Metadata-Version: 2.1
Name: pycateda
Version: 1.0.1
Summary: pycateda Python Package - A simplified Exploratory Data Analysis with less lines of code
Home-page: https://github.com/arjuns2020/pycateda
Author: Arjun Srinivasa Murthy
Author-email: arjunsm2020@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: seaborn
Requires-Dist: missingno
Requires-Dist: notebook

# pycateda Python Package - A simplified Exploratory Data Analysis with less lines of code

![pycateda](https://miro.medium.com/max/658/1*0JSGjWN6mXIShXvkam-Q2A.png)

# What does it do?

1. Read the data and returns a DataFrame
2. Get a summary of basic descriptive analysis of the dataset
   A. Top 5 rows
   B. Bottom 5 rows
   C. The shape of the Data Frame
   D. Missing Values in the data frame
   E. Duplicate rows
   F. Descriptive statistics for Numerical + Categorical columns
   G. Number of unique values for each feature
   H. The unique identifier
   I. Prints Unique Value of Each feature
   J. Relative frequency (%) of unique values or bins of numerical features
3. Get the Count of Null Values in each column
4. Get Percentage % of missing values in each column
5. Shows Missing Values Plots of the DataFrame
6. Build a Profile Report of the entire dataset
7. Check numerical variables in the DataFrame
8. Check discrete variables in the Data Frame
9. Check Continuous variables in the Data Frame
10. Analyze continuous variables Visually in the Data Frame
11. Analyze Transformed ( Log ) Continuous Variables
12. Analyze Outliers in the data frame (Box Plots)


