globestylizer 0.1.10

A tool for the Boston Globe for data cleaning

globestylizer

About state_abbr()

globestylizer is a Python library for use in the Boston Globe. It contains a module cleaner with a function called state_abbr and has pandas as a dependency.

The intended use case for this library is for cleaning a pandas Dataframe that contains state abbreviations that are not adherent to Globe style, and returning a copy of the Dataframe that is in Globe style.

Installation

In your virtual environment, for example pipenv, pipenv install globestylizer. Remember that this is dependent on pandas as well.

Usage

Import globestylizer's cleaner in your py file or Jupyter notebook: from globestylizer import cleaner

You can call the state_abbr function on any pandas Dataframe and it will parse the entire Dataframe for state abbreviations or full state names. It is case-insensitive so it can handle any variety of capitalizations. Here's example data:

State1	State2	Value
Alaska	Alaska	15
AK	AK	654
AL	AL	45.32
Alabama	Alabama	789
Tennessee	Tennessee	15
Tenn.	Tenn.	3
TN	TN	31.2
WYOMING	WYOMING	789
wyoming	wyoming	455
WYoming	WYoming	11
WY	WY	2

Assuming you have a pandas Dataframe defined as df, you can call it as so:

abbreviated_df = cleaner.state_abbr(df)

And abbreviated_df will return:

State1	State2	Value
Alaska	Alaska	15.0
Alaska	Alaska	654.0
Ala.	Ala.	45.32
Ala.	Ala.	789.0
Tenn.	Tenn.	15.0
Tenn.	Tenn.	3.0
Tenn.	Tenn.	31.2
Wyo.	Wyo.	789.0
Wyo.	Wyo.	455.0
Wyo.	Wyo.	11.0
Wyo.	Wyo.	2.0

You also have the option of parsing only selected columns. If you have state names as part of strings in other columns that you don't want abbreviated, pass in the columns that you do want abbreviated as a second argument. Assuming you have data as such, and only want to clean the column 'State2':

State1	State2	Value
Alaska	Alaska	15
AK	AK	654
AL	AL	45.32
Alabama	Alabama	789
Tennessee	Tennessee	15
Tenn.	Tenn.	3
TN	TN	31.2
WYOMING	WYOMING	789
wyoming	wyoming	455
WYoming	WYoming	11
WY	WY	2

Pass in the column name as the second argument.

partially_abbreviated_df = cleaner.state_abbr(df, columns=['State2'])

And partially_abbreviated_df will return:

State1	State2	Value
Alaska	Alaska	15.0
AK	Alaska	654.0
AL	Ala.	45.32
Alabama	Ala.	789.0
Tennessee	Tenn.	15.0
Tenn.	Tenn.	3.0
TN	Tenn.	31.2
WYOMING	Wyo.	789.0
wyoming	Wyo.	455.0
WYoming	Wyo.	11.0
WY	Wyo.	2.0