Weightipy

Weightipy is a modernized, lightweight, and high-performance library for weighting survey data using the RIM (iterative raking) algorithm. It is a streamlined fork of Quantipy3.

⚠️ pandas 3.0 users: Versions <0.4.2 are broken with pandas 3.0 (weights silently fail). Upgrade: pip install weightipy>=0.4.2

Why Weightipy?

• Fast: Runs up to 6x faster than Quantipy.
• Modern: Supports Python 3.7+ and the latest Pandas/Numpy versions.
• Flexible: Supports simple raking, segmented (nested) weighting, and loading targets from various census data formats.
• Lightweight: Removed heavy dependencies and reporting overhead to focus purely on the weighting engine.

Installation

pip install weightipy

Quick Start

Weightipy creates a new column of weights that aligns your dataset's distribution with specific targets.

1. Simple Weighting (Manual Dictionary)

If you have a simple list of percentages, you can define them in a dictionary.

import weightipy as wp
import pandas as pd

# Your survey data
df = pd.read_csv("my_survey.csv")

# Define targets (percentages)
targets = {
    "age_group": {"18-24": 10.0, "25+": 90.0},
    "gender": {"Male": 49.0, "Female": 51.0}
}

# Create schema and weight
scheme = wp.scheme_from_dict(targets)
df_weighted = wp.weight_dataframe(df, scheme, weight_column="weights")

# Check efficiency
eff = wp.weighting_efficiency(df_weighted["weights"])
print(f"Weighting Efficiency: {eff:.2f}%")

2. Segmented Weighting (Nested)

A common requirement is to weight specific groups differently (e.g., weight Age and Gender within Region, while also correcting the size of the Regions themselves).

You can now do this in a single step using a Segmented Scheme:

targets = {
    "segment_by": "region",
    "segment_targets": {"North": 40.0, "South": 60.0}, # Global proportions
    "segments": {
        "North": {
            "age_group": {"18-24": 15.0, "25+": 85.0},
            "gender": {"Male": 50.0, "Female": 50.0}
        },
        "South": {
            "age_group": {"18-24": 10.0, "25+": 90.0},
            "gender": {"Male": 48.0, "Female": 52.0}
        }
    }
}

scheme = wp.scheme_from_dict(targets)
df_weighted = wp.weight_dataframe(df, scheme)

Working with Census Data

Manually typing targets is tedious. Weightipy provides tools to generate schemas directly from census tables or reference datasets.

Scenario A: You have "Tidy/Long" Aggregates

Common with US Census API, Eurostat, tidycensus, or SQL exports.

If your target data looks like this:

Use scheme_from_long_df:

df_census = pd.read_csv("census_long_format.csv")

scheme = wp.scheme_from_long_df(
    df=df_census,
    col_variable="Variable", # Column containing 'Age', 'Gender'
    col_category="Category", # Column containing '18-24', 'Male'
    col_value="Count",       # Column containing the population count
    col_filter="Region"      # Optional: Split schema by Region
)

df_weighted = wp.weight_dataframe(df, scheme)

Scenario B: You have Reference Data (Wide/Detailed)

Common when you have a "Golden Standard" dataset, a detailed frequency table of all combinations, or raw microdata.

If your target data looks like this (one row per combination, or one row per combination of demographic variables):

Use scheme_from_df. Weightipy will group and sum the data to calculate the correct distributions.

df_reference = pd.read_csv("census_detailed.csv")

scheme = wp.scheme_from_df(
    df=df_reference,
    cols_weighting=["Age", "Gender"],
    col_freq="Population_Count",
    col_filter="Region"  # Optional: Weight Age/Gender within Region
)

df_weighted = wp.weight_dataframe(df, scheme)

Data Validation

Before applying weights, it is highly recommended to validate that your survey data aligns with your schema. Weightipy can detect critical errors (e.g., a category exists in the census but is missing in the survey) and warnings (e.g., targets not summing to 100%).

# Get a report of all issues (does not raise exception)
report = wp.validate_scheme_dict(df, targets, raise_error=False)

if not report.empty:
    print(report)
    # Columns: [group, variable, issue_type, severity, details]

# Or strict validation (raises ValueError on Critical errors)
wp.validate_scheme_dict(df, targets, raise_error=True)

Serialization & Advanced Usage

For advanced workflows—such as manual overrides, multi-threading, or network transmission—it is often better to work with the raw configuration dictionary rather than the Rim class directly.

Weightipy exposes the intermediate extraction functions for this purpose. These return a JSON-serializable dictionary.

# 1. Extract raw dictionary from data
config = wp.scheme_dict_from_df(df_census, cols_weighting=..., col_freq=...)

# 2. Modify manually (e.g., fix a specific target)
config['segments']['North']['age_group']['18-24'] = 12.5

# 3. Serialize (safe for network or threading)
import json
payload = json.dumps(config)

# 4. Reconstruct Scheme later/elsewhere
scheme = wp.scheme_from_dict(config)

API Reference

Contributing

We welcome volunteers!

• Run Tests: python3 -m pytest tests
• Development: Please include a test case with any pull request.

Origins & Credits

Weightipy is based on Quantipy.

• Quantipy Creator: Gary Nelson (Datasmoothie)

• Contributors: Alexander Buchhammer, Alasdair Eaglestone, James Griffiths, Kerstin Müller (YouGov), Birgir Hrafn Sigurðsson, Geir Freysson.

• Weightipy: Remi Sebastian Kits