``` python import polars as pl from great_tables import GT, html import gt_extras as gte pre_tax_col = "gini_market__age_total" post_tax_col = "gini_disposable__age_total" # Read the data df = pl.read_csv( "income_inequality_raw.csv", schema={ "Entity": pl.String, "Code": pl.String, "Year": pl.Int64, post_tax_col: pl.Float64, pre_tax_col: pl.Float64, "population_historical": pl.Int64, "owid_region": pl.String, }, null_values=["NA", ""], ) # Propagate the region field to all rows of that country df = ( df.sort("Entity") .group_by("Entity", maintain_order=True) .agg( [ pl.col("Code"), pl.col("Year"), pl.col(post_tax_col), pl.col(pre_tax_col), pl.col("population_historical"), pl.col("owid_region").fill_null(strategy="backward"), ] ) .explode( [ "Code", "Year", post_tax_col, pre_tax_col, "population_historical", "owid_region", ] ) ) # Drop rows where there is a null in either pre-tax or post-tax cols df = df.drop_nulls( subset=( pl.col(post_tax_col), pl.col(pre_tax_col), ) ) # Compute the percent reduction in gini coefficient. df = df.with_columns( ((pl.col(pre_tax_col) - pl.col(post_tax_col)) / pl.col(pre_tax_col) * 100) .round(2) .alias("gini_pct_change") ) # Calculate 5-year benchmark (mean) of percent change for each country df = df.with_columns( pl.col("gini_pct_change") .rolling_mean(window_size=5) .over(pl.col("Entity")) .alias("gini_pct_benchmark_5yr") ) # Select rows with large population in the year 2020, sorted by coefficient post-tax df = ( df.filter(pl.col("population_historical").gt(40000000)) .filter(pl.col("Year").eq(2020)) .sort(by=pl.col(post_tax_col)) ) # Scale population df = df.with_columns((pl.col("population_historical").log10()).alias("pop_log")) pop_min = df["pop_log"].min() / 1 pop_max = df["pop_log"].max() # Set up gt-extras icons, scaling population to 1-10 range df = df.with_columns( ((pl.col("pop_log") - pop_min) / (pop_max - pop_min) * 10 + 1) .round(0) .cast(pl.Int64) .alias("pop_icons") ) # Format original population value with commas df = df.with_columns( pl.col("population_historical").map_elements( lambda x: f"{int(x):,}" if x is not None else None, return_dtype=pl.String ) ) # Apply gte.fa_icon_repeat to each entry in the pop_icons column df_with_icons = df.with_columns( pl.col("pop_icons").map_elements( lambda x: gte.fa_icon_repeat(name="person", repeats=int(x)), return_dtype=pl.String, ) ) ``` ``` python # Generate the table, before gt-extras add-ons gt = ( GT(df_with_icons, rowname_col="Entity", groupname_col="owid_region") .tab_header( "Income Inequality Before and After Taxes in 2020", "As measured by the Gini coefficient, where 0 is best and 1 is worst", ) .cols_move("pop_icons", after=pre_tax_col) .cols_align("left") .cols_hide(["Year", "pop_log", "population_historical"]) .fmt_flag("Code") .cols_label( { "Code": "", "gini_pct_change": "Improvement Post Taxes", "pop_icons": "Population", } ) .tab_source_note( html( """

Source: Data from [#TidyTuesday](https://github.com/rfordatascience/tidytuesday) (2025-08-05).

Dumbbell plot: Blue: post-tax Gini coefficient Gold: pre-tax Gini coefficient

Bullet plot: Percent reduction in Gini after taxes for each country, compared to its 5-year average benchmark.

""" ) ) ) # Apply the gt-extras functions via pipe ( gt.pipe( gte.gt_plt_dumbbell, col1=pre_tax_col, col2=post_tax_col, col1_color="#e0b165", col2_color="#106ea0", dot_border_color="transparent", num_decimals=2, width=240, label="Pre-tax to Post-tax Coefficient", ) .pipe( gte.gt_plt_bullet, "gini_pct_change", "gini_pct_benchmark_5yr", fill="#963d4c", target_color="#3D3D3D", bar_height=15, width=200, ) .pipe( gte.gt_merge_stack, col1="pop_icons", col2="population_historical", ) .pipe(gte.gt_theme_guardian) ) ```

Income Inequality Before and After Taxes in 2020
As measured by the Gini coefficient, where 0 is best and 1 is worst
	Pre-tax to Post-tax Coefficient	Population	Improvement Post Taxes
Europe
France		65,905,226
Germany		83,628,661
Spain		47,679,437
Italy		59,912,714
United Kingdom		67,351,806
Asia
South Korea		51,858,440
Turkey		86,091,644
North America
United States		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" class="fa" /> 339,436,106
Mexico		126,798,998
South America
Brazil		208,660,785
Source: Data from [#TidyTuesday](https://github.com/rfordatascience/tidytuesday) (2025-08-05). Dumbbell plot: Blue: post-tax Gini coefficient Gold: pre-tax Gini coefficient Bullet plot: Percent reduction in Gini after taxes for each country, compared to its 5-year average benchmark.