Problem when integrating min_max for variables -- Seems that I have to choose between presenting SD or range

[anovabr]

I'm using TableOne in Python 3.11.9
This code below is not presenting the SD of each variable. Is it possible to have both SD and range ?
Thanks.

# Define the list of variables
variables = ['f_exec_a', 'f_exec_e', 'f_exec_o', 'f_exec_dm', 'f_exec_c', 'f_exec_pb', 'fe_score']

# Generate the descriptive table
table = TableOne(df_fe, 
                 columns=variables,
                 min_max=variables,
                 continuous=variables,
                 display_all=True,
                 missing=False)

# Print the table
print(table.tabulate(tablefmt="rounded_outline"))

[tompollard]

Short answer is that we don't currently support what you are asking for, sorry! If you want SD, you can do:

import pandas as pd
import numpy as np
from tableone import TableOne

# Create sample data
np.random.seed(0)
df_fe = pd.DataFrame({
    'f_exec_a': np.random.normal(50, 10, 100),
    'f_exec_e': np.random.normal(60, 15, 100),
    'f_exec_o': np.random.normal(70, 20, 100),
    'f_exec_dm': np.random.normal(80, 5, 100),
    'f_exec_c': np.random.normal(90, 12, 100),
    'f_exec_pb': np.random.normal(100, 18, 100),
    'fe_score': np.random.normal(110, 7, 100)
})

variables = ['f_exec_a', 'f_exec_e', 'f_exec_o', 'f_exec_dm', 'f_exec_c', 'f_exec_pb', 'fe_score']

table = TableOne(df_fe, 
                 columns=variables,
                 continuous=variables,
                #  min_max=variables,
                 display_all=True,
                 missing=False)

print(table.tabulate(tablefmt="rounded_outline"))

╭──────────────────────┬────┬─────────────╮
│                      │    │ Overall     │
├──────────────────────┼────┼─────────────┤
│ n                    │    │ 100         │
│ f_exec_a, mean (SD)  │    │ 50.6 (10.1) │
│ f_exec_e, mean (SD)  │    │ 61.2 (15.6) │
│ f_exec_o, mean (SD)  │    │ 68.8 (19.1) │
│ f_exec_dm, mean (SD) │    │ 79.0 (4.7)  │
│ f_exec_c, mean (SD)  │    │ 89.8 (12.5) │
│ f_exec_pb, mean (SD) │    │ 96.3 (18.4) │
│ fe_score, mean (SD)  │    │ 109.1 (7.1) │
╰──────────────────────┴────┴─────────────╯

If you want the range, you can do:

table = TableOne(df_fe, 
                 columns=variables,
                 continuous=variables,
                 min_max=variables,
                 display_all=True,
                 missing=False)

print(table.tabulate(tablefmt="rounded_outline"))

╭───────────────────────────┬────┬────────────────────╮
│                           │    │ Overall            │
├───────────────────────────┼────┼────────────────────┤
│ n                         │    │ 100                │
│ f_exec_a, mean [min,max]  │    │ 50.6 [24.5,72.7]   │
│ f_exec_e, mean [min,max]  │    │ 61.2 [26.6,95.7]   │
│ f_exec_o, mean [min,max]  │    │ 68.8 [14.5,116.1]  │
│ f_exec_dm, mean [min,max] │    │ 79.0 [66.7,91.3]   │
│ f_exec_c, mean [min,max]  │    │ 89.8 [57.1,122.4]  │
│ f_exec_pb, mean [min,max] │    │ 96.3 [45.2,136.2]  │
│ fe_score, mean [min,max]  │    │ 109.1 [90.2,126.7] │
╰───────────────────────────┴────┴────────────────────╯

...but there is currently no way of displaying SD, Range in a single command. It wouldn't be difficult to implement, but we would need to decide a way of making this configurable through the input arguments to TableOne.

If you have ideas for handling the argument, let us know.

[anovabr]

Thanks!

…

On Sat, Apr 26, 2025 at 1:28 PM Tom Pollard ***@***.***> wrote: *tompollard* left a comment (tompollard/tableone#197) <#197 (comment)> Short answer is that we don't currently support what you are asking for, sorry! If you want SD, you can do: import pandas as pd import numpy as np from tableone import TableOne # Create sample data np.random.seed(0) df_fe = pd.DataFrame({ 'f_exec_a': np.random.normal(50, 10, 100), 'f_exec_e': np.random.normal(60, 15, 100), 'f_exec_o': np.random.normal(70, 20, 100), 'f_exec_dm': np.random.normal(80, 5, 100), 'f_exec_c': np.random.normal(90, 12, 100), 'f_exec_pb': np.random.normal(100, 18, 100), 'fe_score': np.random.normal(110, 7, 100) }) variables = ['f_exec_a', 'f_exec_e', 'f_exec_o', 'f_exec_dm', 'f_exec_c', 'f_exec_pb', 'fe_score'] table = TableOne(df_fe, columns=variables, continuous=variables, # min_max=variables, display_all=True, missing=False) print(table.tabulate(tablefmt="rounded_outline")) ╭──────────────────────┬────┬─────────────╮ │ │ │ Overall │ ├──────────────────────┼────┼─────────────┤ │ n │ │ 100 │ │ f_exec_a, mean (SD) │ │ 50.6 (10.1) │ │ f_exec_e, mean (SD) │ │ 61.2 (15.6) │ │ f_exec_o, mean (SD) │ │ 68.8 (19.1) │ │ f_exec_dm, mean (SD) │ │ 79.0 (4.7) │ │ f_exec_c, mean (SD) │ │ 89.8 (12.5) │ │ f_exec_pb, mean (SD) │ │ 96.3 (18.4) │ │ fe_score, mean (SD) │ │ 109.1 (7.1) │ ╰──────────────────────┴────┴─────────────╯ If you want the range, you can do: table = TableOne(df_fe, columns=variables, continuous=variables, min_max=variables, display_all=True, missing=False) print(table.tabulate(tablefmt="rounded_outline")) ╭───────────────────────────┬────┬────────────────────╮ │ │ │ Overall │ ├───────────────────────────┼────┼────────────────────┤ │ n │ │ 100 │ │ f_exec_a, mean [min,max] │ │ 50.6 [24.5,72.7] │ │ f_exec_e, mean [min,max] │ │ 61.2 [26.6,95.7] │ │ f_exec_o, mean [min,max] │ │ 68.8 [14.5,116.1] │ │ f_exec_dm, mean [min,max] │ │ 79.0 [66.7,91.3] │ │ f_exec_c, mean [min,max] │ │ 89.8 [57.1,122.4] │ │ f_exec_pb, mean [min,max] │ │ 96.3 [45.2,136.2] │ │ fe_score, mean [min,max] │ │ 109.1 [90.2,126.7] │ ╰───────────────────────────┴────┴────────────────────╯ ...but there is currently no way of displaying SD, Range in a single command. It wouldn't be difficult to implement, but we would need to decide a way of making this configurable through the input arguments to TableOne. If you have ideas for handling the argument, let us know. — Reply to this email directly, view it on GitHub <#197 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AH3MOVR3Q6ZD2237LEK2M2T23OX3JAVCNFSM6AAAAAB3WS7HC6VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDQMZSGQYTIMJXG4> . You are receiving this because you authored the thread.Message ID: ***@***.***>

-- Luis Anunciacao, PhD Professor, PUC-Rio Courtesy Professor, University of Oregon M&M Lab <https://labmm.org/> | Scholar <https://scholar.google.com.br/citations?user=dPoJxfoAAAAJ&hl=en> | Lattes <http://lattes.cnpq.br/3982200733248687>