exploratory 3.4.10

Exploratory Data Analysis

exploratory

Exploratory Data Analysis

Description

This project explortory was created to perform Exploratory Data Analysis on any structured dataset. Dataset can have categorical or numerical data types. This project takes pandas dataframe and gives summary statistics and individual plots having categorical count for catagorical variables and PDF's, CDF's with mean, median and mode for numerical variables. The both the results are stored in PDF and CSV file in your current directory/path.

Installation:

Use the package manager pip to install exploratory

pip install exploratory

Usage:

from exploratory import EDA
EDA(df)
# df --> pandas dataframe
#Please input the DPI value, as DPI value increases runtime would increase. Defualt DPI value: 150

Expected Outputs:

• CSV File, DataFrame Containing

Column	Description
Variable	Variable Name in the dataset provided
Cardinality	Number of levels/classes in each variable
total_count	Count of total records (non null)
unique_rate	Cardinality / total_count, Unique Rate of 1 indicates a ID variable
percent_missing	Percentage of missing values across each column
mean	Average of column (Ignores Object/String variables)
std	Standard deviation of column (Ignores Object/String variables)
min	Minimum of column (Ignores Object/String variables)
25%	25th percentile value of column (Ignores Object/String variables)
median	50th percentile value of column (Ignores Object/String variables)
75%	75th percentile value of column (Ignores Object/String variables)
max	Maximum of column (Ignores Object/String variables)
data_types	Data type of column (Int / Float / Object etc)
range	Max Value - Min Value (Ignores Object/String variables)

• PDF with Statistical Summary and variable distribution graphs (categorical & continous)

Contributing

Pull requests are welcome. Please use this 'https://github.com/Abhilash-MS/exploratory' Please feel free to contact authors for any suggestions or issues, Ram [e-mail], Abhilash [e-mail]