Python module to report, clean, and optimize Pandas Dataframes effectively.
Full Documentation Here.
The first step of any data analysis project is to check and clean the data, in this module I implemented a very effiecint code that can:
- Automatically drop columns that have a unique value (these columns are useless, so it will be dropped).
- Report your Pandas DataFrame to decide for actions, this report will show:
- Duplicated rows report.
- Columns’ Datatype to optimize memory report.
- Columns to convert to categorical report.
- Outliers report.
- Missing values report.
- Clean the dataframe by dropping columns that have a high ratio of missing values, rows with missing values, and duplicated rows in the dataframe.
- Optimize the dataframe by converting columns to the desired data type and converting categorical columns to 'category' data type.
To install clean_df, run this command in your terminal:
$ pip install clean_df
For more information on installation details for this project, please see the docs/installation.rst file.
This module is very easy to use, for a full detailed example please see the docs/usage.rst file.
from clean_df import CleanDataFrame
Pass your pandas dataframe to CleanDataFrame class:
cdf = CleanDataFrame(
df=df, # the dataframe to be cleaned
max_num_cat=5 # maximum number of unique values in a column to be
) # converted to categorical datatype, default is 5
Call report method to see a full report about the dataframe (duplications, columns to optimize its data types, categorical columns, outliers, and missing values:
cdf.report(
show_matrix=True, # show matrix missing values (from missingno package), default is True
show_heat=True, # show heat missing values (from missingno package), default is True
matrix_kws={}, # if need to pass any arguments to matrix plot, default is {}
heat_kws={} # if need to pass any arguments to heat plot, default is {}
)
Call clean method to drop high number of missing value columns, duplicated rows, and rows with missing values:
cdf.clean(
min_missing_ratio=0.05, # the minimum ratio of missing values to drop a column, default is 0.05
drop_nan=True # if True, drop the rows with missing values after dropping columns
# with missingsa above min_missing_ratio
drop_kws={}, # if need to pass any arguments to pd.DataFrame.drop(), default is {}
drop_duplicates_kws={} # same drop_kws, but for drop_duplicates function
)
Call optimize method to optimize the dataframe by changing columns' data types based on its values for maximum memory savings:
cdf.optimize()
cdf.df
See the CONTRIBUTING.rst for contribution details. Feel free to contact me for any subject through my:
Also, you are welcomed to visit my personal blog .
Free software: MIT license.
- The full documentation is hosted on my website, and on ReadTheDocs.
- The source code is available in GitHub.
- This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
- Here are additional resources I got a lot from them.