Welcome to etl_toolkit’s documentation!

The etl_toolkit contains many utilities to write better pyspark to simplify pipelines on Databricks. Navigate to each module of the etl_toolkit to learn about the functions provided and how to use them.

• expressions: Contains functions for deriving complex pyspark.Columns

• analyses: Contains functions for deriving complex pyspark.DataFrames that apply many data transformations.

Contents:

• E module (“expressions”)
  • Aggregate Expressions
  • Boolean Expressions
  • Calculation Expressions
  • ID Expressions
  • Mapping Expressions
    • chain_assigns()
  • Normalization Expressions
  • Regex Expressions
  • Time Expressions
• A module (“analyses”)
  • Calculation Analyses
  • Card Analyses
  • E-Receipt Analyses
  • Dedupe Analyses
  • Index Analyses
  • Investor Standard Analyses
  • Investor Reporting Analyses
    • backtest_configuration()
    • add_unified_consensus_column()
  • Ordering Analyses
  • Parser Analyses
    • parser()
  • Scalar Analyses
  • Time Analyses
  • Comparison Analyses
  • Investor Standard Metrics Analyses
  • Calendar Analyses
• Table creation functions
  • Batch Tables
  • Standard Metrics Tables
• Mutable Table functions
  • Table Operations

Indices and tables

• Index

• Module Index

• Search Page