Native Backend Quickstart

Native Backend Quickstart#

This section describes how to use the native backend of PySummaries to generate the html reports. The relevant function is pandas_to_report_html. Any of the parameters for this function can be passed to get_table_summary to customize the appearance of the table.

Let’s say you have a dataframe “df” (you can display the code generating it). It has multiindexes for rows and columns. This is the way it gets displayed by default.

Hide code cell source 
import sys
sys.path.insert(0, "../..")

import pandas as pd

nums = [["91 (67.9%)", "28 (49.1%)", "7 (50.0%)", "126 (61.5%)"],
["43 (32.1%)", "29 (50.9%)",	"7 (50.0%)", "79 (38.5%)"],
["50.0 (15.9)", "55.1 (17.9)", "65.3 (10.9)", "52.5 (16.7)"],
["52.0 [4.00, 84.0]", "56.0 [14.0, 95.0]", "65.0 [49.0, 86.0]", "54.0 [4.00, 95.0]"],
["91 (67.9%)", "28 (49.1%)", "7 (50.0%)", "126 (61.5%)"],
["43 (32.1%)", "29 (50.9%)",	"7 (50.0%)", "79 (38.5%)"],
["50.0 (15.9)", "55.1 (17.9)", "65.3 (10.9)", "52.5 (16.7)"],
["52.0 [4.00, 84.0]", "56.0 [14.0, 95.0]", "65.0 [49.0, 86.0]", "54.0 [4.00, 95.0]"]]

cols = [("Specific","Alive", ""),
("Specific","Death", "Melanoma death"),
("Specific","Death", "Non-melanoma death"),	
("","Overall", "")]


rows = [("Section 1", "Gender", "Male"), ("Section 1", "Gender", "Female"),
    ("Section 1", "Age", "Mean (SD)"), ("Section 1", "Age", "Median [min max]"),
    ("Section 2", "Gender", "Male"), ("Section 2", "Gender", "Female"),
    ("Section 2", "Age", "Mean (SD)"), ("Section 2", "Age", "Median [min max]")]

df = pd.DataFrame(nums, columns=pd.MultiIndex.from_tuples(cols), index=pd.MultiIndex.from_tuples(rows))

from IPython.core.display import display 

display(df)
Specific
Alive Death Overall
Melanoma death Non-melanoma death
Section 1 Gender Male 91 (67.9%) 28 (49.1%) 7 (50.0%) 126 (61.5%)
Female 43 (32.1%) 29 (50.9%) 7 (50.0%) 79 (38.5%)
Age Mean (SD) 50.0 (15.9) 55.1 (17.9) 65.3 (10.9) 52.5 (16.7)
Median [min max] 52.0 [4.00, 84.0] 56.0 [14.0, 95.0] 65.0 [49.0, 86.0] 54.0 [4.00, 95.0]
Section 2 Gender Male 91 (67.9%) 28 (49.1%) 7 (50.0%) 126 (61.5%)
Female 43 (32.1%) 29 (50.9%) 7 (50.0%) 79 (38.5%)
Age Mean (SD) 50.0 (15.9) 55.1 (17.9) 65.3 (10.9) 52.5 (16.7)
Median [min max] 52.0 [4.00, 84.0] 56.0 [14.0, 95.0] 65.0 [49.0, 86.0] 54.0 [4.00, 95.0]

Not bad … but a bit ugly … let’s improve its appearance with PySummaries pandas_to_report_html function !!!

from pysummaries import pandas_to_report_html  

# convert the pandas dataframe to the nice html representation
# let's add a caption, foot notes and numbers for columns

caption = "Table 1: A demo table"
footers = ["A foot note", "Another foot note"]
strat_numbers = {("Specific","Alive", ""):"134",
    ("Specific", "Death", "Melanoma death"):"57",
    ("Specific", "Death", "Non-melanoma death"):"14",	
    ("", "Overall", ""):"205"}

html_table = pandas_to_report_html(df, strat_numbers=strat_numbers)

# if you are working on jupyternotebook or jupyterbook, 
# you can display the html directly
html_table
Specific
Alive Death Overall

(N=134) 		Melanoma death
(N=57) 		Non-melanoma death
(N=14)
(N=205)
Section 1
Gender
Male 91 (67.9%) 28 (49.1%) 7 (50.0%) 126 (61.5%)
Female 43 (32.1%) 29 (50.9%) 7 (50.0%) 79 (38.5%)
Age
Mean (SD) 50.0 (15.9) 55.1 (17.9) 65.3 (10.9) 52.5 (16.7)
Median [min max] 52.0 [4.00, 84.0] 56.0 [14.0, 95.0] 65.0 [49.0, 86.0] 54.0 [4.00, 95.0]
Section 2
Gender
Male 91 (67.9%) 28 (49.1%) 7 (50.0%) 126 (61.5%)
Female 43 (32.1%) 29 (50.9%) 7 (50.0%) 79 (38.5%)
Age
Mean (SD) 50.0 (15.9) 55.1 (17.9) 65.3 (10.9) 52.5 (16.7)
Median [min max] 52.0 [4.00, 84.0] 56.0 [14.0, 95.0] 65.0 [49.0, 86.0] 54.0 [4.00, 95.0]

You can add custom css and more!. Explore the documentation to see more examples!