Creating new dataframe conditioned on dataframe columns and series values

Suppose that I have df with index t1, t2, t3 and columns A, B, C, D, E and a series with index t1, t2, t3 with values such that

t1 [A, B, C]
t2 [D, E]
t3 [B, C, D]

How do I create a new dataframe such with index t1, t2, t3 and columns A, B, C, D, E such that each value in the dataframe depends on whether the column value is in the lists of the series. So in this example, I want the dataframe

A B C D E
t1 T T T F F
t2 F F F T T
t3 F T T T F

I know that there are functions like apply and transform for dataframes but these usually apply to each value of the dataframe rather than column values themselves.

You can explode the Series and assign a new column val then set_index and unstack the A..E value column to convert it to column header

out = (s.explode().to_frame('col').assign(val='T')
       .set_index('col', append=True).unstack('col', fill_value='F')
       .droplevel(level=0, axis=1).rename_axis('', axis=1))

print(out)

     A  B  C  D  E
t1   T  T  T  F  F
t2   F  F  F  T  T
t3   F  T  T  T  F

Or you can do it with pd.crosstab

s = s.explode()
out = pd.crosstab(s.index, s).replace({1: 'T', 0: 'F'})

print(out)

col_0  A  B  C  D  E
row_0
t1     T  T  T  F  F
t2     F  F  F  T  T
t3     F  T  T  T  F

Or with Series.str.get_dummies

out = s.str.join(',').str.get_dummies(sep=',')

print(out)

    A  B  C  D  E
t1  1  1  1  0  0
t2  0  0  0  1  1
t3  0  1  1  1  0

Solution

A more understandable approach.

1. Get the index of original series (index = series_t.index)
2. Set the column of the new dataframe (columns = ['A', 'B', 'C', 'D', 'E'])
3. Create an empty dataFrame (df = pd.DataFrame(index=index, columns=columns))
4. Use for loop and if statement set each value of the dataFrame(df)

Code

import pandas as pd

# create original series
series_t = pd.Series(index=['t1', 't2', 't3'], data=[['A', 'B', 'C'], ['D', 'E'], ['B', 'C', 'D']])
print(series_t)

print('----------------------------------------------')

# create the new dataFrame from the original series
index = series_t.index
columns = ['A', 'B', 'C', 'D', 'E']
df = pd.DataFrame(index=index, columns=columns)
for i in index:
    for j in columns:
        if (j in series_t[i]):
            df[j][i] = 'T'
        else:
            df[j][i] = 'F'
print(df)

Output

t1    [A, B, C]
t2       [D, E]
t3    [B, C, D]
dtype: object
----------------------------------------------
    A  B  C  D  E
t1  T  T  T  F  F
t2  F  F  F  T  T
t3  F  T  T  T  F