weightipy 0.2.0

No project description provided

Weightipy

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

Planned features

• Support for multithreaded weighting
• Support for more weighting algorithms
• Rewrite of the API to be less oriented towards how Quantipy worked and more in line with simple weighting needs

Origins

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

Contributors

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

Installation

pip install weightipy

or

python3 -m pip install weightipy

Note that the package is called weightipy on pip.

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

Weighting

If your data hasn't been weighted yet, you can use Weightipy's RIM weighting algorithm.

Assuming we have the variables gender and agecat we can weight the dataset with these two variables:

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"])

Or if we want more control of the raking process, we can use the Rim class directly:

import weightipy as wp

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])

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

Or by using the underlying functions that will give more access to reports etc:

...

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

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 DataSet)

python3 -m unittest tests.test_rim

Tests for unsupported features are skipped, see here for what tests are supported.

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