How to transform pandas df containing boolean columns into a heatmap compatible table?-CodePudding

I have a table something like this:

	A	B	C
1	1	1	1
2	0	1	1
3	0	0	1

Where each cell is containing whether the row is connected to the specified column.

I want to transform this into a table like this:

	A	B	C
A	1	1	1
B	1	2	2
C	1	2	3

Where each cell contains the number of rows (from the original table) that has both connected to the specific column and row name in the second table.

For example, the 3 in the second table means that there are 3 rows in the original table that is connected to the B and C columns.

The goal is to plot a heatmap from the second table using plotly.

CodePudding user response：

code:

import pandas as pd
import numpy as np 
import plotly.express as px



# data for data frame
data = {'A': {0: 1, 1: 0, 2: 0}, 
        'B': {0: 1, 1: 1, 2: 0}, 
        'C': {0: 1, 1: 1, 2: 1}}

# create dataframe
df = pd.DataFrame(data)

# list for our combination values
values = []

# loop over each column combination - 
# AA, AB, AC, BA, BB, BC, CA, CB, CC
for row in df.columns:
    # create a list for each row value
    # first iteration [AA, AB, AC]
    # Second iteration [BA, BB, BC]
    # third iteration [CA, CB, CC]
    temp_val = []
    for col in df.columns:
        # get number of rows that are connected 
        val = sum(df[row] & df[col])
        # add to temp list
        temp_val.append(val)
    # add the row to all rows list
    values.append(temp_val)

# create data frame
heat_df = pd.DataFrame(values, index=df.columns, columns=df.columns)

# plot heatmap
fig = px.imshow(heat_df)
fig.show()

Dataframe output:

    A   B   C
A   1   1   1
B   1   2   2
C   1   2   3

Heatmap: