fcall

Python package for parsing Farm Credit Administration ("FCA") Call Report data into tidy Polars DataFrames.

There is also a related R package {fcall} at https://github.com/ketchbrookanalytics/fcall.

Installation

fcall can be installed from PyPI:

# using pip
pip install fcall

# using uv
uv add fcall

Alternatively, install the development version directly from GitHub:

# using pip
pip install git+https://github.com/ketchbrookanalytics/fcall-py.git

# using uv
uv add git+https://github.com/ketchbrookanalytics/fcall-py.git

Background

FCA publishes Call Report data on a quarterly basis at https://www.fca.gov/bank-oversight/call-report-data-for-download. Ketchbrook Analytics replicates these files in a public AWS S3 bucket, which fcall interacts with via its download_data() function.

As of March 2026, this data represents a set of 72 .TXT files. These files represent 36 datasets. The files prefixed with "D_" contain metadata (the column names, data types, etc.) of the associated file containing the raw, header-less comma-separated data. For example, the file that starts with "D_INST" contains the metadata for the file that starts with "INST_".

Further, some of these datasets are structured in a way that makes data analysis difficult. In these cases, we chose to pivot the data to make it more analysis-friendly.

This package provides 3 utility functions:

1. download_data() allows users to programmatically download (and unzip) data from a specific quarter
2. process_data() parses the data from these unzipped .TXT files into a dict of Polars DataFrames containing the Call Report data and file metadata
3. compare_metadata() compares two sets of Call Report data from different quarters

Usage

import fcall

# Download & unzip a quarter into a directory
# (returns True on success; prints a message and returns False on failure)
if fcall.download_data(
    year=2025,
    month="September",
    dest="./fcadata",
):
    # Parse the .TXT files into tidy Polars DataFrames + metadata
    result = fcall.process_data("./fcadata")
    result["data"]["RCB"]      # a polars.DataFrame
    result["metadata"]["RCB"]  # parsed schema for RCB

# Compare metadata between two quarters
fcall.download_data(
    year=2022,
    month="September",
    dest="./fcadata2",
)
fcall.compare_metadata(
    dir1="./fcadata",
    dir2="./fcadata2",
)

Database

Ketchbrook Analytics has also created a PostgreSQL database to store historical FCA Call Report data in a traditional, relational schema that aligns with the output DataFrame structure resulting from running process_data(). This database allows users to execute SQL queries to easily analyze Call Report data across multiple quarters.

Please reach out to info@ketchbrookanalytics.com if you would like access to this database.