Examples With Code

Mega Examples

Cars

Car	year	hp	trq
Car Performance Review
Using gt-extras functionality
California	2015	553.0	557.0
GTC4Lusso	2017	680.0	514.0
FF	2015	652.0	504.0
F12Berlinetta	2015	731.0	509.0
LaFerrari	2015	949.0	664.0
NSX	2017	573.0	476.0
GT-R	2016	545.0	436.0
Aventador	2015	700.0	507.0
Huracan	2015	610.0	413.0
Gallardo	2014	550.0	398.0
Continental GT	2016	500.0	487.0
Granturismo	2016	454.0	384.0
Quattroporte	2016	404.0	406.0
Ghibli	2016	345.0	369.0
6-Series	2016	315.0	330.0
i8	2016	357.0	420.0

from great_tables import GT
from great_tables.data import gtcars
import gt_extras as gte

# Setup dataframe
gtcars_mini = gtcars.iloc[5:21].copy().reset_index(drop=True)
gtcars_mini["efficiency"] = gtcars_mini["mpg_c"] / gtcars_mini["hp"] * 100

(
    # Start with a standard GT
    GT(gtcars_mini, rowname_col="model")
    .tab_stubhead(label="Car")
    .cols_hide(["drivetrain", "hp_rpm", "trq_rpm", "trim", "bdy_style", "msrp", "trsmn", "ctry_origin"])
    .cols_align("center")
    .tab_header(title="Car Performance Review", subtitle="Using gt-extras functionality")

    # Add gt-extras features using gt.pipe()
    .pipe(gte.gt_color_box, columns=["hp", "trq"], palette=["lightblue", "darkblue"])
    .pipe(gte.gt_plt_dot, category_col="mfr", data_col="efficiency", domain=[0, 0])
    .pipe(gte.gt_plt_bar, columns=["mpg_c", "mpg_h"])
    .pipe(gte.gt_fa_rating, columns="efficiency")
    .pipe(gte.gt_hulk_col_numeric, columns="year", palette="viridis")
    .pipe(gte.gt_theme_538)
)

NFL 2011 Season

2011 NFL Season at a Glance

Super Bowl XLVI: New York Giants def. New England Patriots

Team	Record	Scoring		Season Trends
Team	Record	Points For vs Points Against	Point Diff	Games	Streak
AFC East
NE	14-2-0		11.9		7
MIA	10-6-0		-1.1		-1
BUF	7-9-0		1.3		-2
NYJ	5-11-0		-8.4		1
AFC North
PIT	11-5-0		4.5		7
BAL	8-8-0		1.4		-2
CIN	6-9-1		0.6		1
CLE	1-15-0		-11.8		-1
AFC South
TEN	9-7-0		0.2		1
HOU	9-7-0		-3.1		-1
IND	8-8-0		1.2		1
JAX	3-13-0		-5.1		-1
AFC West
KC	12-4-0		4.9		2
OAK	12-4-0		1.9		-1
DEN	9-7-0		2.2		1
SD	5-11-0		-0.8		-5
Source: Lee Sharpe, nflverse

Team	Record	Scoring		Season Trends
Team	Record	Points For vs Points Against	Point Diff	Games	Streak
NFC East
DAL	13-3-0		7.2		-1
NYG	11-5-0		1.6		1
WAS	8-7-1		0.8		-1
PHI	7-9-0		2.2		2
NFC North
GB	10-6-0		2.8		6
DET	9-7-0		-0.8		-3
MIN	8-8-0		1.2		1
CHI	3-13-0		-7.5		-4
NFC South
ATL	11-5-0		8.4		4
TB	9-7-0		-0.9		1
NO	7-9-0		0.9		-1
CAR	6-10-0		-2.1		-2
NFC West
SEA	10-5-1		3.9		1
ARI	7-8-1		3.5		2
LA	4-12-0		-10.6		-7
SF	2-14-0		-10.7		-1
Source: Lee Sharpe, nflverse

import polars as pl
from great_tables import GT, md
import gt_extras as gte

nfl_df = pl.read_json("./nfl-season/nfl_2011_stats.json")

def make_gt(df) -> GT:
    return (
        GT(df, rowname_col="Team", groupname_col="team_division")
        .tab_header(
            title="2011 NFL Season at a Glance",
            subtitle="Super Bowl XLVI: New York Giants def. New England Patriots",
        )
        .tab_source_note(
            md(
                '<span style="float: right;">Source: [Lee Sharpe, nflverse](https://github.com/nflverse/nfldata)</span>'
            )
        )
        .tab_stubhead(label="Team")
        .tab_spanner(label="Scoring", columns=["Avg PF", "Avg PA", "Point Diff"])
        .tab_spanner(label="Season Trends", columns=["Games", "Streak"])
        .fmt_number(columns="Point Diff", decimals=1)
        .fmt_image("team_logo_espn")
        .cols_hide(["Wins", "team_conf", "team_name"])
        .cols_align(align="center", columns=["Avg PF", "Avg PA", "Games"])
        .cols_move("team_logo_espn", after="Team")
        .cols_label({"team_logo_espn": ""})
        .pipe(
            gte.gt_plt_winloss,
            column="Games",
            win_color="blue",
            loss_color="orange",
            tie_color="gray",
            height=40,
            width=120,
        )
        .pipe(
            gte.gt_fa_rank_change,
            column="Streak",
            icon_type="turn",
            color_up="blue",
            color_down="orange",
            size=14,
        )
        .pipe(
            gte.gt_plt_dumbbell,
            col1="Avg PF",
            col2="Avg PA",
            col1_color="green",
            col2_color="red",
            width=300,
            height=40,
            num_decimals=0,
            label="Points For vs Points Against",
            font_size=12,
        )
        .pipe(
            gte.gt_color_box,
            columns="Point Diff",
            palette=["green", "yellow", "red"],
            domain=[14, -14]
        )
        .pipe(gte.gt_highlight_cols, columns="Team", fill="lightgray")
        .pipe(gte.gt_highlight_rows, rows="NYG", fill="gold", alpha=0.3)
        .pipe(gte.gt_highlight_rows, rows="NE", fill="silver", alpha=0.3)
        .pipe(gte.gt_add_divider, columns="Point Diff", color="darkblue", weight=3)
        .pipe(gte.gt_theme_538)
    )


gt1 = make_gt(nfl_df.head(16))
gt2 = make_gt(nfl_df.slice(16, 16))

gte.gt_two_column_layout(gt1, gt2, table_header_from=1)

Gini Coefficient

Setup

import polars as pl

pre_tax_col = "gini_market__age_total"
post_tax_col = "gini_disposable__age_total"

# Read the data
df = pl.read_csv(
    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-05/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", ""],
)

# Propogate 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"),
            # Most important action happens here
            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 = (
    # Choose a smaller pop to include more countries
    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
    )
)

		Pre-tax to Post-tax Coefficient	Population	Improvement Post Taxes
Income Inequality Before and After Taxes in 2020
As measured by the Gini coefficient, where 0 is best and 1 is worst
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			339,436,106
Mexico			126,798,998
South America
Brazil			208,660,785
Source: Data from #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.

from great_tables import GT, html
import gt_extras as gte

# 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,
    )
)

# 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(
            """
            <div>
            <strong>Source:</strong> Data from <a href="https://github.com/rfordatascience/tidytuesday">#TidyTuesday</a> (2025-08-05).<br>
                <div>
                <strong>Dumbbell plot:</strong>
                <span style="color:#106ea0;">Blue:</span> post-tax Gini coefficient
                <span style="color:#e0b165;">Gold:</span> pre-tax Gini coefficient
                <br>
                </div>
            <strong>Bullet plot:</strong> Percent reduction in Gini after taxes for each country, compared to its 5-year average benchmark.
            </div>
            """
        )
    )
)

# 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)
)

Plotting

gt_plt_bar

Car	mfr	year
LaFerrari	Ferrari	2015
NSX	Acura	2017
GT-R	Nissan	2016
Aventador	Lamborghini	2015
Huracan	Lamborghini	2015
Gallardo	Lamborghini	2014
Continental GT	Bentley	2016
Granturismo	Maserati	2016
Quattroporte	Maserati	2016

from great_tables import GT
from great_tables.data import gtcars
import gt_extras as gte

gtcars_mini = gtcars.loc[
    9:17,
    ["model", "mfr", "year", "hp", "hp_rpm", "trq", "trq_rpm", "mpg_c", "mpg_h"]
]

gt = (
    GT(gtcars_mini, rowname_col="model")
    .tab_stubhead(label="Car")
    .cols_align("center")
    .cols_align("left", columns="mfr")
)

gt.pipe(
    gte.gt_plt_bar,
    columns= ["hp", "hp_rpm", "trq", "trq_rpm", "mpg_c", "mpg_h"]
)

gt_plt_bar_pct

x	autoscale_on	autoscale_off
10
20
30
40

import polars as pl
from great_tables import GT
import gt_extras as gte

df = pl.DataFrame({"x": [10, 20, 30, 40]}).with_columns(
    pl.col("x").alias("autoscale_on"),
    pl.col("x").alias("autoscale_off"),
)

gt = GT(df)

(
    gt.pipe(
        gte.gt_plt_bar_pct,
        column=["autoscale_on"],
        autoscale=True,
        labels=True,
        fill="green",
    ).pipe(
        gte.gt_plt_bar_pct,
        column=["autoscale_off"],
        autoscale=False,
        labels=True,
    )
)

gt_plt_bar_stack

x	Group 1 \| Group 2 \| Group 3
Example A
Example B
Example C

import pandas as pd
from great_tables import GT
import gt_extras as gte

df = pd.DataFrame({
    "x": ["Example A", "Example B", "Example C"],
    "col": [
        [10, 40, 50],
        [30, 30, 40],
        [50, 20, 30],
    ],
})

gt = GT(df)

gt.pipe(
    gte.gt_plt_bar_stack,
    column="col",
    palette=["red", "grey", "black"],
    labels=["Group 1", "Group 2", "Group 3"],
    width=200,
)

gt_plt_bullet

	Temp plot	Temp
Daily Temp vs Monthly Average
5/1		67
5/2		72
6/1		78
6/2		74
7/1		84
7/2		85
8/1		81
8/2		81
9/1		91
9/2		92
Target line shows monthly average temperature

import polars as pl
from great_tables import GT
from great_tables.data import airquality
import gt_extras as gte

air_bullet = (
    pl.from_pandas(airquality)
    .with_columns(pl.col("Temp").mean().over("Month").alias("target_temp"))
    .group_by("Month", maintain_order=True)
    .head(2)
    .with_columns(
        (pl.col("Month").cast(pl.Utf8) + "/" + pl.col("Day").cast(pl.Utf8)).alias(
            "Date"
        )
    )
    .select(["Date", "Temp", "target_temp"])
    .with_columns(pl.col(["Temp", "target_temp"]).round(1))
)

(
    GT(air_bullet, rowname_col="Date")
    .tab_header(title="Daily Temp vs Monthly Average")
    .tab_source_note("Target line shows monthly average temperature")

    ## Call gt_plt_bullet
    .pipe(
        gte.gt_plt_bullet,
        data_column="Temp",
        target_column="target_temp",
        width=200,
        fill="tomato",
        target_color="darkblue",
        keep_data_column=True,
    )
    .cols_move_to_end("Temp")
    .cols_align("left", "Temp plot")
)

gt_plt_conf_int

group	mean	ci_lower	ci_upper
A	5.2	3.1	7.3
B	7.8	6.1	9.7
C	3.4	1.8	5.0

import pandas as pd
from great_tables import GT
import gt_extras as gte

df = pd.DataFrame({
    'group': ['A', 'B', 'C'],
    'mean': [5.2, 7.8, 3.4],
    'ci_lower': [3.1, 6.1, 1.8],
    'ci_upper': [7.3, 9.7, 5.0],
    'ci': [5.2, 7.8, 3.4],
})

gt = GT(df)
gt.pipe(
    gte.gt_plt_conf_int,
    column='ci',
    ci_columns=['ci_lower', 'ci_upper'],
    width=120,
)

gt_plt_dot

Car	mfr	hp
F12Berlinetta		731.0
LaFerrari		949.0
NSX		573.0
GT-R		545.0
Aventador		700.0
Huracan		610.0
Gallardo		550.0
Continental GT		500.0
Granturismo		454.0
Quattroporte		404.0
Ghibli		345.0
6-Series		315.0
i8		357.0

from great_tables import GT
from great_tables.data import gtcars
import gt_extras as gte

gtcars_mini = gtcars.loc[8:20, ["model", "mfr", "hp"]]

gt = (
    GT(gtcars_mini, rowname_col="model")
    .tab_stubhead(label="Car")
)

gt.pipe(gte.gt_plt_dot, category_col="mfr", data_col="hp")

gt_plt_dumbbell

date	Open to Close ($)
2008-12-03
2008-12-04
2008-12-05
2008-12-08
2008-12-09
2008-12-10
2008-12-11
2008-12-12
2008-12-15
Purple: Open Green: Close

import pandas as pd
from great_tables import GT, html, style, loc
from great_tables.data import sp500
import gt_extras as gte

# Trim the data to December 2008
df = sp500[["date", "open", "close"]].copy()
df["date"] = pd.to_datetime(df["date"], errors='coerce')

dec_2008 = df[
    (df["date"].dt.month == 12) &
    (df["date"].dt.year == 2008)
]
dec_2008 = dec_2008.iloc[::-1].iloc[2:11]

# Make the Great Table
gt = (
    GT(dec_2008)
    .tab_source_note(html("Purple: Open<br>Green: Close"))
    .tab_style(
        style=style.text(align="right"),
        locations=[loc.source_notes()]
    )
)

gt.pipe(
    gte.gt_plt_dumbbell,
    col1='open',
    col2='close',
    label = "Open to Close ($)",
    num_decimals=0,
    width=300,
    height=60,
)

gt_plt_summary

Type	Column	Missing	Mean	Median	SD
Summary Table
45 rows x 5 cols
	Date	0.0%	—	—	—
	Value	11.1%	22.50	21.00	8.83
	Category	22.2%	—	—	—
	Boolean	0.0%	0.67	—	—
	Status	33.3%	—	—	—

import polars as pl
from great_tables import GT
import gt_extras as gte
from datetime import datetime

df = pl.DataFrame({
    "Date": [
        datetime(2024, 1, 1),
        datetime(2024, 1, 2),
        datetime(2024, 1, 7),
        datetime(2024, 1, 8),
        datetime(2024, 1, 13),
        datetime(2024, 1, 16),
        datetime(2024, 1, 20),
        datetime(2024, 1, 22),
        datetime(2024, 2, 1),
    ] * 5,
    "Value": [10, 15, 20, None, 25, 18, 22, 30, 40] * 5,
    "Category": ["A", "B", "C", "A", "B", "C", "D", None, None] * 5,
    "Boolean": [True, False, True] * 15,
    "Status": ["Active", "Inactive", None] * 15,
})

gte.gt_plt_summary(df)

gt_plt_winloss

Team	10 Games
Liverpool
Chelsea
Man City

from great_tables import GT, md
import gt_extras as gte
import pandas as pd

df = pd.DataFrame(
    {
        "Team": ["Liverpool", "Chelsea", "Man City"],
        "10 Games": [
            [1, 1, 0, 1, 0.5, 1, 0, 1, 1, 0],
            [0, 0, 1, 0, 1, 1, 1, 0, 1, 1],
            [0.5, 1, 0.5, 0, 1, 0, 1, 0.5, 1, 0],
        ],
    }
)

gt = GT(df)

gt.pipe(
    gte.gt_plt_winloss,
    column="10 Games",
    win_color="green",
)

Coloring

gt_color_box

Island	size
Asia	16988
Africa	11506
North America	9390
South America	6795
Antarctica	5500
Europe	3745
Australia	2968
Greenland	840
New Guinea	306
Borneo	280

from great_tables import GT
from great_tables.data import islands
import gt_extras as gte

islands_mini = (
    islands
    .sort_values(by="size", ascending=False)
    .head(10)
)

gt = (
    GT(islands_mini, rowname_col="name")
    .tab_stubhead(label="Island")
)

gt.pipe(gte.gt_color_box, columns="size", palette=["lightblue", "navy"])

gt_data_color_by_group

	Color by Group	Color All
grp_a
row_1	0.1111	0.1111
row_2	2.222	2.222
row_3	33.33	33.33
row_4	444.4	444.4
grp_b
row_5	5550.0	5550.0
row_6
row_7	777000.0	777000.0
row_8	8880000.0	8880000.0
Left: `gt_data_color_by_group`, Right: `data_color`

from great_tables import GT, md
from great_tables.data import exibble
import gt_extras as gte

gt = (
    GT(exibble, rowname_col="row", groupname_col="group")
    .cols_hide(columns=None)
    .cols_unhide("num")
    .cols_label({"num": "Color by Group"})
    .pipe(gte.gt_duplicate_column, column="num", dupe_name="Color All")
    .tab_source_note(md("Left: `gt_data_color_by_group`, Right: `data_color`"))
)

(
    gt
    .data_color(columns="Color All", palette="PiYG")
    .pipe(gte.gt_data_color_by_group, columns=["num"], palette="PiYG")
)

gt_highlight_cols

Car	year	hp	trq	mpg_h
GT	2017	647.0	550.0	18.0
458 Speciale	2015	597.0	398.0	17.0
458 Spider	2015	562.0	398.0	17.0
458 Italia	2014	562.0	398.0	17.0
488 GTB	2016	661.0	561.0	22.0
California	2015	553.0	557.0	23.0
GTC4Lusso	2017	680.0	514.0	17.0
FF	2015	652.0	504.0	16.0

from great_tables import GT, md
from great_tables.data import gtcars
import gt_extras as gte

gtcars_mini = gtcars[["model", "year", "hp", "trq", "mpg_h"]].head(8)

gt = (
    GT(gtcars_mini, rowname_col="model")
    .tab_stubhead(label=md("*Car*"))
)

(
    gt
    .pipe(gte.gt_highlight_cols, columns="hp")
    .pipe(gte.gt_highlight_cols, columns="mpg_h", fill="darkorchid")
)

gt_highlight_rows

Car	year	hp	trq
GT	2017	647.0	550.0
458 Speciale	2015	597.0	398.0
458 Spider	2015	562.0	398.0
458 Italia	2014	562.0	398.0
488 GTB	2016	661.0	561.0
California	2015	553.0	557.0
GTC4Lusso	2017	680.0	514.0
FF	2015	652.0	504.0

from great_tables import GT, md
from great_tables.data import gtcars
import gt_extras as gte

gtcars_mini = gtcars[["model", "year", "hp", "trq"]].head(8)

gt = (
    GT(gtcars_mini, rowname_col="model")
    .tab_stubhead(label=md("*Car*"))
)

(
    gt
    .pipe(gte.gt_highlight_rows, rows=2, fill="#007FFF")
    .pipe(gte.gt_highlight_rows, rows=3, fill="chartreuse")
    .pipe(gte.gt_highlight_rows, rows=7, fill="#FF007F")
)

gt_hulk_col_numeric

Car	mfr	year	hp	trq	mpg_h
GT	Ford	2017	647.0	550.0	18.0
458 Speciale	Ferrari	2015	597.0	398.0	17.0
458 Spider	Ferrari	2015	562.0	398.0	17.0
458 Italia	Ferrari	2014	562.0	398.0	17.0
488 GTB	Ferrari	2016	661.0	561.0	22.0
California	Ferrari	2015	553.0	557.0	23.0
GTC4Lusso	Ferrari	2017	680.0	514.0	17.0
FF	Ferrari	2015	652.0	504.0	16.0
F12Berlinetta	Ferrari	2015	731.0	509.0	16.0

from great_tables import GT
from great_tables.data import gtcars
import gt_extras as gte

gtcars_mini = gtcars.loc[0:8, ["model", "mfr", "year", "hp", "trq", "mpg_h"]]

gt = (
    GT(gtcars_mini, rowname_col="model")
    .tab_stubhead(label="Car")
)

gt.pipe(gte.gt_hulk_col_numeric, columns=["hp", "trq", "mpg_h"])

Themes

Setup themes

from great_tables import GT, html
import gt_extras as gte
from great_tables.data import airquality

# Prepare the dataset
airquality_mini = airquality.head(10).assign(Year=1973)

# Create the base table
gt = (
    GT(airquality_mini)
    .tab_header(
        title="Default Theme",
        subtitle="New York Air Quality Measurements",
    )
    .tab_spanner(label="Time", columns=["Year", "Month", "Day"])
    .tab_spanner(
        label="Measurement",
        columns=["Ozone", "Solar_R", "Wind", "Temp"],
    )
    .cols_move_to_start(columns=["Year", "Month", "Day"])
    .cols_label(
        Ozone=html("Ozone,<br>ppbV"),
        Solar_R=html("Solar R.,<br>cal/m<sup>2</sup>"),
        Wind=html("Wind,<br>mph"),
        Temp=html("Temp,<br>&deg;F"),
    )
)

gt

Time			Measurement
Default Theme
New York Air Quality Measurements
Year	Month	Day	Ozone, ppbV	Solar R., cal/m²	Wind, mph	Temp, °F
1973	5	1	41.0	190.0	7.4	67
1973	5	2	36.0	118.0	8.0	72
1973	5	3	12.0	149.0	12.6	74
1973	5	4	18.0	313.0	11.5	62
1973	5	5			14.3	56
1973	5	6	28.0		14.9	66
1973	5	7	23.0	299.0	8.6	65
1973	5	8	19.0	99.0	13.8	59
1973	5	9	8.0	19.0	20.1	61
1973	5	10		194.0	8.6	69

gt_theme_538

Time			Measurement
FiveThirtyEight Theme
Year	Month	Day	Ozone, ppbV	Solar R., cal/m²	Wind, mph	Temp, °F
1973	5	1	41.0	190.0	7.4	67
1973	5	2	36.0	118.0	8.0	72
1973	5	3	12.0	149.0	12.6	74
1973	5	4	18.0	313.0	11.5	62
1973	5	5			14.3	56
1973	5	6	28.0		14.9	66
1973	5	7	23.0	299.0	8.6	65
1973	5	8	19.0	99.0	13.8	59
1973	5	9	8.0	19.0	20.1	61
1973	5	10		194.0	8.6	69

gt = gt.tab_header(title="FiveThirtyEight Theme")

gt.pipe(gte.gt_theme_538)

gt_theme_espn

Time			Measurement
ESPN Theme
Year	Month	Day	Ozone, ppbV	Solar R., cal/m²	Wind, mph	Temp, °F
1973	5	1	41.0	190.0	7.4	67
1973	5	2	36.0	118.0	8.0	72
1973	5	3	12.0	149.0	12.6	74
1973	5	4	18.0	313.0	11.5	62
1973	5	5			14.3	56
1973	5	6	28.0		14.9	66
1973	5	7	23.0	299.0	8.6	65
1973	5	8	19.0	99.0	13.8	59
1973	5	9	8.0	19.0	20.1	61
1973	5	10		194.0	8.6	69

gt = gt.tab_header(title="ESPN Theme")

gt.pipe(gte.gt_theme_espn)

gt_theme_guardian

Time			Measurement
Guardian Theme
Year	Month	Day	Ozone, ppbV	Solar R., cal/m²	Wind, mph	Temp, °F
1973	5	1	41.0	190.0	7.4	67
1973	5	2	36.0	118.0	8.0	72
1973	5	3	12.0	149.0	12.6	74
1973	5	4	18.0	313.0	11.5	62
1973	5	5			14.3	56
1973	5	6	28.0		14.9	66
1973	5	7	23.0	299.0	8.6	65
1973	5	8	19.0	99.0	13.8	59
1973	5	9	8.0	19.0	20.1	61
1973	5	10		194.0	8.6	69

gt = gt.tab_header(title="Guardian Theme")

gt.pipe(gte.gt_theme_guardian)

gt_theme_nytimes

Time			Measurement
NYTimes Theme
Year	Month	Day	Ozone, ppbV	Solar R., cal/m²	Wind, mph	Temp, °F
1973	5	1	41.0	190.0	7.4	67
1973	5	2	36.0	118.0	8.0	72
1973	5	3	12.0	149.0	12.6	74
1973	5	4	18.0	313.0	11.5	62
1973	5	5			14.3	56
1973	5	6	28.0		14.9	66
1973	5	7	23.0	299.0	8.6	65
1973	5	8	19.0	99.0	13.8	59
1973	5	9	8.0	19.0	20.1	61
1973	5	10		194.0	8.6	69

gt = gt.tab_header(title="NYTimes Theme")

gt.pipe(gte.gt_theme_nytimes)

gt_theme_excel

Time			Measurement
Excel Theme
Year	Month	Day	Ozone, ppbV	Solar R., cal/m²	Wind, mph	Temp, °F
1973	5	1	41.0	190.0	7.4	67
1973	5	2	36.0	118.0	8.0	72
1973	5	3	12.0	149.0	12.6	74
1973	5	4	18.0	313.0	11.5	62
1973	5	5			14.3	56
1973	5	6	28.0		14.9	66
1973	5	7	23.0	299.0	8.6	65
1973	5	8	19.0	99.0	13.8	59
1973	5	9	8.0	19.0	20.1	61
1973	5	10		194.0	8.6	69

gt = gt.tab_header(title="Excel Theme")

gt.pipe(gte.gt_theme_excel)

gt_theme_dot_matrix

Time			Measurement
Dot Matrix Theme
Year	Month	Day	Ozone, ppbV	Solar R., cal/m²	Wind, mph	Temp, °F
1973	5	1	41.0	190.0	7.4	67
1973	5	2	36.0	118.0	8.0	72
1973	5	3	12.0	149.0	12.6	74
1973	5	4	18.0	313.0	11.5	62
1973	5	5			14.3	56
1973	5	6	28.0		14.9	66
1973	5	7	23.0	299.0	8.6	65
1973	5	8	19.0	99.0	13.8	59
1973	5	9	8.0	19.0	20.1	61
1973	5	10		194.0	8.6	69

gt = gt.tab_header(title="Dot Matrix Theme")

gt.pipe(gte.gt_theme_dot_matrix)

gt_theme_dark

Time			Measurement
Dark Theme
Year	Month	Day	Ozone, ppbV	Solar R., cal/m²	Wind, mph	Temp, °F
1973	5	1	41.0	190.0	7.4	67
1973	5	2	36.0	118.0	8.0	72
1973	5	3	12.0	149.0	12.6	74
1973	5	4	18.0	313.0	11.5	62
1973	5	5			14.3	56
1973	5	6	28.0		14.9	66
1973	5	7	23.0	299.0	8.6	65
1973	5	8	19.0	99.0	13.8	59
1973	5	9	8.0	19.0	20.1	61
1973	5	10		194.0	8.6	69

gt = gt.tab_header(title="Dark Theme")

gt.pipe(gte.gt_theme_dark)

gt_theme_pff

Time			Measurement
PFF Theme
Year	Month	Day	Ozone, ppbV	Solar R., cal/m²	Wind, mph	Temp, °F
1973	5	1	41.0	190.0	7.4	67
1973	5	2	36.0	118.0	8.0	72
1973	5	3	12.0	149.0	12.6	74
1973	5	4	18.0	313.0	11.5	62
1973	5	5			14.3	56
1973	5	6	28.0		14.9	66
1973	5	7	23.0	299.0	8.6	65
1973	5	8	19.0	99.0	13.8	59
1973	5	9	8.0	19.0	20.1	61
1973	5	10		194.0	8.6	69

gt = gt.tab_header(title="PFF Theme")

gt.pipe(gte.gt_theme_pff)

Icons and Images

add_text_img

	Points	Assists
Josh Hart	1051	453
Jalen Brunson	1690	475
Images and data courtesy of ESPN

import pandas as pd
from great_tables import GT, md, html
import gt_extras as gte

df = pd.DataFrame(
    {
        "Player": ["Josh Hart", "Jalen Brunson"],
        "Points": [1051, 1690],
        "Assists": [453, 475],
    }
)

hart_img = gte.add_text_img(
    text="Josh Hart",
    img_url="https://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/3062679.png",
)

brunson_img = gte.add_text_img(
    text="Jalen Brunson",
    img_url="https://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/3934672.png",
)

df["Player"] = [hart_img, brunson_img]
gt = (
    GT(df, rowname_col="Player")
    .tab_source_note(md("Images and data courtesy of [ESPN](https://www.espn.com)"))
)

gt

fa_icon_repeat

Country	Population	People
World Population in 2020
Each person icon represents ~10 million people
United States	331,511,512
Brazil	213,196,304
Mexico	125,998,302
France	67,571,107
Colombia	50,930,662
Poland	37,899,070
Ecuador	17,588,595
Haiti	11,306,801
Switzerland	8,638,167

import pandas as pd
from great_tables import GT
from great_tables.data import countrypops
import gt_extras as gte

countries_2020 = (
    countrypops
    .query("year == 2020")
    .sort_values("population", ascending=False)
    .iloc[[2, 5, 10, 20, 28, 38, 65, 80, 98]]
)

# Scale: 1 icon = 10 million people
def create_population_icons(population):
    icon_count = max(1, round(population / 10_000_000))

    return gte.fa_icon_repeat(
        name="person",
        repeats=icon_count,
        fill="cornflowerblue",
    )

# Prepare the data
df = pd.DataFrame({
    "Country": countries_2020["country_name"],
    "Population": countries_2020["population"],
    "People": [create_population_icons(pop) for pop in countries_2020["population"]],
})

(
    GT(df)
    .tab_header(
        title="World Population in 2020",
        subtitle="Each person icon represents ~10 million people"
    )
    .fmt_number(
        columns="Population",
        decimals=0
    )
)

gt_fa_rank_change

name	1996_2001	2001_2006	2006_2011
Addington Highlands	-0.0111	0.0458	0.002
Adelaide Metcalfe	0.0067	-0.0044	-0.0341
Adjala-Tosorontio	0.0773	0.0608	-0.0086
Admaston/Bromley	-0.0046	-0.0382	0.0471
Ajax	0.1447	0.2226	0.2155
Alberton	-0.0691	0.0021	-0.0981
Alfred and Plantagenet	0.0334	0.0071	0.0626
Algonquin Highlands	0.083	0.0816	0.1063
Alnwick/Haldimand	0.0575	0.1008	0.0283
Amaranth	0.0928	0.0199	0.0307

from great_tables import GT
from great_tables.data import towny
import gt_extras as gte

mini_towny = towny.head(10)
gt = GT(mini_towny).cols_hide(None).cols_unhide("name")

columns = [
    "pop_change_1996_2001_pct",
    "pop_change_2001_2006_pct",
    "pop_change_2006_2011_pct",
]

for col in columns:
    gt = (
        gt
        .cols_align(columns=col, align="center")
        .cols_unhide(col)
        .cols_label({col: col[11:20]})

        .pipe(
            gte.gt_fa_rank_change,
            column=col,
            neutral_range=[-0.01, 0.01],
        )
    )

gt

gt_fa_rating

Car	mfr	hp	trq	mpg_c
F12Berlinetta	Ferrari	731.0	509.0	11.0
LaFerrari	Ferrari	949.0	664.0	12.0
NSX	Acura	573.0	476.0	21.0
GT-R	Nissan	545.0	436.0	16.0
Aventador	Lamborghini	700.0	507.0	11.0
Huracan	Lamborghini	610.0	413.0	16.0
Gallardo	Lamborghini	550.0	398.0	12.0
Continental GT	Bentley	500.0	487.0	15.0

from random import randint
from great_tables import GT
from great_tables.data import gtcars
import gt_extras as gte

gtcars_mini = (
    gtcars
    .loc[8:15, ["model", "mfr", "hp", "trq", "mpg_c"]]
    .assign(rating=[randint(1, 5) for _ in range(8)])
)

gt = (
    GT(gtcars_mini, rowname_col="model")
    .tab_stubhead(label="Car")
)

gt.pipe(gte.gt_fa_rating, columns="rating", name="star")

img_header

	Josh Hart	Jalen Brunson	Mikal Bridges
Points	1051	1690	1444
Rebounds	737	187	259
Assists	453	475	306
Blocks	27	8	43
Steals	119	60	75
Images and data courtesy of ESPN

import pandas as pd
from great_tables import GT, md
import gt_extras as gte

df = pd.DataFrame(
    {
        "Category": ["Points", "Rebounds", "Assists", "Blocks", "Steals"],
        "Hart": [1051, 737, 453, 27, 119],
        "Brunson": [1690, 187, 475, 8, 60],
        "Bridges": [1444, 259, 306, 43, 75],
    }
)

hart_header = gte.img_header(
    label="Josh Hart",
    img_url="https://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/3062679.png",
)

brunson_header = gte.img_header(
    label="Jalen Brunson",
    img_url="https://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/3934672.png",
)

bridges_header = gte.img_header(
    label="Mikal Bridges",
    img_url="https://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/3147657.png",
)

gt = (
    GT(df, rowname_col="Category")
    .tab_source_note(md("Images and data courtesy of [ESPN](https://www.espn.com)"))
    .cols_label(
        {
            "Hart": hart_header,
            "Brunson": brunson_header,
            "Bridges": bridges_header,
        }
    )
)

gt

Utilities

fmt_pct_extra

name	Population Change
name	'96-'01	'01-'06	'06-'11
Whitchurch-Stouffville	11.6%	6.7%	54.3%
White River	<5%	-15.3%	-27.8%
Whitestone	6.4%	20.8%	-10.9%
Whitewater	<5%	<5%	<5%
Wilmot	7.5%	15.0%	12.4%
Windsor	5.4%	<5%	<5%
Wollaston	-5.6%	7.5%	<5%
Woodstock	<5%	8.3%	5.4%
Woolwich	5.1%	8.0%	17.7%
Zorra	<5%	<5%	<5%

from great_tables import GT
from great_tables.data import towny
import gt_extras as gte

towny_mini = towny[
    [
        "name",
        "pop_change_1996_2001_pct",
        "pop_change_2001_2006_pct",
        "pop_change_2006_2011_pct",
    ]
].tail(10)

gt = (
    GT(towny_mini)
    .tab_spanner(label="Population Change", columns=[1, 2, 3])
    .cols_label(
        pop_change_1996_2001_pct="'96-'01",
        pop_change_2001_2006_pct="'01-'06",
        pop_change_2006_2011_pct="'06-'11",
    )
)

gt.pipe(
    gte.fmt_pct_extra,
    columns=[1, 2, 3],
    threshold=5,
    color="green",
)

gt_add_divider

name_given	Location		phone_number	Body Measurements
name_given	address	city	phone_number	height_cm	weight_kg
Ruth	4299 Bobcat Drive	Baileys Crossroads	240-783-7630	153	76.4
Peter	3705 Hidden Pond Road	Red Boiling Springs	615-699-3517	175	74.9
Fanette	4200 Swick Hill Street	New Orleans	985-205-2970	167	61.6
Judyta	2287 Cherry Ridge Drive	Oakfield	585-948-7790	156	54.5
Leonard	1496 Hillhaven Drive	Los Angeles	323-857-6576	177	113.2
Maymun	4088 Barnes Avenue	Hamilton	513-738-1936	172	88.4

import pandas as pd
from great_tables import GT
from great_tables.data import peeps
import gt_extras as gte

peeps_mini = peeps.head(6)

gt = (
    GT(peeps_mini)
    .cols_hide([
        "name_family", "postcode", "country", "country_code",
        "dob", "gender", "state_prov", "email_addr",
    ])
    .tab_spanner("Location", ["address", "city"])
    .tab_spanner("Body Measurements", ["height_cm", "weight_kg"])
)

gt.pipe(
    gte.gt_add_divider,
    columns="name_given",
    color="#FFB90F",
    divider_style="double",
    weight=8,
).pipe(
    gte.gt_add_divider,
    columns="phone_number",
    color="purple",
    sides=["right", "left"],
    weight=5,
)

gt_duplicate_column

mfr	model	year	hp_dupe	hp
Ford	GT	2017	647.0	647.0
Ferrari	458 Speciale	2015	597.0	597.0
Ferrari	458 Spider	2015	562.0	562.0
Ferrari	458 Italia	2014	562.0	562.0
Ferrari	488 GTB	2016	661.0	661.0

from great_tables import GT
from great_tables.data import gtcars
import gt_extras as gte

gtcars_mini = gtcars[["mfr", "model", "year", "hp"]].head(5)
gt = GT(gtcars_mini)

(
    gt.pipe(
        gte.gt_duplicate_column,
        column="hp",
        after="year",
    ).pipe(
        gte.gt_highlight_cols,
        ["hp", "hp_dupe"],
        fill="tan",
    ).pipe(
        gte.gt_add_divider,
        columns=["hp_dupe"],
        include_labels=False,
    )
)

gt_merge_stack

	team_abbr	team_wordmark
NFC
Cardinals NFC West	ARI
Falcons NFC South	ATL
Panthers NFC South	CAR
Bears NFC North	CHI
AFC
Ravens AFC North	BAL
Bills AFC East	BUF
Bengals AFC North	CIN
Browns AFC North	CLE

import pandas as pd
from great_tables import GT
import gt_extras as gte

df = pd.read_csv("../assets/teams_colors_logos.csv")
df = (df.filter(items=["team_nick", "team_abbr", "team_conf", "team_division", "team_wordmark"]).head(8))

gt = GT(df, groupname_col="team_conf", rowname_col="team_nick")
gt = gt.fmt_image(columns="team_wordmark")


gt.pipe(
    gte.gt_merge_stack,
    col1="team_nick",
    col2="team_division",
)

gt_two_column_layout

Joined Table

name_given	address
Ruth	4299 Bobcat Drive
Peter	3705 Hidden Pond Road
Fanette	4200 Swick Hill Street
Judyta	2287 Cherry Ridge Drive
Leonard	1496 Hillhaven Drive
Maymun	4088 Barnes Avenue

name_given	address
Alma	4897 Duffy Street
Mariana	2016 Kovar Road
Ning	3892 Bel Meadow Drive
Martin	1850 Valley Lane
Cendrillon	Rue de Liège 466
Adélaïde	Orchideeenlaan 470

from great_tables import GT
import gt_extras as gte
from great_tables.data import peeps

df1 = peeps.loc[0:5, ["name_given", "address"]]
df2 = peeps.loc[6:11, ["name_given", "address"]]

gt1 = GT(df1).tab_header(title="Joined Table")
gt2 = GT(df2)

gte.gt_two_column_layout(gt1, gt2, table_header_from=1)

with_hyperlink

Package	Github Stars
Click the Links!
Great Tables	2334
Plotnine	4256
Quarto	4628

import pandas as pd
from great_tables import GT
import gt_extras as gte

df = pd.DataFrame(
    {
        "name": ["Great Tables", "Plotnine", "Quarto"],
        "url": [
            "https://posit-dev.github.io/great-tables/",
            "https://plotnine.org/",
            "https://quarto.org/",
        ],
        "github_stars": [2334, 4256, 4628],
        "repo_url": [
            "https://github.com/posit-dev/great-tables",
            "https://github.com/has2k1/plotnine",
            "https://github.com/quarto-dev/quarto-cli",
        ],
    }
)

df["Package"] = [
    gte.with_hyperlink(name, url)
    for name, url in zip(df["name"], df["url"])
]

df["Github Stars"] = [
    gte.with_hyperlink(github_stars, repo_url, new_tab=False)
    for github_stars, repo_url in zip(df["github_stars"], df["repo_url"])
]

GT(df[["Package", "Github Stars"]]).tab_header("Click the Links!")

with_tooltip

Package	Designed for
Hover for Tooltip
Great Tables	Tabling
Plotnine	Plotting
Quarto	Notebooking

import pandas as pd
from great_tables import GT
import gt_extras as gte

df = pd.DataFrame(
    {
        "name": ["Great Tables", "Plotnine", "Quarto"],
        "Description": [
            "Absolutely Delightful Table-making in Python",
            "A grammar of graphics for Python",
            "An open-source scientific and technical publishing system",
        ],
        "Designed for": ["Tabling", "Plotting", "Notebooking"]
    }
)

df["Package"] = [
    gte.with_tooltip(name, description, color = "none")
    for name, description in zip(df["name"], df["Description"])
]

GT(df[["Package", "Designed for"]]).tab_header("Hover for Tooltip")