weightipy 0.3.0

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Weightipy

Weightipy is a cut down version of Quantipy3 for weighting people data using the RIM (iterative raking) algorithm.

Changes from Quantipy

• Removed all quantipy overhead. Weightipy supports the latest versions of Pandas and Numpy and is tested for Python 3.7, 3.8, 3.9, 3.10 and 3.11.
• Weightipy runs up to 6 times faster than Quantipy, depending on the dataset.
• Rim class will not generate reports like Quantipy did, unless the parameter verbose is set to True on the Rim constructor.

Installation

pip install weightipy

or

python3 -m pip install weightipy

Create a virtual envirionment

If you want to create a virtual environment when using Weightipy:

conda

conda create -n envwp python=3

with venv

python -m venv [your_env_name]

5-minutes to Weightipy

Get started

Assuming we have the variables gender and agecat we can weight the dataset like this:

import weightipy as wp

targets = {
    "agecat": {"18-24": 5.0, "25-34": 30.0, "35-49": 26.0, "50-64": 19.0, "65+": 20.0},
    "gender": {"Male": 49, "Female": 51}
}
scheme = wp.scheme_from_dict(targets)

df_weighted = wp.weight_dataframe(
    df=my_df,
    scheme=scheme,
    weight_column="weights"
)
efficiency = wp.weighting_efficiency(df_weighted["weights"])

In case we are working with census data, which also includes a region variable and we would like to weight the data by age and gender in each region, we can use the scheme_from_df function:

import weightipy as wp
import pandas as pd

df_data = pd.read_csv("data_to_weight.csv")
df_census = pd.read_csv("census_data.csv")

scheme = wp.scheme_from_df(
    df=df_census,
    cols_weighting=["agecat", "gender"],
    col_filter="region",
    col_freq="freq"
)
df_weighted = wp.weight_dataframe(
    df=d,
    scheme=scheme,
    weight_column="weights"
)
efficiency = wp.weighting_efficiency(df_weighted["weights"])

Or by using the underlying functions that will give more access to the weighting process, we can use the Rim and WeightEngine classes directly:

import weightipy as wp

# in this example, agecat and gender are int dtype

age_targets = {'agecat':{1:5.0, 2:30.0, 3:26.0, 4:19.0, 5:20.0}}
gender_targets = {'gender':{0:49, 1:51}}
scheme = wp.Rim('gender_and_age')
scheme.set_targets(targets=[age_targets, gender_targets])

my_df["identity"] = range(len(my_df))
engine = wp.WeightEngine(data=df)
engine.add_scheme(scheme=scheme, key="identity", verbose=False)
engine.run()
df_weighted = engine.dataframe()
col_weights = f"weights_{scheme.name}"

efficiency = wp.weighting_efficiency(df_weighted[col_weights])

print(engine.get_report())

Weight variable       weights_gender_and_age
Weight group                  _default_name_
Weight filter                           None
Total: unweighted                 582.000000
Total: weighted                   582.000000
Weighting efficiency               60.009826
Iterations required                14.000000
Mean weight factor                  1.000000
Minimum weight factor               0.465818
Maximum weight factor               6.187700
Weight factor ratio                13.283522

For more references on the underlying classes, refer to the Quantipy documentation

Overview of functions to get started:

Function	Description
weight_dataframe	Weights data by scheme, returns modified dataframe with new weight column.
weighting_efficiency	Takes weights and returns efficiency of weighting. See: https://quantipy.readthedocs.io/en/staging-develop/sites/lib_doc/weights/03_diags.html#the-weighting-efficiency
scheme_from_dict	Turns a dict of dicts into a Rim scheme. Keys of the dict are column names and the values are distributions. These are normalized.
scheme_from_df	Creates a Rim scheme from a dataframe from specified weighting columns and frequency column. Useful when working with census data.
Rim class	Useful for creation of more complex weighting schemas. For example when weighting subregions or groups, which require filters. See: https://quantipy.readthedocs.io/en/staging-develop/sites/lib_doc/weights/02_rim.html#using-the-rim-class
WeightEngine class	Useful for more specialised manipulation of the weighting process

Planned features

• More utility functions to simplify the weighting process
• More performance improvements, in order to better support batch weighting of many datasets
• Support for multithreaded weighting (possibly using Polars)
• Rewrite of the API to be less oriented towards how Quantipy worked and more in line with simple weighting needs
• Far future: Support for more weighting algorithms

Contributing

The test suite for Weightipy can be run with the command

python3 -m pytest tests

But when developing a specific aspect of Weightipy, it might be quicker to run (e.g. for the Rim class)

python3 -m unittest tests.test_rim

We welcome volunteers and supporters. Please include a test case with any pull request, especially those that run calculations.

Quantipy

Origins

• Quantipy was concieved of and instigated by Gary Nelson: http://www.datasmoothie.com

Contributors on Quantipy

• Alexander Buchhammer, Alasdair Eaglestone, James Griffiths, Kerstin Müller : https://yougov.co.uk
• Datasmoothie’s Birgir Hrafn Sigurðsson and Geir Freysson: http://www.datasmoothie.com