pyjanitor 0.20.10

Tools for cleaning pandas DataFrames

pyjanitor is a Python implementation of the R package janitor, and provides a clean API for cleaning data.

Why janitor?

Originally a port of the R package, pyjanitor has evolved from a set of convenient data cleaning routines into an experiment with the method chaining paradigm.

Data preprocessing usually consists of a series of steps that involve transforming raw data into an understandable/usable format. These series of steps need to be run in a certain sequence to achieve success. We take a base data file as the starting point, and perform actions on it, such as removing null/empty rows, replacing them with other values, adding/renaming/removing columns of data, filtering rows and others. More formally, these steps along with their relationships and dependencies are commonly referred to as a Directed Acyclic Graph (DAG).

The pandas API has been invaluable for the Python data science ecosystem, and implements method chaining of a subset of methods as part of the API. For example, resetting indexes (.reset_index()), dropping null values (.dropna()), and more, are accomplished via the appropriate pd.DataFrame method calls.

Inspired by the ease-of-use and expressiveness of the dplyr package of the R statistical language ecosystem, we have evolved pyjanitor into a language for expressing the data processing DAG for pandas users.

Functionality

Current functionality includes:

• Cleaning columns name (multi-indexes are possible!)

• Removing empty rows and columns

• Identifying duplicate entries

• Encoding columns as categorical

• Splitting your data into features and targets (for machine learning)

• Adding, removing, and renaming columns

• Coalesce multiple columns into a single column

• Date conversions (from matlab, excel, unix) to Python datetime format

• Expand a single column that has delimited, categorical values into dummy-encoded variables

• Concatenating and deconcatenating columns, based on a delimiter

• Syntactic sugar for filtering the dataframe based on queries on a column

• Experimental submodules for finance, biology, chemistry, engineering, and pyspark