Country Profiles¶

This tutorial covers the three country-profile functions:

Function	Description
`get_cp()`	Download a full country profile dataset
`get_cp_ki()`	Country profile key indicators (metadata-rich)
`unnest_ki()`	Flatten nested key-indicator rows into one row per indicator

`get_cp()` — Country profile download¶

get_cp() downloads the full country profile dataset: a comprehensive set of poverty and inequality estimates computed at multiple poverty lines for one or more countries.

Basic usage¶

import povineq

# Single country
df = povineq.get_cp(country="AGO")

# Multiple countries
df = povineq.get_cp(country=["IDN", "IND"])

# All countries
df = povineq.get_cp()

Custom poverty line¶

The default poverty line is $2.15/day (2017 PPP). Supply your own:

df = povineq.get_cp(country="BRA", povline=3.65)

PPP year¶

Switch between PPP base years:

# 2011 PPP
df = povineq.get_cp(country="AGO", ppp_version=2011)

# 2017 PPP (default)
df = povineq.get_cp(country="AGO", ppp_version=2017)

Polars output¶

df = povineq.get_cp(country="IDN", dataframe_type="polars")

`get_cp_ki()` — Key indicators¶

get_cp_ki() returns a condensed set of key indicators per country. Each row contains a nested structure with indicator values across multiple years.

import povineq

df = povineq.get_cp_ki(country="IDN")
print(df.columns.tolist())

Unnesting key indicators¶

The nested columns can be hard to work with directly. Use unnest_ki() to flatten the result into one row per country-indicator:

import povineq

df_ki = povineq.get_cp_ki(country="IDN")
df_flat = povineq.unnest_ki(df_ki)
print(df_flat.head())

Multiple countries:

df_ki = povineq.get_cp_ki(country=["IDN", "IND", "BRA"])
df_flat = povineq.unnest_ki(df_ki)

API Reference¶

See povineq.country_profiles for the full parameter documentation.