Package to efficiently create network measures using CBS networks (POPNET) in the RA. For example you may be interested in calculating the average income of the parents of the classmates of a student. This package allows you to do this in a fast and efficient way.
pip install git+https://[email protected]/sodascience/netcbs.git@mainSee notebook for accessible information and examples.
Create network measures (e.g. the average income and age of the parents (link type 301) of the classmates of children in the sample)
query = "[Income, Age] -> Family[301] -> Schoolmates[all] -> Sample"
df = netcbs.transform(query,
df_sample = df_sample, # dataset with the sample to study
df_agg = df_agg, # dataset with the income variable
year=2021, # year to study
cbsdata_path='G:/Bevolking', # path to the CBS data
agg_funcs=[pl.mean, pl.sum, pl.count], # calculate the average
return_pandas=False, # returns a pandas dataframe instead of a polars dataframe
lazy=True # use polars lazy evaluation (faster/less memory usage)
)The library uses a query system to specify the relationships between the main sample dataframe and the context data. The query consists of a series of context types separated by arrows (->), with optional relationship types in square brackets. For example, the query "[Income] -> Family[301] -> Schoolmates[all] -> Sample" specifies that the income of the parents of the student's classmates should be calculated based on the provided sample dataframe.
The library checks the latest verion of each network file for the year specified in the transform function.
The library removes duplicate entries from the df_sample and df_agg dataframes, and converts them to polars for efficient.
The validate_query function (called automatically by the transform function) ensures that the query string is correctly formatted and that all necessary columns are present in the input dataframes. It splits the query into individual contexts and verifies each part, raising errors for any issues found.
The different network files (contexts) are merged (inner join) consecutively based on the relationship columns specified in the query. The resulting dataframe is then aggregated based on the aggregation function (e.g., pl.mean, pl.sum) specified in the transform function.
We recommend to use the polars lazy evaluation (lazy=True) to reduce memory usage and speed up the calculations. For debugging this can be disabled by setting lazy=False.
Contributions are what make the open source community an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
Please refer to the CONTRIBUTING file for more information on issues and pull requests.
The package netCBS is published under an MIT license. When using netCBS for academic work, please cite:
TODO
This project is developed and maintained by the ODISSEI Social Data Science (SoDa) team.
Do you have questions, suggestions, or remarks? File an issue in the issue tracker or feel free to contact the team via https://odissei-data.nl/en/using-soda/.