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dptools: data preprocessing functions for Python

PyPI Latest Release Python 3.7 Project Status: Active – The project has reached a stable, usable state and is being actively developed. Licence Build Status Downloads


The dptools Python package provides helper functions to simplify common data processing tasks in a data science pipeline, including feature engineering, data aggregation, working with missing values and more.

The package currently encompasses the following functions:

  • Feature engineering:
    • add_date_features(): create date and time-based features
    • add_text_features(): create text-based features (including counts and TF-IDF)
    • aggregate_data(): aggregate data and create features based on aggregated statistics
    • encode_factors(): perform label or dummy encoding of categorical features
  • Data processing:
    • split_nested_features(): split features nested in a single column
    • fill_missings(): replace missings with specific values
    • correct_colnames(): correct column names to be unique and remove foreign symbols
    • print_missings(): print information on features with missing values
    • print_factor_levels(): print levels of categorical features
  • Data cleaning:
    • find_correlated_features(): identify features with a high pairwise correlation
    • find_constant_features(): identify features with a single unique value
  • Import and versioning:
    • read_csv_with_json(): read CSV where some columns are in JSON format
    • save_csv_version(): save CSV with an automatically assigned version to prevent overwriting


The latest stable release of dptools can be installed from PyPI:

pip install dptools

You may also install the development version from Github:

pip install git+

After the installation, you can import the included functions:

from dptools import *


This section contains a few examples of using functions from dptools for different data preprocessing tasks. Please refer to the docstring documentation in the implemented functions for further examples.

Creating a toy data set

First, let us create a toy data frame to demonstrate the package functionality.

# import dependencies
import pandas as pd
import numpy as np

# create data frame
data = {'age': [27, np.nan, 30, 25, np.nan],
        'height': [170, 168, 173, 177, 165],
        'gender': ['female', 'male', np.nan, 'male', 'female'],
        'income': ['high', 'medium', 'low', 'low', 'no income']}
df = pd.DataFrame(data)
age height gender income
27.0 170 female high
NaN 168 male medium
30.0 173 NaN low
25.0 177 male low
NaN 165 female no income

Aggregating features

# aggregating the data
from dptools import aggregate_data
df_new = aggregate_data(df, group_var = 'gender', num_stats = ['mean', 'max'], fac_stats = 'mode')   
gender age_mean age_max height_mean height_max income_mode
female 27.0 27.0 167.5 170 'high'
male 25.0 25.0 172.5 177 'low'

Creating text-based features

# creating text-based features
from dptools import add_text_features
df_new = add_text_features(df, text_vars = 'income')
age height gender income_word_count income_char_count income_tfidf_0 ... income_tfidf_3
27.0 170 female 1 4 1.0 ... 0.0
NaN 168 male 1 6 0.0 ... 1.0
30.0 173 NaN 1 3 0.0 ... 0.0
25.0 177 male 1 3 0.0 ... 0.0
NaN 165 female 2 9 0.0 ... 0.0

Working with missings

# print statistics on missing values
from dptools import print_missings
Total Percent
age 2 0.4
gender 1 0.2

Finding correlated features

# displays one correlated feature from each pair
from dptools import find_correlated_features
feats = find_correlated_features(df, cutoff = 0.4, method = 'spearman')

Found 1 correlated features.


Data versioning

# first call saves df as 'data_v1.csv'
from dptools import save_csv_version
save_csv_version('data.csv', df, index = False)

# second call saves df as 'data_v2.csv' as data_v1.csv already exists
save_csv_version('data.csv', df, index = False)


Installation requires Python 3.7+ and the following packages:


In case you need help on the included data preprocessing functions or you want to report an issue, please do so at the corresponding GitHub page.


Python package with utilities for data processing, aggregation, feature engineering and data versioning








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