Styling data frames

Data set download

[2]:

import polars as pl
import polars.selectors as cs
import great_tables

It is sometimes useful to highlight features in a data frame when viewing them. (Note that this is generally far less useful than making informative plots, which we will come to shortly.) We sometimes also want to make a table for display. For this purpose, Polars works seamlessly with Great Tables package. To convert a Polars data frame to a Great Tables object that can then be stylized, simply use the df.style attribute of a Polars data frame. You can read about the endless possibilities for styling Polars data frames using Great Tables in the documentation.

Here, we will do a quick demonstration, again using a data set from Beattie, et al. containing results from a study the effects of sleep quality on performance in the Glasgow Facial Matching Test (GMFT). We will make a pretty-looking table also highlighting rows corresponding to women who scored at 90% or above.

[3]:

# Load data set
df = pl.read_csv(os.path.join(data_path, 'gfmt_sleep.csv'), null_values='*')

# Style the table
(
    df.sort(by='participant number').style
    .tab_options(table_font_size='x-small')
    .tab_header(title='GFMT sleep study')
    .tab_spanner('Participant data', ['participant number', 'gender', 'age'])
    .tab_spanner('GFMT results', cs.contains('correct'))
    .tab_spanner('Sleep indicators', ['sci', 'psqi', 'ess'])
    .tab_style(
        style=great_tables.style.fill('#99dbc9'),
        locations=great_tables.loc.body(
            rows=(pl.col('gender') == 'f')
            & (pl.col('percent correct') >= 90)
        ),
    )
)

[3]:
GFMT sleep study
Participant data GFMT results Sleep indicators
participant number gender age correct hit percentage correct reject percentage percent correct confidence when correct hit confidence incorrect hit confidence correct reject confidence incorrect reject confidence when correct confidence when incorrect sci psqi ess
1 f 42 80 65 72.5 51.5 44.5 43.0 49.0 51.0 49.0 29 1 5
2 f 45 80 90 85.0 75.0 55.5 80.0 75.0 78.5 67.0 19 5 1
3 f 16 70 80 75.0 70.0 57.0 54.0 53.0 57.0 54.5 23 1 3
4 f 21 70 65 67.5 63.5 64.0 50.0 50.0 60.0 50.0 26 5 4
5 f 18 90 100 95.0 76.5 83.0 80.0 None 80.0 83.0 21 7 5
6 f 28 95 80 87.5 100.0 85.0 94.0 61.0 99.0 65.0 19 7 12
7 f 38 90 95 92.5 77.0 43.5 79.0 21.0 78.0 36.0 28 3 4
8 f 39 65 80 72.5 91.0 90.0 93.0 83.5 93.0 90.0 9 13 2
9 m 17 90 90 90.0 80.5 87.5 76.5 27.0 78.5 67.5 29 3 4
10 f 25 100 100 100.0 90.0 None 85.0 None 90.0 None 17 10 11
11 f 22 80 60 70.0 70.0 70.0 70.0 65.0 70.0 70.0 22 4 6
12 m 41 90 80 85.0 76.5 55.5 67.5 52.5 74.0 55.5 28 5 3
13 m 53 95 60 77.5 40.0 33.0 56.0 49.0 47.0 44.0 31 2 11
14 m 43 95 90 92.5 52.0 29.0 49.0 36.0 52.0 29.0 31 2 10
15 f 23 90 80 85.0 88.0 40.0 70.5 66.5 84.0 54.5 32 2 12
16 m 42 90 90 90.0 75.5 55.5 70.5 50.0 75.0 50.0 4 11 7
17 m 19 55 60 57.5 62.0 50.0 66.0 50.5 63.0 50.0 25 5 6
18 f 31 90 95 92.5 89.5 90.0 86.0 81.0 89.0 88.0 10 9 3
19 f 45 100 85 92.5 68.0 None 61.0 54.0 62.0 54.0 30 2 13
20 f 43 65 65 65.0 59.0 55.0 64.0 59.0 59.5 57.0 28 3 12
21 m 35 90 100 95.0 75.5 35.5 74.5 None 75.5 35.5 30 3 5
22 f 35 100 75 87.5 89.5 None 71.0 80.0 88.0 80.0 13 8 20
23 m 24 55 100 77.5 68.0 67.0 80.0 None 78.0 67.0 22 4 10
24 f 64 75 85 80.0 50.0 25.0 66.0 24.0 63.0 24.5 20 9 4
25 f 36 100 80 90.0 88.0 None 66.0 63.5 81.0 63.5 26 5 4
26 m 35 70 90 80.0 29.5 28.5 19.0 41.5 24.0 35.0 32 2 6
27 f 74 60 65 62.5 68.5 49.0 61.0 49.0 65.0 49.0 13 9 12
28 f 61 80 20 50.0 71.0 63.0 31.0 72.5 64.5 70.5 15 14 2
29 f 43 90 85 87.5 65.5 27.0 41.0 45.0 50.0 45.0 32 1 3
30 m 32 90 75 82.5 67.0 56.5 66.0 65.0 66.0 64.0 16 9 3
31 m 44 95 90 92.5 83.0 56.0 79.0 42.5 81.0 54.0 24 4 0
32 f 29 95 55 75.0 67.0 53.0 65.0 53.0 67.0 53.0 26 8 12
33 m 62 45 90 67.5 54.0 37.0 65.0 81.5 62.0 61.0 14 9 9
34 f 33 80 100 90.0 70.5 76.5 64.5 None 68.0 76.5 14 12 10
35 f 53 100 50 75.0 74.5 None 60.5 65.0 71.0 65.0 14 8 7
36 m 22 70 65 67.5 78.0 88.0 77.0 80.0 77.0 84.0 30 2 6
37 f 46 80 75 77.5 72.5 72.0 100.0 91.0 100.0 91.0 28 2 5
38 f 41 70 55 62.5 82.0 61.5 73.0 69.0 82.0 64.0 14 5 19
39 f 35 50 95 72.5 62.5 29.0 77.0 32.0 68.0 31.0 21 6 9
40 m 53 65 80 72.5 54.0 32.0 47.5 78.5 48.0 51.0 29 3 7
41 f 36 90 100 95.0 76.5 75.5 75.0 None 76.0 75.5 15 7 0
42 m 29 100 70 85.0 75.0 None 64.5 43.0 74.0 43.0 32 1 6
43 f 31 85 90 87.5 82.0 49.0 81.0 36.0 82.0 49.0 26 5 10
44 f 21 85 90 87.5 66.0 29.0 70.0 29.0 67.0 29.0 26 7 18
45 f 42 90 90 90.0 83.0 83.0 80.5 36.0 82.5 76.0 23 3 11
46 f 40 95 65 80.0 80.0 89.0 79.0 58.5 79.5 63.0 10 12 8
48 f 23 90 85 87.5 67.0 47.0 69.0 40.0 67.0 40.0 18 6 8
49 f 24 85 75 80.0 58.0 50.0 49.0 68.0 55.0 59.0 14 13 4
50 m 54 90 70 80.0 90.0 83.5 77.5 69.0 88.0 79.0 22 6 16
51 f 24 85 95 90.0 97.0 41.0 74.0 73.0 83.0 55.5 29 1 7
52 f 21 85 75 80.0 65.0 73.0 56.0 68.0 63.5 68.0 20 6 9
53 f 21 90 80 85.0 84.0 55.5 73.5 70.0 80.5 65.0 27 4 11
54 f 43 95 75 85.0 74.0 89.0 68.0 65.0 71.0 68.0 19 4 4
55 f 32 75 55 65.0 85.0 81.0 85.0 86.0 85.0 83.5 5 13 7
56 m 50 70 85 77.5 92.5 72.5 95.0 65.0 95.0 65.0 29 3 7
57 f 53 95 75 85.0 84.0 55.0 68.0 61.0 78.5 58.0 24 5 4
58 f 16 85 85 85.0 55.0 30.0 50.0 40.0 52.5 35.0 29 2 11
59 f 67 95 75 85.0 70.0 7.0 69.0 60.0 69.0 59.5 17 7 12
60 m 36 90 65 77.5 67.5 28.5 55.0 52.0 61.0 50.0 26 4 3
61 f 34 90 90 90.0 58.5 43.0 73.5 53.5 66.0 47.5 30 0 3
62 f 42 100 100 100.0 74.5 None 74.0 None 74.0 None 17 5 4
63 f 46 80 90 85.0 92.0 75.5 92.0 63.0 92.0 73.5 25 1 11
64 f 69 95 80 87.5 80.0 65.0 78.5 70.5 80.0 70.0 31 1 1
65 f 31 100 95 97.5 98.0 None 90.0 40.0 92.0 40.0 27 4 4
66 f 44 90 95 92.5 87.0 47.5 69.0 87.0 83.0 67.0 32 1 2
67 f 25 100 100 100.0 61.5 None 58.5 None 60.5 None 28 8 9
68 f 45 70 50 60.0 80.5 51.5 63.0 69.0 72.5 61.5 25 4 1
69 f 47 90 100 95.0 100.0 None 71.5 83.0 97.5 83.0 30 2 2
70 f 33 85 70 77.5 70.0 38.0 58.5 65.0 68.0 40.0 21 7 12
71 f 40 40 100 70.0 69.0 56.0 70.0 None 70.0 56.0 0 11 14
72 f 18 80 75 77.5 67.5 51.5 66.0 57.0 67.0 53.0 29 4 6
73 f 74 85 80 82.5 66.0 55.0 63.0 50.5 65.0 55.0 20 1 5
74 m 21 40 40 40.0 90.5 80.0 74.5 83.0 82.0 81.0 22 7 5
75 f 45 80 95 87.5 74.0 67.0 76.0 17.0 75.0 64.0 23 4 4
76 f 61 100 40 70.0 69.5 None 44.5 73.0 54.5 73.0 16 4 12
77 f 42 70 90 80.0 87.0 72.0 90.5 43.5 88.5 64.0 11 10 10
78 m 31 100 70 85.0 92.0 None 81.0 60.0 87.5 60.0 14 6 11
79 f 37 90 80 85.0 95.5 68.0 83.5 83.0 94.0 71.0 20 5 9
80 m 28 100 50 75.0 100.0 None 100.0 100.0 100.0 100.0 12 7 12
81 m 41 90 85 87.5 80.0 59.5 70.0 41.0 77.0 59.0 17 6 3
82 f 41 80 75 77.5 94.5 61.5 86.0 74.0 92.0 67.0 27 4 8
83 f 34 90 35 62.5 81.0 52.0 71.0 58.0 81.0 58.0 27 2 6
84 f 39 75 70 72.5 57.0 57.0 59.5 50.0 58.0 50.0 22 3 10
85 f 18 85 85 85.0 93.0 92.0 91.0 89.0 91.5 91.0 25 4 21
86 f 31 100 85 92.5 100.0 None 100.0 50.0 100.0 50.0 30 3 5
87 m 26 95 75 85.0 85.0 88.0 82.0 82.0 85.0 85.0 32 1 5
88 m 66 60 85 72.5 67.5 66.0 74.0 57.0 74.0 64.0 30 5 9
89 f 26 60 80 70.0 70.0 77.0 82.0 67.5 77.0 70.5 14 8 1
90 m 45 100 95 97.5 100.0 None 100.0 100.0 100.0 100.0 14 9 6
91 m 62 100 80 90.0 81.0 None 74.5 82.0 79.5 82.0 32 2 1
92 m 22 85 95 90.0 66.0 56.0 72.0 63.0 70.5 59.5 28 1 8
93 f 28 100 75 87.5 89.5 None 67.0 60.0 80.0 60.0 16 7 4
94 f 41 35 75 55.0 55.0 61.0 80.0 57.0 72.0 60.0 31 1 11
95 m 46 95 80 87.5 90.0 75.0 80.0 80.0 85.0 75.0 29 3 5
96 f 56 70 50 60.0 63.0 52.5 67.5 65.5 64.0 59.5 26 6 7
97 f 23 70 85 77.5 77.0 66.5 77.0 77.5 77.0 74.0 20 8 10
98 f 70 90 85 87.5 65.5 85.5 87.0 80.0 74.0 80.0 19 8 7
99 f 24 70 80 75.0 61.5 81.0 70.0 61.0 65.0 81.0 31 2 15
100 f 44 65 25 45.0 62.0 72.0 87.0 77.0 69.5 73.5 1 15 6
101 f 28 100 40 70.0 87.0 None 68.0 54.0 81.0 54.0 14 7 2
102 f 40 75 65 70.0 53.0 37.0 84.0 52.0 81.0 51.0 22 4 7
103 f 33 85 40 62.5 80.0 27.0 31.0 82.5 81.0 73.0 24 5 7

There is much more you can do, and the documentation of Great Tables has plenty of examples and tips. In my experience, though, it is rare that you will need to style a data frame; results are usually shown graphically.

Computing environment

[4]:

%load_ext watermark
%watermark -v -p polars,great_tables,jupyterlab

Python implementation: CPython
Python version       : 3.12.3
IPython version      : 8.20.0

polars      : 1.0.0
great_tables: 0.9.0
jupyterlab  : 4.0.13