Visualization of column value relationship
Project description
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
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