I dont know if I put the question correctly.. For example, I want
1
0
1
0
1
0
1
0
change into
1 0 1
0 1 0
1 0 x
The first list should not be changed.. and change the type to DataFrame..
I try use numpy.array, flatten the array. and reshape to columns using reshape(-1,3).T .. but since there are some missing value to it.. I cannot reshape the array properly..
A possible solution would be to add the missing values to the array before resizing.
Starting point:
import numpy as np
import pandas as pd
# I assume you flattened the array.
data = np.array([1, 0, 1, 0, 1, 0, 1, 0, ])
Adding the new data based on the required shape and fill value:
new_shape = (3, 3)
fill_value = np.NaN
missing_length = np.product(new_shape) - data.size
missing_array = np.full(missing_length, fill_value)
data = np.hstack([data, missing_array])
Then apply the reshape and convert it to a dataframe:
data = data.reshape(new_shape)
df = pd.DataFrame(data)
print(df)
output:
0 1 2
0 1.0 0.0 1.0
1 0.0 1.0 0.0
2 1.0 0.0 NaN