https://github.com/alastairrushworth/inspectpd
📈 📊 Methods for Exploring Pandas Data Frames
https://github.com/alastairrushworth/inspectpd
categorical-data-visualization correlation dataframe eda exploratory-data-analysis pandas python
Last synced: 11 months ago
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📈 📊 Methods for Exploring Pandas Data Frames
- Host: GitHub
- URL: https://github.com/alastairrushworth/inspectpd
- Owner: alastairrushworth
- License: mit
- Created: 2019-10-13T14:35:34.000Z (over 6 years ago)
- Default Branch: main
- Last Pushed: 2025-01-31T06:25:16.000Z (about 1 year ago)
- Last Synced: 2025-04-10T19:10:00.592Z (11 months ago)
- Topics: categorical-data-visualization, correlation, dataframe, eda, exploratory-data-analysis, pandas, python
- Language: Python
- Homepage:
- Size: 117 KB
- Stars: 7
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# inspectpd
## Overview
`inspectpd` is collection of utilities for columnwise summary,
comparison and visualisation of `pandas` dataframes. The package patches
in the following methods for pandas objects:
- [`.inspect_types()`](#column-types) summary of column types
- [`.inspect_mem()`](#memory-usage) summary of memory usage of columns
- [`.inspect_na()`](#missing-values) columnwise prevalence of missing
values
- [`.inspect_cor()`](#correlation) correlation coefficients of numeric
columns
- [`.inspect_imb()`](#feature-imbalance) feature imbalance of
non-numeric
- [`.inspect_num()`](#numeric-summaries) summaries of numeric columns
- [`.inspect_cat()`](#categorical-levels) summaries of non-numeric
## Installation and use
You can install `inspectpd` using `pip` with
```python
pip install inspectpd
```
Simply import the package and use the methods on a pandas dataframe:
```
import pandas as pd
import inspectpd
# example data set just for illustration
from inspectpd import starwars
# categorical features summary
starwars.inspect_cat()
```
You can also get a quick visualisation of the summary too:
```
# get a plot of the categorical features
starwars.inspect_cat().view()
```