There is a constantly growing number of users and packages using pandas to address their specific needs in data preparation, analysis and visualization. pandas is being used ubiquitously and is a good choise to handle small-sized data. However, pandas scales poorly and is non-interactive on moderate to large datasets. Modin provides a drop-in replacement API for pandas and scales computation across nodes and CPUs available. What you need to do to switch to Modin is just replace a single line of code.

# import pandas as pd
import modin.pandas as pd

While most packages can consume a pandas DataFrame and operate it efficiently, this is not the case with a Modin DataFrame due to its distributed nature. Thus, some packages may lack support for handling Modin DataFrame(s) correctly and, moreover, efficiently. Modin implements such methods as __array__, __dataframe__, etc. to facilitate other libraries to consume a Modin DataFrame. If you feel that a certain library can operate efficiently with a specific format of data, it is possible to convert a Modin DataFrame to the format preferred.


You can refer to pandas ecosystem page to get more details on where pandas can be used and what libraries it powers.

from import to_pandas

pandas_df = to_pandas(modin_df)


You can refer to NumPy ecosystem section of NumPy documentation to get more details on where NumPy can be used and what libraries it powers.

from import to_numpy

numpy_arr = to_numpy(modin_df)