhwBYNx1t1ajaHnvUsM,1064
narwhals/_pandas_like/utils.py,sha256=8OdQIrxuspIfop3AM3qxBzKcsK6OTm_ykE9dh9KW_kg,25496
narwhals/_polars/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
narwhals/_polars/dataframe.py,sha256=NCmeBBXKDsFh_mLZF0z6r3fRO3l1080JdreHL_3Asog,23303
narwhals/_polars/expr.py,sha256=4MLaKi2yzKoB6Pd5t6-TRK19MsesHWB6mPNr0KcCgWE,16185
narwhals/_polars/group_by.py,sha256=PsxQPWX7QWy2iXUWJEelC5O6mR1TKr9MB_i6O5RVxms,2373
narwhals/_polars/namespace.py,sha256=lgfcUXhdfb-uV18tYZK7Jgj74yzDEGa-CD_MpJZlX_E,10671
narwhals/_polars/series.py,sha256=J8_3pLA6YXBm-S9ZifnlQv_cqal21qC10MVFc6C1r64,26070
narwhals/_polars/typing.py,sha256=tiAgtciFZmlqqH3Q6MdQLXZEb1ajH-YbePpaKjeuqQE,786
narwhals/_polars/utils.py,sha256=hIjAce1DOez6ZEJPBbY4x_Pl4wGubdnbye5H6tE0_DA,12386
narwhals/_spark_like/__init__.py,sha256=T7_UCePrrYs1ZeMatvCUYUvQcXvrDjQ4b08_ugWIHAo,87
narwhals/_spark_like/dataframe.py,sha256=r645rnjsUjkJCYy2wXm0VmYeCe2h_UHAOjQGDV5CkRA,23269
narwhals/_spark_like/expr.py,sha256=u6ruKJuoYkdAuvon5KL6YlSYtP_4ewOtRxKmnLTih6A,14052
narwhals/_spark_like/expr_dt.py,sha256=1ICwI2xpTuK5PyGjiLEdfLgsgudDPEVdMGgVnM_c5Uk,7515
narwhals/_spark_like/expr_list.py,sha256=aU29eGRQ89F7z5XticN9-l7ShVJpD2Ni4rdxE9Wfd7w,1132
narwhals/_spark_like/expr_str.py,sha256=eGN7uW9BfP7-KVey-RrqFcQjZrO7WnTlU3PZPpPSzKk,1298
narwhals/_spark_like/expr_struct.py,sha256=haBDpuRhn_nGAFjMF3arhhRr6NfefNei9vEmAOa0fQI,613
narwhals/_spark_like/group_by.py,sha256=rsAhSHEoA1pHzPk--9xtKvLJbTHOtJ45ftVKUhI7KUc,1246
narwhals/_spark_like/namespace.py,sha256=rFliViMHZ03H22K9eYCPQ9B-hbiGpHnJII_0gWbOnc8,8027
narwhals/_spark_like/selectors.py,sha256=SzJPoFjyIEviSSvPRvL81o1jjQJcM-Veqb52vFU66JQ,1086
narwhals/_spark_like/utils.py,sha256=ViTlbGgfG5koBKHtO5sCACFSFD94iTJtNGTUETOEBLw,11367
narwhals/_sql/__init__.py,sha256=T7_UCePrrYs1ZeMatvCUYUvQcXvrDjQ4b08_ugWIHAo,87
narwhals/_sql/dataframe.py,sha256=RgmNIcJ2r4JIurB64vzqo0-qPUJH9kBFo4hT1gvqB0w,1491
narwhals/_sql/expr.py,sha256=O0ZXOLWmytBilCYc_WU7yeANEa8ZngLE0aPIxbcPyXw,29763
narwhals/_sql/expr_dt.py,sha256=CWFn7Ki3YW3XT_Hy88pdCTZs8j6jP4GpP38vgPd-vX4,1612
narwhals/_sql/expr_str.py,sha256=lZziCwu6flcjwtyUXa-2P6mGitV56tKZsnYgLD6wh9I,5234
narwhals/_sql/group_by.py,sha256=-yR5J2-Kk0awFRoFnLici00rfTK_5CxFKJSxc-Bt8Zs,1669
narwhals/_sql/namespace.py,sha256=2KRj7AnNfWEpWbjkl2X5pCya9jrq8jLwKMKFp32GFI0,2755
narwhals/_sql/typing.py,sha256=e3LkLPI4oa2IzykR7BgO9IIfCKRw0vrX4uHxPTB-uJM,487
narwhals/_sql/when_then.py,sha256=4lXcc_J_N6vHGby6kPJl1PGqLPUGbgHYuIXiYROyoW4,3636
narwhals/_translate.py,sha256=e8RjNCNX4QGJWKjM6VANDTG_bVT2VusjNfjsnkCBO3g,6112
narwhals/_typing.py,sha256=goAuPl-_FoIpI8NOShf54fQCOATPPJdYeqcHdacQNO0,6430
narwhals/_typing_compat.py,sha256=h-BtLEl7CrZ-hYLlwYTOcCdsUS3dvgvkxQTcDQ7RYmA,2516
narwhals/_utils.py,sha256=HJ3BnwVhl8wrmnxB9II6S1RS_uzPRPyyMgUFeXGhmiU,67278
narwhals/dataframe.py,sha256=cQ66Ld14nvzLeSd9tHRJKqJxtucG1AvgpF8D7hUraso,134314
narwhals/dependencies.py,sha256=7fnp1kST9nA1Hl10_SsRC2iXdz3eOaR1w1Xf33JYtPc,19846
narwhals/dtypes.py,sha256=3N-VO1ZaFHCTf_JDaFjFJYQVJwYK57UD2m_cmRzCjMk,29613
narwhals/exceptions.py,sha256=9ocrbLNP7fZLqP2gV1PS9OexNhzf8h7of2wR5wi5kE0,3704
narwhals/expr.py,sha256=ZIp3oUhM4MPr8v7v_8Bv5RHqSoMs7p97CWRDxDc-xF4,98752
narwhals/expr_cat.py,sha256=o4MhGmPoO3_DlkRB06Z3ilyqyj70mwcW3kftRayDq2s,1210
narwhals/expr_dt.py,sha256=R3F9z9FAluZBZ7ETt2lOdkMrq_OmG2YMYBpkIkGzUQc,32465
narwhals/expr_list.py,sha256=8-_L7wzxm90kcRooFW2QEdzn0FgJNMnUispBReBE5fs,6744
narwhals/expr_name.py,sha256=0QD8Yg7FKu739ENSJI2lxIGSjho89J9l9DjxeBOz9bM,4866
narwhals/expr_str.py,sha256=u3Juck5nPG9HlfnroHl1oKW_YU76rthsMUWlKWCPeYs,20055
narwhals/expr_struct.py,sha256=V_Hj3kChdcZPIlfb_Kslp5W-j-XGFcfdMFzYpZdjNWE,1742
narwhals/functions.py,sha256=nxOJ3abbCNlr4wbAQ40-MlLczEW8sVyYggq9JlrMnmA,64415
narwhals/group_by.py,sha256=7UkbFvCZ6n0rtgDovbElueEA8p4oS3i3ny_g5TGabek,7164
narwhals/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
narwhals/schema.py,sha256=4yiRz6Qv373ATpN6kCFNSblUb4vMDX9USpKeJTDRrYI,12369
narwhals/selectors.py,sha256=ybbFG7Sjebr8qoMgD43O6QuHBGl52yUpGRe08L1LKyo,10759
narwhals/series.py,sha256=IIMiPTJMlBNv3kc6QDpxqBr0Oaetxb5WbZZOktaJldw,95125
narwhals/series_cat.py,sha256=KU5DMtCqi0KKVrmTfCLpgI32AGuY3MYZclNF6soh1Xc,834
narwhals/series_dt.py,sha256=a7JIMtA_Wn9ZiBa9O_-t7k6Lk-Az2AhHbQYlS1VwxAA,23051
narwhals/series_list.py,sha256=PMXSEL_Pai2ZoMcNi0KZ6WdXHlMvTVyFK600TTGhCeg,3802
narwhals/series_str.py,sha256=pdB2MI5UO7o81CeXPEiJ6v9XOhP0WPeAQACM1W_4e3Y,15400
narwhals/series_struct.py,sha256=bixxdASlxYyP1prDjMmfltVU5212a596VQZE11yszUg,930
narwhals/stable/__init__.py,sha256=b9soCkGkQzgF5jO5EdQ6IOQpnc6G6eqWmY6WwpoSjhk,85
narwhals/stable/v1/__init__.py,sha256=yBCnM1ZhWuZwMndoFb_HZeGxVy1qRDa8DFPjy0bFnIQ,42613
narwhals/stable/v1/_dtypes.py,sha256=7zGmarnurUTgY6DI4KQ1MSAC7B9ZZiI5Em7plb-HAEs,2700
narwhals/stable/v1/_namespace.py,sha256=gfsbT4R4aLmmdArY35LRpEHPiUeZKEEnXGiY9ypFtwE,296
narwhals/stable/v1/dependencies.py,sha256=aM0IShF4hbaaMEDRJQXvsu4RABZOdBG4QhrpJPxb7fg,5001
narwhals/stable/v1/dtypes.py,sha256=u2NFDJyCkjsK6p3K9ULJS7CoG16z0Z1MQiACTVkhkH4,1082
narwhals/stable/v1/selectors.py,sha256=xEA9bBzkpTwUanGGoFwBCcHIAXb8alwrPX1mjzE9mDM,312
narwhals/stable/v1/typing.py,sha256=7dBQVxmW6zQOjfT2N0rEddKUBD61roxhZJDJhApC0fk,6433
narwhals/stable/v2/__init__.py,sha256=FGAuHHO0XVCNoMT8E_hoCB3OaUI_Amm80CpYLG817NQ,40366
narwhals/stable/v2/_namespace.py,sha256=oYB5nrFGxqqTonkRx9vUanyBxGs2Yb0j7_juMyvnvWA,296
narwhals/stable/v2/dependencies.py,sha256=vpYWx_dron6wFdbQ60G06EV2UJ_LMd52LDodCrAY5Jg,86
narwhals/stable/v2/dtypes.py,sha256=iMpk2Kc1mNiQYmboOSgmiAijklSUBHSHF2LTKMKnGe8,80
narwhals/stable/v2/selectors.py,sha256=sjJL3agHd8Rgf_lWhgCmEKruhWEkwHdX32-n85OqVJU,83
narwhals/stable/v2/typing.py,sha256=mymfP7wg6FfyunGiK51I-jqEIcPA5u8gFOHN4sDoz5I,6042
narwhals/this.py,sha256=BbKcj0ReWqE01lznzKjuqq7otXONvjBevWWC5aJhQxs,1584
narwhals/translate.py,sha256=Mscw6Nh5yUgkKqWyEEst205vbfdskOUa9DlouVe2ThA,24741
narwhals/typing.py,sha256=Bh2JbyEZk0tmrzKhfnxGLFjs1W3sDTiomrXzP6rW8rQ,17164
narwhals/utils.py,sha256=2GT3XxucWI6l9r9jTwMw7Aha2G73FsSXgXNFZ3O_ZyA,223

0
lib/python3.11/site-packages/narwhals-2.3.0.dist-info/REQUESTED Normal file
View File

4
lib/python3.11/site-packages/narwhals-2.3.0.dist-info/WHEEL Normal file
View File

@ -0,0 +1,4 @@
Wheel-Version: 1.0
Generator: hatchling 1.27.0
Root-Is-Purelib: true
Tag: py3-none-any

21
lib/python3.11/site-packages/narwhals-2.3.0.dist-info/licenses/LICENSE.md Normal file
View File

@ -0,0 +1,21 @@
MIT License
Copyright (c) 2024, Marco Gorelli
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.