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scikit-na is a comprehensive Python package for missing data (NA) analysis and exploration. It provides statistical functions, interactive visualizations, and export capabilities to help data scientists understand and handle missing values in their datasets.

Why scikit-na?

• Comprehensive Analysis: Get detailed statistics on missing data patterns
• Interactive Reports: Generate widget-based reports for Jupyter notebooks
• Multiple Export Formats: Share results as CSV, JSON, HTML, or Excel files
• Statistical Modeling: Build logistic regression models to understand missingness
• Rich Visualizations: Create heatmaps, correlation plots, and distribution charts
• Hypothesis Testing: Test for missing completely at random (MCAR) patterns

Features

• Interactive report (based on ipywidgets)
• Export functionality (CSV, JSON, HTML, XLSX formats)
• Descriptive statistics
• Regression modeling
• Hypotheses tests
• Data visualization

Donate

If you find this package useful, please consider donating any amount of money. This will help me spend more time on supporting open-source software.

Installation

Basic installation

pip install scikit-na

With optional dependencies

# For export functionality (Excel support)
pip install scikit-na[export]

# For development
pip install scikit-na[dev]

# Install from source
pip install git+https://github.com/maximtrp/scikit-na.git

Quick Start

import scikit_na as na
import pandas as pd

# Load your data
data = pd.read_csv('your_dataset.csv')

# Get missing data summary
summary = na.summary(data)
print(summary)

# Create interactive report
report = na.report(data)

# Export results
na.export_summary(data, 'missing_data_analysis.csv', format='csv')

Examples

The following examples use the Titanic dataset (from Kaggle) that contains NA values in three columns: Age, Cabin, and Embarked.

Core Functions

Function	Description
na.summary()	Comprehensive missing data statistics
na.correlate()	Correlations between missing values
na.describe()	Descriptive stats grouped by missingness
na.model()	Logistic regression for missing patterns
na.test_hypothesis()	Statistical tests for MCAR
na.report()	Interactive widget-based report
na.export_summary()	Export analysis to files
na.export_report()	Export interactive reports

Summary

Per each column

By default, summary() function returns the results for each column.

import scikit_na as na
import pandas as pd

data = pd.read_csv('titanic_dataset.csv')

# Excluding three columns without NA to fit the table here
na.summary(data, columns=data.columns.difference(['SibSp', 'Parch', 'Ticket']))

	Age	Cabin	Embarked	Fare	Name	PassengerId	Pclass	Sex	Survived
na_count	177	687	2	0	0	0	0	0	0
na_pct_per_col	19.87	77.1	0.22	0	0	0	0	0	0
na_pct_total	20.44	79.33	0.23	0	0	0	0	0	0
na_unique_per_col	19	529	2	0	0	0	0	0	0
na_unique_pct_per_col	10.73	77	100	0	0	0	0	0	0
rows_after_dropna	714	204	889	891	891	891	891	891	891
rows_after_dropna_pct	80.13	22.9	99.78	100	100	100	100	100	100

NA unique is the number of NA values per each column that are unique for it, i.e. do not intersect with NA values in the other columns (or that will remain in dataset if we drop NA values in the other columns).

Whole dataset

We can also get a summary of missing data for the whole dataset:

na.summary(data, per_column=False)

	dataset
total_columns	12
total_rows	891
na_rows	708
non_na_rows	183
total_cells	10692
na_cells	866
na_cells_pct	8.1
non_na_cells	9826
non_na_cells_pct	91.9

Correlations

To calculate correlations between columns in terms of missing data, just call correlate() function with your DataFrame as the first argument:

na.correlate(data, method="spearman").round(3)

	Embarked	Age	Cabin
Embarked	1	-0.024	-0.087
Age	-0.024	1	0.144
Cabin	-0.087	0.144	1

This method can be used to uncover hidden patterns in missing data across many columns in a dataset. Columns with no missing data are automatically excluded.

There is a function to visualize correlations with a heatmap:

na.altair\
    .plot_corr(data, corr_kws={'method': 'spearman'})
    .properties(width=150, height=150)

Visualization

Heatmap

Now, let's visualize NA values on a heatmap. We will be using Altair + Vega backend:

na.altair.plot_heatmap(data)

Droppables are those values that will be dropped if we simply use pandas.DataFrame.dropna() on the entire dataset.

Stairs plot

Stairs plot is one more useful visualization of dataset shrinkage on applying pandas.Series.dropna() method to each column sequentially (sorted by the number of NA values, by default):

na.altair.plot_stairs(data)

After dropping all NAs in Cabin column, we are left with 21 more NAs (in Age and Embarked columns). This plot also shows tooltips with exact numbers of NA values that are dropped per each column.

Histogram

You may need to adjust some parameters before a histogram starts looking as you expect:

chart = na.altair.plot_hist(data, col='Pclass', col_na='Age')\
    .properties(width=200, height=200)
chart.configure_axisX(labelAngle = 0)

Regression model

We can build a logistic regression model with Age as a dependent variable and Fare, Parch, Pclass, SibSp, Survived as independent variables. Internally, pandas.Series.isna() method is called on Age column, and the resulting boolean values are converted to integers (True/False becomes 1/0). Finally, fitting a logistic model is done by statsmodels package:

# Selecting columns with numeric data
# Dropping "PassengerId" column
subset = data.loc[:, data.dtypes != object].drop(columns=['PassengerId'])
model = na.model(subset, col_na='Age')
model.summary()

Optimization terminated successfully.
Current function value: 0.467801
Iterations 7
                        Logit Regression Results
===============================================================================
Dep. Variable:                    Age   No. Observations:                   891
Model:                          Logit   Df Residuals:                       885
Method:                           MLE   Df Model:                             5
Date:                Sat, 05 Jun 2021   Pseudo R-squ.:                  0.06164
Time:                        17:51:31   Log-Likelihood:                 -416.81
converged:                       True   LL-Null:                        -444.19
Covariance Type:            nonrobust   LLR p-value:                  1.463e-10
===============================================================================
                coef    std err          z      P>|z|      [0.025      0.975]
-------------------------------------------------------------------------------
(intercept)    -2.7294      0.429     -6.369      0.000      -3.569      -1.890
Fare            0.0010      0.003      0.376      0.707      -0.004       0.006
Parch          -0.8874      0.223     -3.984      0.000      -1.324      -0.451
Pclass          0.5953      0.147      4.046      0.000       0.307       0.884
SibSp           0.2548      0.095      2.684      0.007       0.069       0.441
Survived       -0.1026      0.198     -0.519      0.604      -0.490       0.285
===============================================================================

Interactive report

Use scikit_na.report() function to show interactive report interface:

na.report(data)

Export functionality

Export your analysis results to various formats for sharing and further processing:

Export summary statistics

# Export to CSV
na.export_summary(data, filename='missing_data_summary.csv', format='csv')

# Export to JSON
na.export_summary(data, filename='summary.json', format='json')

# Export to Excel
na.export_summary(data, filename='analysis.xlsx', format='xlsx')

Export interactive reports

# Export complete report to HTML
na.export_report(data, filename='missing_data_report.html', format='html')

# Export with custom columns
na.export_report(
    data, 
    columns=['Age', 'Cabin', 'Embarked'],
    filename='focused_analysis.html', 
    format='html'
)

The export functionality supports:

• CSV: Summary statistics in tabular format
• JSON: Structured data for programmatic access
• HTML: Interactive reports for web viewing
• XLSX: Excel-compatible spreadsheets

API Reference

Statistical Functions

• summary(data, columns=None, per_column=True, round_dec=2) - Missing data statistics
• correlate(data, columns=None, drop=True, **kwargs) - Correlation analysis
• describe(data, col_na, columns=None, na_mapping=None) - Grouped descriptive stats
• model(data, col_na, columns=None, intercept=True, **kwargs) - Logistic regression
• test_hypothesis(data, col_na, test_fn, columns=None, **kwargs) - Hypothesis testing
• stairs(data, columns=None, **kwargs) - Dataset shrinkage analysis

Visualization Functions

• altair.plot_heatmap(data, **kwargs) - Missing data heatmap
• altair.plot_corr(data, **kwargs) - Correlation heatmap
• altair.plot_stairs(data, **kwargs) - Stairs plot
• altair.plot_hist(data, col, col_na, **kwargs) - Missing data histogram

Export Functions

• export_summary(data, filename, format, **kwargs) - Export summary statistics
• export_report(data, filename, format, **kwargs) - Export interactive reports

Interactive Reports

• report(data, columns=None, **kwargs) - Generate interactive widget-based report

Contribution

Any contribution is highly appreciated: pull requests, suggestions, or bug reports.