WBData - World Bank Data Client

Warnings

• breaking The 1.0.0 release introduced significant architectural changes, renaming many functions and arguments for consistency and clarity. Code written for pre-1.0.0 versions will likely break.
  Fix: Consult the v1.0.0 documentation or examples for updated function and argument names (e.g., `get_dataframe` replaces older data retrieval methods). The overall data retrieval pattern has shifted.
• breaking Version 1.1.0 dropped support for older, End-of-Life Python versions, now requiring Python >=3.10. Additionally, version 0.3.0 previously dropped support for Python <= 3.5.
  Fix: Upgrade your Python environment to 3.10 or newer to use `wbdata` 1.1.0+. Ensure your development environment meets the `requires_python` specification.
• gotcha Prior to v1.0.0, date arguments exclusively required `datetime.datetime` objects. Since v1.0.0, string representations for years, months, or quarters are also accepted, which can lead to different interpretations of date ranges.
  Fix: For older versions, ensure `datetime.datetime` objects are consistently used. For v1.0.0+, while strings offer flexibility, use `datetime.datetime` objects for precise control over start and end dates in date ranges to avoid ambiguity.
• breaking The `display` argument in search functions (e.g., `search_indicator`, `search_country`) was removed in v0.3.0. These functions now return lists that pretty-print as tables when directly expressed in a shell or notebook.
  Fix: Remove the `display` argument from calls to search functions. Handle the returned list directly; its `__repr__` method provides the tabular display in interactive environments.

Install

• pip install wbdata Install latest version

Imports

• wbdata

  import wbdata

  The primary way to use the library; all main functions are methods of the `wbdata` module (e.g., `wbdata.get_dataframe`).

Quickstart

Demonstrates how to fetch time-series data for multiple indicators and countries, retrieve metadata about indicators, and perform searches for available indicators. Data is typically returned as a Pandas DataFrame for easy manipulation.

import wbdata
import datetime

# Define the date range from 2000 to 2010
data_date = datetime.datetime(2000, 1, 1), datetime.datetime(2010, 1, 1)

# Define countries using their ISO2 codes
countries = ["FR", "DE", "IT"]

# Define indicators with human-readable aliases
indicators = {"NY.GDP.PCAP.CD": "GDP per Capita", "SP.POP.TOTL": "Total Population"}

# Get data as a Pandas DataFrame
df = wbdata.get_dataframe(indicators, country=countries, data_date=data_date)
print("\n--- Sample Data (GDP per Capita, Total Population) ---")
print(df.head())

# Get information about a specific indicator
print("\n--- Indicator Info for GDP per Capita ---")
indicators_info = wbdata.get_indicator("NY.GDP.PCAP.CD")
for key, value in indicators_info.items():
    print(f"{key}: {value}")

# Search for indicators containing 'population'
print("\n--- Search Results for 'population' ---")
population_indicators = wbdata.search_indicator("population")
# In an interactive shell or notebook, printing the list directly would show a table
print(f"Found {len(population_indicators)} indicators matching 'population'. Showing first 3:\n{population_indicators[:3]}")