frs

This contains a Python package for managing conversions of Family Resources Survey data into OpenFisca-UK compatible (entity-level) datasets. There are two main direct uses:

• Use the frs command to manage FRS datasets stored locally
• Import the package and use the frs.load() function to return the generated datasets

Command-line Tool

Instructions for usage:

usage: frs [-h] [--path PATH] {status,gen,regen}

Utility for managing Family Resources Survey microdata

positional arguments:
  {status,gen,regen}  The action to take on stored data

optional arguments:
  -h, --help          show this help message and exit
  --path PATH         The path to the FRS data

Viewing status

frs status prints out a summary of the status of the stored datasets. For example, when newly installed, it should output:

FRS status:
        FRS TAB files stored?                           No
        FRS OpenFisca-UK input files generated?         No
        OpenFisca-UK input files outdated?              N/A

When FRS data is loaded with frs gen, it should output:

FRS status:
        FRS TAB files stored?                           Yes
        FRS OpenFisca-UK input files generated?         Yes
        OpenFisca-UK input files outdated?              No (files generated with current version, 0.2.0)

Generating datasets

Run frs gen --path [PATH_TO_FRS_TAB_FILES] to generate the input datasets. The output should look like this:

Storing FRS files: 100%|██████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 97.44it/s]
Stored FRS source files successfully.
Generating OpenFisca-UK input datasets:
Reading adult.tab: 33238it [00:02, 11676.03it/s]
Reading benunit.tab: 22406it [00:00, 31335.18it/s]
Reading child.tab: 9849it [00:00, 33614.09it/s]
Reading job.tab: 20494it [00:00, 33871.75it/s]
Reading pension.tab: 10248it [00:00, 120552.48it/s]
Reading benefits.tab: 38475it [00:00, 83630.24it/s]
Reading accounts.tab: 65391it [00:00, 225508.67it/s]
Reading assets.tab: 18432it [00:00, 198199.28it/s]
Reading maint.tab: 438it [00:00, 109534.05it/s]
Reading chldcare.tab: 7285it [00:00, 145566.87it/s]
Writing person.csv file: 100%|███████████████████████████████████████████████████████| 43087/43087 [00:00<00:00, 59506.24it/s] 
Reading extchild.tab: 778it [00:00, 194699.79it/s]
Writing benunit.csv file: 100%|█████████████████████████████████████████████████████| 22406/22406 [00:00<00:00, 147388.99it/s] 
Reading househol.tab: 19169it [00:00, 23990.09it/s]
Writing household.csv file: 100%|███████████████████████████████████████████████████| 19169/19169 [00:00<00:00, 147383.17it/s]

Importing FRS data

Importing entity-level datasets as DataFrames can be done with:

import frs
person_df, benunit_df, household_df = frs.load()

Note that frs.load() will raise an exception if the data has not been generated.