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Version Build status Code coverage Support Python versions


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


  • 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.


pip install weightedcalcs


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.

Weightedcalcs in the wild

Other Python weighted-calculation libraries


Pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.





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