Missingno is a Python library, version 0.5.2, designed for visualizing missing data in datasets. It offers a small toolset of flexible and easy-to-use visualizations including matrix, bar, heatmap, and dendrogram plots, allowing users to quickly gain a visual summary of data completeness. It is actively maintained with recent releases addressing compatibility and adding features.
Install & Compatibility
Where this runs
tested against v0.5.2 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.920 runs
installs and imports cleanly · install 0.0s · import 5.431s · 395.4MB
glibcpy 3.10–3.920 runs
installs and imports cleanly · install 15.3s · import 5.151s · 379MB
398MB installed
● package 398MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to create a Pandas DataFrame with simulated missing values and then visualize them using `missingno.matrix` and `missingno.bar`. The matrix plot provides a visual summary of missing data patterns, while the bar chart shows the count of non-null values per column.
import pandas as pd
import numpy as np
import missingno as msno
import matplotlib.pyplot as plt
# Create a sample DataFrame with missing values
data = {
'A': [1, 2, np.nan, 4, 5],
'B': [np.nan, 2, 3, 4, np.nan],
'C': [1, 2, 3, np.nan, 5],
'D': [1, 2, 3, 4, 5]
}
df = pd.DataFrame(data)
print("DataFrame with missing values:")
print(df)
print("\nMissingno Matrix Visualization:")
# Generate a missingness matrix plot
msno.matrix(df, figsize=(8, 4))
plt.title('Missing Data Matrix')
plt.show()
print("\nMissingno Bar Chart Visualization:")
# Generate a bar chart of missingness
msno.bar(df, figsize=(8, 4))
plt.title('Missing Data Bar Chart')
plt.show()
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Version history
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Security & dependencies
CVE tracking and dependency tree are planned for a later release.