Draft implementation of the DataFrame Standard for pandas and polars
Project description
DataFrame Standard POC
Work-in-progress POC of what the DataFrame Standard might look like for pandas and polars.
What's this?
Please read our blog post! https://data-apis.org/blog/dataframe_standard_rfc/.
How to try this out
Here's an example of how you can try this out:
import pandas as pd
from dataframe_api_compat import pandas_standard
from dataframe_api_compat import polars_standard
def convert_to_standard_compliant_dataframe(df):
if isinstance(df, pd.DataFrame):
return pandas_standard.convert_to_standard_compliant_dataframe(df)
elif isinstance(df, pl.DataFrame):
return polars_standard.convert_to_standard_compliant_dataframe(df)
else:
raise TypeError(f"Got unexpected type: {type(df)}")
df = pd.DataFrame({'a': [1,2,3]})
df_std = convert_to_standard_compliant_dataframe(df)
The object df_std is a Standard-compliant DataFrame.
Installation
pip install git+https://github.com/MarcoGorelli/dataframe-api-compat
Testing
pytest --cov=dataframe_api_compat --cov=test_standard --cov-fail-under=100
100% branch coverage isn't the objective - it's the bare minimum.
Linting
pre-commit run --all-files
Type Checking
First, clone the dataframe_standard to some local path. Then, run:
MYPYPATH=<path to dataframe-api/spec/API_specification> mypy dataframe_api_compat
MYPYPATH=<path to dataframe-api/spec/API_specification> mypy polars_standard.py
For example, if you cloned both repos in the same place, this could be:
MYPYPATH=../dataframe-api/spec/API_specification/ mypy dataframe_api_compat
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