A Python tool that automatically cleans data sets and readies them for analysis.
datacleaner works with data in pandas DataFrames.
datacleaner is not magic, and it won't take an unorganized blob of text and automagically parse it out for you.
What datacleaner will do is save you a ton of time encoding and cleaning your data once it's already in a format that pandas DataFrames can handle.
Currently, datacleaner does the following:
-
Optionally drops any row with a missing value
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Replaces missing values with the mode (for categorical variables) or median (for continuous variables) on a column-by-column basis
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Encodes non-numerical variables (e.g., categorical variables with strings) with numerical equivalents
We plan to add more cleaning features as the project grows.
Please see the repository license for the licensing and usage information for datacleaner.
Generally, we have licensed datacleaner to make it as widely usable as possible.
datacleaner is built to use pandas DataFrames and some scikit-learn modules for data preprocessing. As such, we recommend installing the Anaconda Python distribution prior to installing datacleaner.
Once the prerequisites are installed, datacleaner can be installed with a simple pip command:
pip install datacleaner
datacleaner can be used on the command line. Use --help to see its usage instructions.
usage: datacleaner [-h] [-cv CROSS_VAL_FILENAME] [-o OUTPUT_FILENAME]
[-cvo CV_OUTPUT_FILENAME] [-is INPUT_SEPARATOR]
[-os OUTPUT_SEPARATOR] [--drop-nans]
[--ignore-update-check] [--version]
INPUT_FILENAME
A Python tool that automatically cleans data sets and readies them for analysis
positional arguments:
INPUT_FILENAME File name of the data file to clean
optional arguments:
-h, --help show this help message and exit
-cv CROSS_VAL_FILENAME
File name for the validation data set if performing
cross-validation
-o OUTPUT_FILENAME Data file to output the cleaned data set to
-cvo CV_OUTPUT_FILENAME
Data file to output the cleaned cross-validation data
set to
-is INPUT_SEPARATOR Column separator for the input file(s) (default: \t)
-os OUTPUT_SEPARATOR Column separator for the output file(s) (default: \t)
--drop-nans Drop all rows that have a NaN in any column (default: False)
--ignore-update-check
Do not check for the latest version of datacleaner
(default: False)
--version show program's version number and exit
An example command-line call to datacleaner may look like:
datacleaner my_data.csv -o my_clean.data.csv -is , -os ,
which will read the data from my_data.csv (assuming columns are separated by commas), clean the data set, then output the resulting data set to my_clean.data.csv.
datacleaner can also be used as part of a script. There are two primary functions implemented in datacleaner: autoclean and autoclean_cv.
autoclean(input_dataframe, drop_nans=False, copy=False, ignore_update_check=False)
Performs a series of automated data cleaning transformations on the provided data set
Parameters
----------
input_dataframe: pandas.DataFrame
Data set to clean
drop_nans: bool
Drop all rows that have a NaN in any column (default: False)
copy: bool
Make a copy of the data set (default: False)
encoder: category_encoders transformer
The a valid category_encoders transformer which is passed an inferred cols list. Default (None: LabelEncoder)
encoder_kwargs: category_encoders
The a valid sklearn transformer to encode categorical features. Default (None)
ignore_update_check: bool
Do not check for the latest version of datacleaner
Returns
----------
output_dataframe: pandas.DataFrame
Cleaned data set
autoclean_cv(training_dataframe, testing_dataframe, drop_nans=False, copy=False, ignore_update_check=False)
Performs a series of automated data cleaning transformations on the provided training and testing data sets
Unlike `autoclean()`, this function takes cross-validation into account by learning the data transformations
from only the training set, then applying those transformations to both the training and testing set.
By doing so, this function will prevent information leak from the training set into the testing set.
Parameters
----------
training_dataframe: pandas.DataFrame
Training data set
testing_dataframe: pandas.DataFrame
Testing data set
drop_nans: bool
Drop all rows that have a NaN in any column (default: False)
copy: bool
Make a copy of the data set (default: False)
encoder: category_encoders transformer
The a valid category_encoders transformer which is passed an inferred cols list. Default (None: LabelEncoder)
encoder_kwargs: category_encoders
The a valid sklearn transformer to encode categorical features. Default (None)
ignore_update_check: bool
Do not check for the latest version of datacleaner
Returns
----------
output_training_dataframe: pandas.DataFrame
Cleaned training data set
output_testing_dataframe: pandas.DataFrame
Cleaned testing data set
Below is an example of datacleaner performing basic cleaning on a data set.
from datacleaner import autoclean
import pandas as pd
my_data = pd.read_csv('my_data.csv', sep=',')
my_clean_data = autoclean(my_data)
my_data.to_csv('my_clean_data.csv', sep=',', index=False)Note that because datacleaner works directly on pandas DataFrames, all DataFrame operations are still available to the resulting data sets.
We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to datacleaner, please file a new issue so we can discuss it.
If you use datacleaner as part of your workflow in a scientific publication, please consider citing the datacleaner repository with the following DOI:
