Metadata-Version: 2.1
Name: data-inspector
Version: 0.0.5
Summary: This module brings different functions to make EDA, data cleaning easier.
Home-page: UNKNOWN
Author: Kazi Amit Hasan
Author-email: kaziamithasan89@gmail.com
License: UNKNOWN
Keywords: eda
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: pandas (==1.1.2)
Requires-Dist: matplotlib (==3.1.2)
Requires-Dist: numpy (==1.18.5)
Requires-Dist: seaborn (==0.11.1)
Requires-Dist: scipy (==1.6.2)

# Data Inspector 
## This module brings different functions to make EDA, data cleaning easier. 
### Author: Kazi Amit Hasan

## Project Description: 

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

data inspector helps to make 
## Installation

```pip install 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)```







Change Log
==========

0.0.1 (20/08/2021)
------------------
- First Release

0.0.2 (20/08/2021)
------------------
- Minor updates

0.0.3 (20/08/2021)
------------------
- Minor updates

0.0.4 (20/08/2021)
------------------
- Minor updates

