rumor_view

Python library to visualize categorical column values relationship.

Objective

Help explore data frame to find column values which has strong relationship. This module estimate P(x1=v1 | x2=v2), and consider significance based on "lift" := P(x1=v1 | x2=v2) / P(x1=v1).

There are two main functionalities;

• show_columns
  • This method provides a map for your exploration. This visualize graph which node represents each column, and edge between nodes when correspondent columns have lift in some value pair of those columns.
• show_relation
  • This method provides a detailed view for a pair of columns. With this you can see concrete values which has strong relationship

Installation

pip install rumor_view

Usage

Right now this module handles all the column as categorical. If you have numeric columns it is recommended to convert them as categorical values before inputing this module.

import rumor_view


view = rumor_view.RumorView(df)

view.show_columns() # this shows all the columns
view.show_columns(target_column="target") # this shows only 2 hops from target column

view.show_relations("target", "column") # this shows value relationships between input columns