pythonpandasdataframemodin

Is it possibe to change similar libraries (Data Analysis) in Python within the same code?

I use the modin library for multiprocessing. While the library is great for faster processing, it fails at merge and I would like to revert to default pandas in between the code.

I understand as per PEP 8: E402 conventions, import should be declared once and at the top of the code however my case would need otherwise.

import pandas as pd
import modin.pandas as mpd    
import os
import ray

ray.init()
os.environ["MODIN_ENGINE"] = "ray"

df = mpd.read_csv()
do stuff

Then I would like to revert to default pandas within the same code but how would i do the below in pandas as there does not seem to be a clear way to switch from pd and mpd in the below lines and unfortunately modin seems to take precedence over pandas.

df = df.loc[:, df.columns.intersection(['col1', 'col2'])]
df = df.drop_duplicates()
df = df.sort_values(['col1', 'col2'], ascending=[True, True])

Is it possible? if yes, how?

Solution

  • Since many have posted answers however in this particular case, as applicable and pointed out by @Nin17 and this comment from Modin GitHub, to convert from Modin to Pandas for single core processing of some of the operations like df.merge you can use

    import pandas as pd
import modin.pandas as mpd    
import os
import ray
ray.init()
os.environ["MODIN_ENGINE"] = "ray"
df_modin = mpd.read_csv() #reading dataframe into Modin for parallel processing
df_pandas = df_modin._to_pandas() #converting Modin Dataframe into pandas for single core processing

    and if you would like to reconvert the dataframe to a modin dataframe for parallel processing

    df_modin = mpd.DataFrame(df_pandas)