# Examples
``` python
import polars as pl
from great_tables import GT, md, html
from great_tables.data import countrypops
countries_mini = (
pl.from_pandas(countrypops)
.filter(pl.col("year") == 2022)
.sort("population", descending=True)
.select("country_name", "population")
.head(10)
)
(
GT(countries_mini)
.tab_header(
title="The World's Most Populous Countries",
subtitle="The top ten in 2022 are presented"
)
.tab_stub(rowname_col="country_name")
.tab_source_note(
source_note="Total population counts all residents regardless of legal status or citizenship."
)
.tab_source_note(
source_note=md("Source: [The World Bank](https://data.worldbank.org/indicator/SP.POP.TOTL).")
)
.tab_stubhead(label="country")
.fmt_integer(columns="population")
)
```
| The World's Most Populous Countries | |
|----|----|
| The top ten in 2022 are presented | |
| country | population |
| India | 1,417,173,173 |
| China | 1,412,175,000 |
| United States | 333,287,557 |
| Indonesia | 275,501,339 |
| Pakistan | 235,824,862 |
| Nigeria | 218,541,212 |
| Brazil | 215,313,498 |
| Bangladesh | 171,186,372 |
| Russian Federation | 143,555,736 |
| Mexico | 127,504,125 |
| Total population counts all residents regardless of legal status or citizenship. | |
| Source: [The World Bank](https://data.worldbank.org/indicator/SP.POP.TOTL). | |
``` python
from great_tables import GT, html
from great_tables.data import gibraltar
gibraltar_mini = gibraltar.head(10)[["temp", "humidity", "wind_speed", "pressure", "date", "time"]]
(
GT(gibraltar_mini)
.tab_header(
title="Weather at Gibraltar Airport",
subtitle="Observations from the early hours of May 1, 2023"
)
.tab_spanner(
label="Observed",
columns=["date", "time"]
)
.tab_spanner(
label="Measurement",
columns=["temp", "humidity", "wind_speed", "pressure"]
)
.cols_move_to_start(columns=["date", "time"])
.fmt_percent(columns="humidity", decimals=0)
.cols_label(
date = "Date",
time = "Time",
temp = html("Temp,
°C"),
humidity = "Humidity",
wind_speed = html("Wind,
m s−1"),
pressure = html("Pressure,
hPa")
)
)
```
[TABLE]
``` python
from great_tables import GT
from great_tables.data import countrypops
import polars as pl
import polars.selectors as cs
# Get vectors of 2-letter country codes for each region of Oceania
oceania = {
"Australasia": ["AU", "NZ"],
"Melanesia": ["NC", "PG", "SB", "VU"],
"Micronesia": ["FM", "GU", "KI", "MH", "MP", "NR", "PW"],
"Polynesia": ["PF", "WS", "TO", "TV"],
}
# Create a dictionary mapping country to region (e.g. AU -> Australasia)
country_to_region = {
country: region for region, countries in oceania.items() for country in countries
}
wide_pops = (
pl.from_pandas(countrypops)
.filter(
pl.col("country_code_2").is_in(list(country_to_region))
& pl.col("year").is_in([2000, 2010, 2020])
)
.with_columns(pl.col("country_code_2").replace(country_to_region).alias("region"))
.pivot(index=["country_name", "region"], on="year", values="population")
.sort("2020", descending=True)
)
(
GT(wide_pops)
.tab_header(title="Populations of Oceania's Countries in 2000, 2010, and 2020")
.tab_spanner(label="Total Population", columns=cs.all())
.tab_stub(rowname_col="country_name", groupname_col="region")
.fmt_integer()
)
```
| Populations of Oceania's Countries in 2000, 2010, and 2020 | | | |
|----|----|----|----|
| | Total Population | | |
| | 2000 | 2010 | 2020 |
| Australasia | | | |
| Australia | 19,028,802 | 22,031,750 | 25,655,289 |
| New Zealand | 3,857,700 | 4,350,700 | 5,090,200 |
| Melanesia | | | |
| Papua New Guinea | 5,508,297 | 7,583,269 | 9,749,640 |
| Solomon Islands | 429,978 | 540,394 | 691,191 |
| Vanuatu | 192,074 | 245,453 | 311,685 |
| New Caledonia | 213,230 | 249,750 | 272,460 |
| Polynesia | | | |
| French Polynesia | 250,927 | 283,788 | 301,920 |
| Samoa | 184,008 | 194,672 | 214,929 |
| Tonga | 102,603 | 107,383 | 105,254 |
| Tuvalu | 9,638 | 10,550 | 11,069 |
| Micronesia | | | |
| Guam | 160,188 | 164,905 | 169,231 |
| Kiribati | 88,826 | 107,995 | 126,463 |
| Micronesia (Federated States) | 111,709 | 107,588 | 112,106 |
| Northern Mariana Islands | 80,338 | 54,087 | 49,587 |
| Marshall Islands | 54,224 | 53,416 | 43,413 |
| Palau | 19,726 | 18,540 | 17,972 |
| Nauru | 10,377 | 10,241 | 12,315 |
``` python
from great_tables import GT, html
from great_tables.data import towny
towny_mini = (
towny[["name", "website", "density_2021", "land_area_km2", "latitude", "longitude"]]
.sort_values("density_2021", ascending=False)
.head(10)
)
towny_mini["url_name"] = "[" + towny_mini["name"] + "](" + towny_mini["website"] + ")"
towny_mini["location"] = (
"[map](http://maps.google.com/?ie=UTF8&hq=&ll="
+ towny_mini["latitude"].astype(str)
+ ","
+ towny_mini["longitude"].astype(str)
+ "&z=13)"
)
(
GT(
towny_mini[["url_name", "location", "land_area_km2", "density_2021"]],
rowname_col="url_name",
)
.tab_header(
title="The Municipalities of Ontario",
subtitle="The top 10 highest population density in 2021",
)
.tab_stubhead(label="Municipality")
.fmt_markdown(columns=["url_name", "location"])
.fmt_number(columns=["land_area_km2", "density_2021"], decimals=1)
.cols_label(
land_area_km2=html("land area,
km2"),
density_2021=html("density,
people/km2"),
)
)
```
[TABLE]
``` python
from great_tables import GT, html
from great_tables.data import sza
import polars as pl
import polars.selectors as cs
sza_pivot = (
pl.from_pandas(sza)
.filter((pl.col("latitude") == "20") & (pl.col("tst") <= "1200"))
.select(pl.col("*").exclude("latitude"))
.drop_nulls()
.pivot(values="sza", index="month", on="tst", sort_columns=True)
)
(
GT(sza_pivot, rowname_col="month")
.data_color(
domain=[90, 0],
palette=["rebeccapurple", "white", "orange"],
na_color="white",
)
.tab_header(
title="Solar Zenith Angles from 05:30 to 12:00",
subtitle=html("Average monthly values at latitude of 20°N."),
)
.sub_missing(missing_text="")
)
```
| Solar Zenith Angles from 05:30 to 12:00 | | | | | | | | | | | | | | |
|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|
| Average monthly values at latitude of 20°N. | | | | | | | | | | | | | | |
| | 0530 | 0600 | 0630 | 0700 | 0730 | 0800 | 0830 | 0900 | 0930 | 1000 | 1030 | 1100 | 1130 | 1200 |
| jan | | | | 84.9 | 78.7 | 72.7 | 66.1 | 61.5 | 56.5 | 52.1 | 48.3 | 45.5 | 43.6 | 43.0 |
| feb | | | 88.9 | 82.5 | 75.8 | 69.6 | 63.3 | 57.7 | 52.2 | 47.4 | 43.1 | 40.0 | 37.8 | 37.2 |
| mar | | | 85.7 | 78.8 | 72.0 | 65.2 | 58.6 | 52.3 | 46.2 | 40.5 | 35.5 | 31.4 | 28.6 | 27.7 |
| apr | | 88.5 | 81.5 | 74.4 | 67.4 | 60.3 | 53.4 | 46.5 | 39.7 | 33.2 | 26.9 | 21.3 | 17.2 | 15.5 |
| may | | 85.0 | 78.2 | 71.2 | 64.3 | 57.2 | 50.2 | 43.2 | 36.1 | 29.1 | 26.1 | 15.2 | 8.8 | 5.0 |
| jun | 89.2 | 82.7 | 76.0 | 69.3 | 62.5 | 55.7 | 48.8 | 41.9 | 35.0 | 28.1 | 21.1 | 14.2 | 7.3 | 2.0 |
| jul | 88.8 | 82.3 | 75.7 | 69.1 | 62.3 | 55.5 | 48.7 | 41.8 | 35.0 | 28.1 | 21.2 | 14.3 | 7.7 | 3.1 |
| aug | | 83.8 | 77.1 | 70.2 | 63.3 | 56.4 | 49.4 | 42.4 | 35.4 | 28.3 | 21.3 | 14.3 | 7.3 | 1.9 |
| sep | | 87.2 | 80.2 | 73.2 | 66.1 | 59.1 | 52.1 | 45.1 | 38.1 | 31.3 | 24.7 | 18.6 | 13.7 | 11.6 |
| oct | | | 84.1 | 77.1 | 70.2 | 63.3 | 56.5 | 49.9 | 43.5 | 37.5 | 32.0 | 27.4 | 24.3 | 23.1 |
| nov | | | 87.8 | 81.3 | 74.5 | 68.3 | 61.8 | 56.0 | 50.2 | 45.3 | 40.7 | 37.4 | 35.1 | 34.4 |
| dec | | | | 84.3 | 78.0 | 71.8 | 66.1 | 60.5 | 55.6 | 50.9 | 47.2 | 44.2 | 42.4 | 41.8 |
``` python
from great_tables import GT, html
from great_tables.data import gtcars
cols = ["hp", "hp_rpm", "trq", "trq_rpm", "mpg_c", "mpg_h", "msrp"]
row_labels = {
"hp": "HP",
"hp_rpm": "HP RPM",
"trq": "Torque",
"trq_rpm": "Torque RPM",
"mpg_c": "MPG city",
"mpg_h": "MPG hwy",
"msrp": "MSRP",
}
gtcars_corr = gtcars[cols].corr().reset_index(names="variable")
gtcars_corr["variable"] = gtcars_corr["variable"].map(row_labels)
(
GT(gtcars_corr, rowname_col="variable")
.tab_header(
title="Correlations Between gtcars Performance Specs",
subtitle="Pearson correlation across horsepower, torque, fuel economy, and price",
)
.fmt_number(columns=cols, decimals=2)
.data_color(
columns=cols,
palette=["#4b6ea9", "white", "#c0392b"],
domain=[-1, 1],
na_color="white",
)
.cols_label(
hp="HP",
hp_rpm=html("HP
RPM"),
trq="Torque",
trq_rpm=html("Torque
RPM"),
mpg_c=html("MPG
city"),
mpg_h=html("MPG
hwy"),
msrp="MSRP",
)
.tab_source_note(source_note="Source: great_tables.data.gtcars")
)
```
[TABLE]
View notebook ⬀
Highest Paid Athletes in 2023
Name
icon
Sport
Earnings
Total \$M
Off field \$M
Off field %
1
Cristiano Ronaldo
Soccer
136.0
90.0
2
Lionel Messi
Soccer
130.0
65.0
3
Kylian Mbappé
Soccer
120.0
20.0
4
LeBron James
Basketball
119.5
75.0
5
Canelo Alvarez
Boxing
110.0
10.0
6
Dustin Johnson
Golf
107.0
5.0
7
Phil Mickelson
Golf
106.0
2.0
8
Stephen Curry
Basketball
100.4
52.0
9
Roger Federer
Tennis
95.1
95.0
\
Original table: [@LisaHornung\_](https://twitter.com/LisaHornung_/status/1752981867769266231) \| Sports icons: [Firza Alamsyah](https://thenounproject.com/browse/collection-icon/sports-96427) \| Data: Forbes
\
View source ⬀ Blog post (R code) ⬀
[TABLE]
View source ⬀ Notebook ⬀
``` python
import polars as pl
import polars.selectors as cs
from great_tables import GT, loc, style
coffee_sales = pl.read_ndjson("./_data/coffee-sales.ndjson")
sel_rev = cs.starts_with("revenue")
sel_prof = cs.starts_with("profit")
# yo
coffee_table = (
GT(coffee_sales)
.tab_header("Sales of Coffee Equipment")
.tab_spanner(label="Revenue", columns=sel_rev)
.tab_spanner(label="Profit", columns=sel_prof)
.cols_label(
revenue_dollars="Amount",
profit_dollars="Amount",
revenue_pct="Percent",
profit_pct="Percent",
monthly_sales="Monthly Sales",
icon="",
product="Product",
)
# formatting ----
.fmt_number(
columns=cs.ends_with("dollars"),
compact=True,
pattern="${x}",
n_sigfig=3,
)
.fmt_percent(columns=cs.ends_with("pct"), decimals=0)
# style ----
.tab_style(
style=style.fill(color="aliceblue"),
locations=loc.body(columns=sel_rev),
)
.tab_style(
style=style.fill(color="papayawhip"),
locations=loc.body(columns=sel_prof),
)
.tab_style(
style=style.text(weight="bold"),
locations=loc.body(rows=pl.col("product") == "Total"),
)
.fmt_nanoplot("monthly_sales", plot_type="bar")
.fmt_image("icon", path="_data/coffee-table-icons/")
.sub_missing(missing_text="")
)
coffee_table
```
| Sales of Coffee Equipment | | | | | | |
|----|----|----|----|----|----|----|
| | Product | Revenue | | Profit | | Monthly Sales |
| | | Amount | Percent | Amount | Percent | |
|  | Grinder | \$904K | 3% | \$568K | 4% |  |
|  | Moka pot | \$2.05M | 7% | \$181K | 1% |  |
|  | Cold brew | \$289K | 1% | \$242K | 2% |  |
|  | Filter | \$404K | 1% | \$70.0K | 0% |  |
|  | Drip machine | \$2.63M | 9% | \$1.37M | 9% |  |
|  | AeroPress | \$2.60M | 9% | \$1.29M | 9% |  |
|  | Pour over | \$846K | 3% | \$365K | 2% |  |
|  | French press | \$1.11M | 4% | \$748K | 5% |  |
|  | Cezve | \$2.51M | 9% | \$1.97M | 13% |  |
|  | Chemex | \$3.14M | 11% | \$818K | 6% |  |
|  | Scale | \$3.80M | 13% | \$2.91M | 20% |  |
|  | Kettle | \$756K | 3% | \$618K | 4% |  |
|  | Espresso Machine | \$8.41M | 29% | \$3.64M | 25% |  |
| | Total | \$29.4M | 100% | \$14.8M | 100% | |