USAggregate 1.0.4

A package for aggregating and merging US geographic data frames.

USAggregate

USAggregate is a Python package for aggregating and merging US relational data frames.

Example Use Case

Merging demographic data at the zip code level with ice cream sales data at the city level to measure the correlation between demographics and ice cream sales at the county level.

Installation

You can install the package using pip:

pip install USAggregate

Use Notes

zip codes in your data sets should be converted to strings before applying 'usaggregate' function. Numeric option will be available in a later version. Convert respect geographic identifiers to 'zip', 'city', 'county' and/or 'state' before applying 'usaggregate' function. Capabilities to handle other column names will be available in a later version. Function is only available for cross-sectional data. Panel data capabilities will be available in a later version.

Below is an example of package usage.

import pandas as pd
from USAggregate import usaggregate

data_zip = pd.DataFrame({
    'zip': ['98199', '98103', '98001', '98002', '91360', '91358', '93001', '93003'],
    'value1': [1, 2, 3, 4, 5, 6, 7, 8],
    'chr1': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']
    })

data_city = pd.DataFrame({
        'city': ['Seattle', 'Auburn', 'Thousand Oaks', 'Ventura'],
        'state': ['WA', 'WA', 'CA', 'CA'],
        'value2': [1, 2, 3, 4],
        'chr2' : ['I', 'J', 'K', 'L']
    })

data_county = pd.DataFrame({
        'county': ['King County', 'Ventura County'],
        'state': ['Washington', 'California'],
        'value3': [5, 6],
        'chr3': ['M', 'N']
    })

# Using the function
result = usaggregate([data_zip, data_city, data_county], level='state', agg_numeric='sum', agg_character='first')
print(result)