Great Tables `v0.18.0`: Easy Column Spanners and More!

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from great_tables import GT
from great_tables.data import towny
import polars as pl
import polars.selectors as cs

# Create a smaller version of the `towny` dataset
towny_mini = (
    pl.from_pandas(towny)
    .filter(pl.col("csd_type") == "city")
    .sort("population_2021", descending=True)
    .select(
        "name",
        cs.starts_with("population_"),
        cs.starts_with("density_")
    )
    .head(5)
)

# Let's look at the column names
print(towny_mini.columns)

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(
    GT(towny_mini, rowname_col="name")
    .tab_spanner_delim(delim="_")
    .fmt_integer(columns=cs.contains("population"))
    .fmt_number(columns=cs.contains("density"), decimals=1)
    .tab_header(title="Population and Density Trends from Census Data")
    .opt_align_table_header(align="left")
)

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from great_tables import GT
import polars as pl

# Create a simple DF with boolean data
bool_df = pl.DataFrame({
    "feature": ["Premium Sound", "Leather Seats", "Sunroof", "Navigation"],
    "model_a": [True, False, True, True],
    "model_b": [True, True, False, True],
    "model_c": [False, True, True, False]
})

(
    GT(bool_df, rowname_col="feature")
    .fmt_tf(tf_style="check-mark", colors=["green", "red"])
    .tab_header(title="Car Features Comparison", subtitle="Using check-mark style")
)

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(
    GT(bool_df, rowname_col="feature")
    .fmt_tf(tf_style="circles", colors=["#4CAF50", "#F44336"])
    .tab_header(title="Car Features Comparison", subtitle="Using circles style")
)

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from great_tables import GT, style, loc
from great_tables.data import gtcars
import polars as pl
import polars.selectors as cs

# Manipulate dataset to create a feature comparison table
gtcars_mini = (
    pl.from_pandas(gtcars)
    .filter(pl.col("year") == 2017)
    .filter(pl.col("ctry_origin").is_in(["Germany", "Italy", "United Kingdom"]))
    .with_columns([
        (pl.col("hp") > 500).alias("High Power"),
        (pl.col("mpg_h") > 25).alias("Fuel Efficient"),
        (pl.col("drivetrain") == "awd").alias("All Wheel Drive"),
        (pl.col("msrp") > 100000).alias("Premium Price"),
        (pl.col("trsmn").str.contains("manual")).alias("Manual Transmission")
    ])
    .select([
        "mfr", "model", "trim",
        "High Power",
        "Fuel Efficient",
        "All Wheel Drive",
        "Premium Price",
        "Manual Transmission"
    ])
    .head(10)
)

(
    GT(gtcars_mini)
    .fmt_tf(
        columns=cs.by_dtype(pl.Boolean),
        tf_style="check-mark",
        colors=["#2E8B57", "#DC143C"]
    )
    .cols_label_rotate(
        columns=cs.by_dtype(pl.Boolean),
        dir="sideways-lr"
    )
    .tab_header(
        title="European Luxury Cars Feature Matrix",
        subtitle="2017 Models with Performance & Luxury Features"
    )
    .opt_stylize(style=1)
    .tab_style(
        style=style.text(size="11px"),
        locations=loc.body()
    )
)

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from great_tables import GT
from great_tables.data import gibraltar
import polars as pl

# Prepare the meteorological data
gibraltar_mini = (
    pl.from_pandas(gibraltar)
    .with_columns(
        [
            pl.concat_str([pl.col("date"), pl.lit(" "), pl.col("time")])
            .str.strptime(pl.Datetime, format="%Y-%m-%d %H:%M")
            .alias("datetime")
        ]
    )
    .filter(pl.col("datetime").dt.hour().is_in([6, 12, 18]))
    .select(["datetime", "temp", "humidity", "condition"])
    .sort("datetime")
    .head(10)
)

(
    GT(gibraltar_mini)
    .fmt_datetime(
        columns="datetime",
        format_str="%b %d %Y (%a) - %I:%M %p",
    )
    .fmt_number(columns="temp", decimals=1, pattern="{x}°C")
    .fmt_percent(columns="humidity", scale_values=False, decimals=0)
    .cols_label(
        datetime="Time",
        temp="Temperature",
        humidity="Humidity",
        condition="Conditions",
    )
    .tab_header(
        title="Gibraltar Temperature and Humidity Conditions",
        subtitle="Morning, Noon, and Evening Readings"
    )
    .opt_stylize(style=1, color="cyan")
)