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weightedcalcs

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

Features

Plays well with pandas.
Support for weighted means, medians, quantiles, standard deviations, and distributions.
Support for grouped calculations, using DataFrameGroupBy objects.
Raises an error when your data contains null-values.
Full test coverage.

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:

calc.mean(my_data, value_var): The weighted arithmetic average of value_var.
calc.quantile(my_data, value_var, q): The weighted quantile of value_var, where q is between 0 and 1.
calc.median(my_data, value_var): The weighted median of value_var, equivalent to .quantile(...) where q=0.5.
calc.std(my_data, value_var): The weighted standard deviation of value_var.
calc.distribution(my_data, value_var): The weighted proportions of value_var, interpreting value_var as categories.
calc.count(my_data): The weighted count of all observations, i.e., the total weight.
calc.sum(my_data, value_var): The weighted sum of value_var.

The obj parameter above should one of the following:

A pandas DataFrame object
A pandas DataFrame.groupby object
A plain Python dictionary where the keys are column names and the values are equal-length lists.

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.