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from reactable import embed_css, render
embed_css()Great Tables Gallery
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import polars as pl
from great_tables import GT, md, html
from great_tables.data import islands
from reactable import render
islands_mini = pl.from_pandas(islands).sort("size", descending=True).head(10)
gt = (
GT(islands_mini)
.tab_header(title="Large Landmasses of the World", subtitle="The top ten largest are presented")
.tab_stub(rowname_col="name")
.tab_source_note(source_note="Source: The World Almanac and Book of Facts, 1975, page 406.")
.tab_source_note(
source_note=md("Reference: McNeil, D. R. (1977) *Interactive Data Analysis*. Wiley.")
)
.tab_stubhead(label="landmass")
.fmt_integer(columns="size")
)
render(gt)Show the Code
import polars as pl
from great_tables import GT, html
from great_tables.data import airquality
airquality_mini = airquality.head(10).assign(Year=1973)
gt = (
GT(pl.from_pandas(airquality_mini))
.tab_header(
title="New York Air Quality Measurements",
subtitle="Daily measurements in New York City (May 1-10, 1973)",
)
.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>°F"),
)
)
render(gt)Show the Code
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"))
.filter(pl.col("region").is_in(["Australasia", "Melanesia"]))
.pivot(index=["country_name", "region"], on="year", values="population")
.sort("2020", descending=True)
)
gt = (
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()
)
render(gt)Show the Code
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)"]
)
pl_towny = pl.from_pandas(towny_mini[["url_name", "location", "land_area_km2", "density_2021"]])
gt = (
GT(
pl_towny,
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, <br>km<sup>2</sup>"),
density_2021=html("density, <br>people/km<sup>2</sup>"),
)
)
render(gt)Show the Code
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 = (
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."),
)
.cols_width({k: 50 for k in sza_pivot.columns})
.sub_missing(missing_text="")
)
render(gt)Show the Code
import polars as pl
import polars.selectors as cs
from great_tables import GT, md
def create_bar(prop_fill: float, max_width: int, height: int) -> str:
"""Create divs to represent prop_fill as a bar."""
width = round(max_width * prop_fill, 2)
px_width = f"{width}px"
return f"""\
<div style="width: {max_width}px; background-color: lightgrey;">\
<div style="height:{height}px;width:{px_width};background-color:green;"></div>\
</div>\
"""
df = pl.read_csv("./sports-earnings/sports_earnings.csv")
res = (
df.with_columns(
(pl.col("Off-the-Field Earnings") / pl.col("Total Earnings")).alias("raw_perc"),
("./sports-earnings/" + pl.col("Sport").str.to_lowercase() + ".png").alias("icon"),
)
.head(9)
.with_columns(
pl.col("raw_perc")
.map_elements(lambda x: create_bar(x, max_width=75, height=20))
.alias("Off-the-Field Earnings Perc")
)
.select("Rank", "Name", "icon", "Sport", "Total Earnings", "Off-the-Field Earnings", "Off-the-Field Earnings Perc")
)
gt = (
GT(res, rowname_col="Rank")
.tab_header("Highest Paid Athletes in 2023")
.tab_spanner("Earnings", cs.contains("Earnings"))
#.fmt_number(cs.starts_with("Total"), scale_by = 1/1_000_000, decimals=1)
.cols_label(**{
"Total Earnings": "Total $M",
"Off-the-Field Earnings": "Off field $M",
"Off-the-Field Earnings Perc": "Off field %"
})
.fmt_number(["Total Earnings", "Off-the-Field Earnings"], scale_by = 1/1_000_000, decimals=1)
.fmt_image("icon", path="./")
.tab_source_note(
md(
'<br><div style="text-align: center;">'
"Original table: [@LisaHornung_](https://twitter.com/LisaHornung_/status/1752981867769266231)"
" | Sports icons: [Firza Alamsyah](https://thenounproject.com/browse/collection-icon/sports-96427)"
" | Data: Forbes"
"</div>"
"<br>"
)
)
)
render(gt)/tmp/ipykernel_2049/3327212400.py:25: MapWithoutReturnDtypeWarning:
Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
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import polars as pl
import polars.selectors as cs
from great_tables import GT, loc, style
coffee_sales = pl.DataFrame.deserialize("coffee-sales/coffee-sales.json", format="json")
sel_rev = cs.starts_with("revenue")
sel_prof = cs.starts_with("profit")
coffee_table = (
GT(coffee_sales)
.tab_header("Sales of Coffee Equipment")
.tab_spanner(label="Revenue", columns=sel_rev)
.tab_spanner(label="Profit", columns=sel_prof)
.cols_label(
revenue_dollars="Amount",
profit_dollars="Amount",
revenue_pct="Percent",
profit_pct="Percent",
monthly_sales="Monthly Sales",
icon="",
product="Product",
)
# formatting ----
.fmt_number(
columns=cs.ends_with("dollars"),
compact=True,
pattern="${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="coffee-sales")
.sub_missing(missing_text="")
)
# coffee_table.save("data/coffee-table.png", scale=2)
render(coffee_table)