Pandas pd.DataFrame from loop-data

I am new to Python. I have some data that I get from a loop. cat and be between two and n and TorF will always be (cat*5) or (cat*4). My gold is to create a pd.DataFrame from two lists, like this

cat = ['a', 'b'] 
TorF = [True, True, True, False, False, True, False, False, True, True]

I think my current solution is kind of clumpy with the int((len(man_corr_n)/len(cat))),

import pandas as pd 
data = [[c, *TorF[i:i+int((len(TorF )/len(cat)))]] for i, c in enumerate(cat)]
df = pd.DataFrame(data)

if there a simpler way to do it?

My desired output is

   0     1     2      3      4      5
0  a  True  True   False  False   True
1  b  True  False  True   False   True

Getting the ratio of the two shapes is a good strategy.

I would however use sliding_window_view:

import pandas as pd
from numpy.lib.stride_tricks import sliding_window_view as swv

cat = ['a', 'b']
man_corr_n = [True, True, True, False, False, True, False, False, True, True]

df = pd.DataFrame(swv(man_corr_n, len(man_corr_n)//len(cat))[:len(cat)],
                  index=cat).reset_index()

Or:

view = swv(man_corr_n, len(man_corr_n)//len(cat))[:len(cat)]
df = pd.DataFrame(np.hstack([np.array(cat)[:,None], view]))

Output:

   0     1     2      3      4      5
0  a  True  True   True  False  False
1  b  True  True  False  False   True

edit: ambiguous output

Your provided code and the shown expected output clearly conflict. Using an unambiguous input (TorF = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), your code gives:

   0  1  2  3  4  5
0  a  0  1  2  3  4
1  b  1  2  3  4  5

While your expected output seems to be:

   0  1  2  3  4  5
0  a  0  2  4  6  8
1  b  1  3  5  7  9

In such case, you just need to reshape:

df = pd.DataFrame(np.reshape(np.r_[cat, TorF], (len(cat), -1), order='F'))

# or
df = pd.DataFrame(np.hstack([list(map(list, cat)), np.reshape(TorF, (len(cat), -1), order='F')]))

Output:

   0     1      2      3      4     5
0  a  True   True  False  False  True
1  b  True  False   True  False  True

You could form a Numpy array from the two Lists, reshape, transpose and form the DataFrame:

import numpy as np
import pandas as pd

cat = ['a', 'b']
TorF = [True, True, True, False, False, True, False, False, True, True]
TorF = np.array(cat + TorF)
TorF2 = TorF.reshape(len(TorF)//2, 2)
df = pd.DataFrame(TorF2.T)

giving:

   0     1      2      3      4     5
0  a  True   True  False  False  True
1  b  True  False   True  False  True

pd.DataFrame({'cat':cat*int(len(TorF)/2),'TorF':TorF})\
    .assign(col1=lambda dd:dd.index//2)\
    .set_index(['col1','cat'])\
    .unstack().T\
    .reset_index(level=1).to_numpy()

Output:

   0     1      2      3      4     5
0  a  True   True  False  False  True
1  b  True  False   True  False  True