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weightedcalcs

weightedcalcs is a pandas-based Python library for calculating weighted means, medians, standard deviations, and more.

Features

Installation

pip install weightedcalcs

Usage

Getting started

Every weighted calculation in weightedcalcs begins with an instance of the weightedcalcs.Calculator class. Calculator takes one argument: the name of your weighting variable. So if you're analyzing a survey where the weighting variable is called "resp_weight", you'd do this:

import weightedcalcs as wc
calc = wc.Calculator("resp_weight")

Types of calculations

Currently, weightedcalcs.Calculator supports the following calculations:

The obj parameter above should one of the following:

Basic example

Below is a basic example of using weightedcalcs to find what percentage of Wyoming residents are married, divorced, et cetera:

import pandas as pd
import weightedcalcs as wc

# Load the 2015 American Community Survey person-level responses for Wyoming
responses = pd.read_csv("examples/data/acs-2015-pums-wy-simple.csv")

# `PWGTP` is the weighting variable used in the ACS's person-level data
calc = wc.Calculator("PWGTP")

# Get the distribution of marriage-status responses
calc.distribution(responses, "marriage_status").round(3).sort_values(ascending=False)

# -- Output --
# marriage_status
# Married                                0.425
# Never married or under 15 years old    0.421
# Divorced                               0.097
# Widowed                                0.046
# Separated                              0.012
# Name: PWGTP, dtype: float64

More examples

See this notebook to see examples of other calculations, including grouped calculations.

Max Ghenis has created a version of the example notebook that can be run directly in your browser, via Google Colab.

Weightedcalcs in the wild

Other Python weighted-calculation libraries