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Other DataFrame libraries

ydata-profiling is built on pandas and numpy. Pandas supports a wide range of data formats including CSV, XLSX, SQL, JSON, HDF5, SAS, BigQuery and Stata. Read more on supported formats by Pandas.

If you have data in another framework of the Python Data ecosystem, you can use ydata-profiling by converting to a pandas DataFrame, as direct integrations are not yet supported. Large datasets might require sampling (as seen in our documentation on how to profile large datasets).

Dask to Pandas
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# Convert dask DataFrame to a pandas DataFrame
df = df.compute()
Vaex to Pandas
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# Convert vaex DataFrame to a pandas DataFrame
df = df.to_pandas_df()

Modin interface

This is not part of the API as pandas.DataFrame, naturally, does not posses such a method. You can use the private method DataFrame._to_pandas() to do this conversion. If you would like to do this through the official API you can always save the Modin DataFrame to storage (csv, hdf, sql, ect) and then read it back using Pandas. This will probably be the safer way when working big DataFrames, to avoid out of memory issues." Source: https://github.com/modin-project/modin/issues/896

Modin to Pandas
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# Convert modin DataFrame to pandas DataFrame
df = df._to_pandas()