Data Inspector

Data Inspector is an open-source python library that brings 15 types of different functions to make EDA, data cleaning easier.

Author: Kazi Amit Hasan

Project Description:

Data Inspector brings 15++ essential exploratory data analysis, data cleaning automations to make a dataset understandable. This is a perfect tool to get started with you data.

Latest Added Feature:

Added regplots in the library

Installation:

pip install data-inspector

Package available at https://pypi.org/project/data-inspector/

Available automation:

1. Line plot : line_plot(data, x_data, y_data, x_label="", y_label="", title="")
2. Skew feature: plot_skewed_feature(data, column)
3. Showing data distribution: show_distribution(data, column)
4. Scatter plot: plot_scatter(data,x_data, y_data)
5. Correlation plot: plot_correlation(data)
6. Create histogram: histogram(data,column, x_label, y_label, title)
7. Create bar plot: plot_bar(data, column, xlabel, ylabel, title)
8. Create boxplots of all features: box_plot(data)
9. Checking dataset's shape: datasetShape(data)
10. Get dataset's diagnostic plots: diagnostic_plots(data, variable)
11. Divide numerical and categorical features: divideFeatures(data)
12. Fill NaN values: fillNan(data, column, value)
13. Get pearson's correlation between two variables: get_correlation(column_1, column_2, data)
14. Plotting kde plots: plot_cont_kde(data, var)
15. Automatic calculating the missing values and their percentage along with visualization : calculating_missing_values(data)
16. Regression plot with 95% CI : plot_regplot(data,x_data, y_data)

Tutorial:

Link: https://github.com/AmitHasanShuvo/data-inspector/blob/main/notebook/example%20notebook.ipynb
Colab link: https://colab.research.google.com/drive/1mj9gz2XyQprSYdKMUKlKkJ9Qi8XmleHW?usp=sharing

Some visualizations:

How to cite:

@online{data-inspector,
title={data-inspector},
url={https://pypi.org/project/data-inspector/},
urldate = {2021-08-21}, 
publisher={Kazi Amit Hasan}
}

Future Works:

1. Add some automations for time series data.

How to contribute:

Any contribution would be highly appreciated. Kindly go through the guidelines for contributing in github.