pydit.wrangling

Sub-package (./wrangling) containing the core data wrangling functionality.

The modules are also self standing, you should be able to copy any .py file and import it in your script to use it with no dependencies on other modules.

There may be some exceptions to this principle in the logging module, but you should be able to create your own logger object and run with it.

anonymise	Module for anonymising a key/identifier column
blanks	Checks for various types of nulls/blanks in a dataframe and returns counts.
calendar_table	Function to create a calendar DataFrame to be used as a lookup table
cleanup_dataframe_columns_names	Module for cleaning up column names of a DataFrame
coalesce_dataframe_columns	Function for coalescing columns in a pandas DataFrame.
coalesce_dataframe_values	Creates a new column with the top N most frequent values and the rest are replaced by Other
collapse_dataframe_levels	Implementation of the collapse_levels function.
counts	Module that implements a few useful count related functions Takes inspiration on the usual counta and countif functions in Excel
date_time_calculations	Module with functions for date and time calculations.
duplicates	Module for checking for duplicates in a dataframe.
file_utils	File utilities for saving and loading files
fillna	Improving on fillna() with options for various data types and opinionated defaults.
fuzzy_matching	Module with utility functions for fuzzy matching
groupby_text_concatenate	Groupby text column into concatenated text
keyword_search_batch	Functions to sweep a dataframe for keywords and return a matrix of matches.
lookup_values(df, key, df_ref, key_ref, ...)	Lookup values from a reference dataframe and return values from a column If the key is a list, it will return a list of values
map_common_values	Module to map/add various values like 1, 2, 3 to "High", "Medium", "Low".
merge	Module to merge dataframes with prefixes or suffixes for all fields not just those that have colissions.
referential_integrity_check	Module to perform referential integrity checks on two dataframes.
sequence	Module to check for numerical sequence of DataFrame column or Series
split_transactions	Utility functions to do analysis/detection of split purchases/expenses
truncate_datetime	Implementation of the truncate_datetime family of functions.
various	Utility functions, they are not used directly in the core functions.