Welcome to pd-explain Documentation

Unlock the power of data exploration and storytelling with pd-explain, your indispensable extension to the Pandas library. Built on the foundation of ExplainED: Explanations for EDA Notebooks, pd-explain allows you effortlessly filter and analyze your data, gaining valuable insights that go beyond basic data manipulation.

Key Features:

• Data Filtering: Easily filter your DataFrame by values in specific columns.

• Data Storytelling: pd-explain provides insightful narratives comparing filtered data to the original dataset.

• Data Visualization: Visualize your data transformations on interactive graphs.

• Group-By Analysis: Extend your analysis to group-by DataFrames, uncovering trends and patterns within your data.

Empower your data analysis workflow and let pd-explain turn your data into compelling stories. Dive into our comprehensive documentation to unleash the full potential of your data exploration journey.

Contents

• Getting Started
  • Installation
  • Importing pd-explain
  • Reading a Table
  • Making it Explainable
• Explain Function - Filter
  • Method Signature
  • Parameters
  • Filter Usage Example with “spotify_all” DataFrame
  • Expected Output
• Parameter Details
  • Using the “top_k”, “figs_in_row” Parameters
  • Controlling Correlation with “corr_TH”
  • Presenting Deleted Correlations
• Explain Function - Group By
  • Group By Usage Example with “spotify_all” DataFrame
  • Output Description
  • Additional Group By Usage Example with “spotify_all” DataFrame
  • Output Description
  • Expected Output
• ExpDataFrame Class API
  • Overview
  • Key Attributes
  • Methods
    • explain()
    • present_deleted_correlated()
    • copy()
• ExpDataFrameGroupBy Class API
  • Overview
  • Methods
    • count()
    • mean()
    • median()
    • sum()
    • min()
    • max()