Intro

The Great Tables package is all about making it simple to produce nice-looking display tables. Display tables? Well yes, we are trying to distinguish between data tables (i.e., DataFrames) and those tables you’d find in a web page, a journal article, or in a magazine. Such tables can likewise be called presentation tables, summary tables, or just tables really. Here are some examples, ripped straight from the web:

We can think of display tables as output only, where we’d not want to use them as input ever again. Other features include annotations, table element styling, and text transformations that serve to communicate the subject matter more clearly.

Let’s Install

The installation really couldn’t be much easier. Use this:

pip install great_tables

A Basic Table using Great Tables

Note

The example below requires the Pandas library to be installed. But Pandas is not required to use Great Tables. You can also use a Polars DataFrame.

Let’s use a subset of the islands dataset available within great_tables.data:

from great_tables import GT, md, html
from great_tables.data import islands

islands_mini = islands.head(10)

The islands data is a simple Pandas DataFrame with 2 columns and that’ll serve as a great start. Speaking of which, the main entry point into the Great Tables API is the GT class. Let’s use that to make a presentable table:

# Create a display table showing ten of the largest islands in the world
gt_tbl = GT(islands_mini)

# Show the output table
# If working in the console, use gt_tbl.show()
gt_tbl

name	size
Africa	11506
Antarctica	5500
Asia	16988
Australia	2968
Axel Heiberg	16
Baffin	184
Banks	23
Borneo	280
Britain	84
Celebes	73

That doesn’t look too bad! Sure, it’s basic but we really didn’t really ask for much. We did receive a proper table with column labels and the data. Oftentimes however, you’ll want a bit more: a Table header, a Stub, and sometimes source notes in the Table Footer component.

Polars DataFrame support

GT accepts both Pandas and Polars DataFrames. You can pass a Polars DataFrame to GT, or use its DataFrame.style property.

import polars as pl

df_polars = pl.from_pandas(islands_mini)

# Approach 1: call GT ----
GT(df_polars)

# Approach 2: Polars style property ----
df_polars.style

name	size
Africa	11506
Antarctica	5500
Asia	16988
Australia	2968
Axel Heiberg	16
Baffin	184
Banks	23
Borneo	280
Britain	84
Celebes	73

Note

The polars.DataFrame.style property is currently considered unstable, and may change in the future. Using GT on a Polars DataFrame will always work.

Some Beautiful Examples

In the following pages we’ll use Great Tables to turn DataFrames into beautiful tables, like the ones below.

Show the Code

from great_tables import GT, md, html
from great_tables.data import islands

islands_mini = islands.head(10)

(
    GT(islands_mini, rowname_col = "name")
    .tab_header(
        title="Large Landmasses of the World",
        subtitle="The top ten largest are presented"
    )
    .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")
)

landmass	size
Large Landmasses of the World
The top ten largest are presented
Africa	11506
Antarctica	5500
Asia	16988
Australia	2968
Axel Heiberg	16
Baffin	184
Banks	23
Borneo	280
Britain	84
Celebes	73
Source: The World Almanac and Book of Facts, 1975, page 406.
Reference: McNeil, D. R. (1977) Interactive Data Analysis. Wiley.

Show the Code

from great_tables import GT, html
from great_tables.data import airquality

airquality_m = airquality.head(10).assign(Year=1973)

gt_airquality = (
    GT(airquality_m)
    .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>&deg;F"),
    )
)

gt_airquality

Time			Measurement
New York Air Quality Measurements
Daily measurements in New York City (May 1-10, 1973)
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