pybrsa 0.1.5

Python package for fetching data from BDDK

pybrsa: A Python Package for Turkish Banking Sector Data

A Python package for programmatic access to Turkish banking sector data from the Turkish Banking Regulation and Supervision Agency (BRSA, known as BDDK in Turkish). The package provides Python users with a clean interface to fetch monthly and quarterly banking statistics, financial reports, and sectoral indicators directly from BRSA's official APIs. Specifically, the package retrieves tables from two distinct publication portals maintained by the BRSA:

• The Monthly Bulletin Portal{.uri}
• The FinTurk Data System{.uri}

Key Features

• Direct API access to BRSA monthly bulletins (17 financial tables)
• Quarterly FinTurk data with city-level granularity (7 tables, 82 cities including 'HEPSI' for all cities)
• Consistent parameter interface for both data sources
• Built-in metadata for tables, banking groups, and provinces
• Multiple export formats: CSV, Excel via save_data()
• Returns pandas DataFrames ready for analysis

Design Philosophy

Lightweight and Authentic: Other packages providing access to BDDK data also fetch data programmatically, but they add a heavy translation layer - maintaining manual configuration files to map Turkish column names and categorical values to English. This provides user convenience at a high maintenance cost.

pybrsa takes a different path. It interacts directly with the API and uses the data it returns with minimal alteration:

• For the Monthly Bulletin, it uses the official English column names and labels provided by the API when lang = "en" is set.
• For the FinTurk dataset, where the API provides data only in Turkish, it returns the authentic Turkish names.

This is a deliberate choice. By avoiding a separate translation file, pybrsa eliminates a major maintenance burden, aiming to adapt instantly to any API changes. This way, the data you see is exactly what the official source provides.

Related Packages

• bddk{.uri} (Python): Uses manual configuration for translations and column mappings and provides access only to the Monthly Bulletin.
• bddkdata{.uri} (Python): Provides similar functionality for Python/pandas users with the same constraints as bddk.
• rbrsa{.uri} R companion to this package with consistent API.
• bddkR{.uri} (R): Uses manual configuration for translations and column mappings and provides access only to the Monthly Bulletin. It is based on 'bddkdata'

Installation

Install from PyPI:

pip install pybrsa

The development version can be installed from GitHub:

pip install git+https://github.com/obakis/pybrsa.git

Getting started

A vignette demonstrating how to use main functions, download and save data from both BDDK and FinTurk interface can be found at: https://obakis.github.io/pybrsa/articles/introduction.html

The pybrsa package retrieves tables from two distinct publication portalsmaintained by the Turkish Banking Regulation and Supervision Agency (BDDK). Both portals are official sources, but they organize the data differently:

• The Monthly Bulletin Portal provides high-level, summary reports designed for general consumption and quick overviews of monthly trends without any geographic coverage.
• The FinTurk Data System provides granular, detailed data, including statistics broken down by province, whereas the standard Monthly Bulletin offers national-level aggregates.

## R users: to use list_tables() as in R you can do
# from pybrsa import list_tables, fetch_bddk
## or importing everything
# from pybrsa import * 

import pybrsa

# Explore available tables
pybrsa.list_tables("bddk")  # For English names
pybrsa.list_tables("bddk", "tr")  # For Turkish names
pybrsa.list_tables("finturk")

# Explore banking groups
pybrsa.list_groups("bddk")
pybrsa.list_groups("bddk", "tr")
pybrsa.list_groups("finturk")

# Fetch monthly data (Table 15: Ratios)
data = pybrsa.fetch_bddk(
    start_year=2024, start_month=1,
    end_year=2024, end_month=3,
    table_no=15, grup_kod=10001
)

# Fetch quarterly FinTurk data
q_data = pybrsa.fetch_finturk(
    start_year=2024, start_quarter=3,
    end_year=2024, end_quarter=9,
    table_no=1, grup_kod=10007
)

# Save results to CSV
# q_data.to_csv("my_file.csv", index=False)
temp_path = pybrsa.tempfile_base()
pybrsa.save_data(q_data, "temp_path", format="csv")