Basic Usage of `missingly`¶

This notebook provides a basic walkthrough of the main features of the missingly package.

1. Creating a sample dataset¶

[1]:

import pandas as pd
import numpy as np
import missingly as mi

data = {
    'A': [1, 2, np.nan, 4, 5, np.nan],
    'B': [10.0, np.nan, 30.0, 40.0, 50.0, 60.0],
    'C': [np.nan, np.nan, np.nan, np.nan, 100, 110]
}
df = pd.DataFrame(data)
df

[1]:

	A	B	C
0	1.0	10.0	NaN
1	2.0	NaN	NaN
2	NaN	30.0	NaN
3	4.0	40.0	NaN
4	5.0	50.0	100.0
5	NaN	60.0	110.0

2. Summary Functions¶

[2]:

mi.miss_var_summary(df)

[2]:

	variable	n_miss	pct_miss
0	A	2	33.333333
1	B	1	16.666667
2	C	4	66.666667

3. Visualizations¶

[3]:

mi.matrix(df)

[3]:

<Axes: >

[4]:

mi.dendrogram(df)

[4]:

<Axes: title={'center': 'Dendrogram of Variables by Missing Data Patterns'}>