Is there a way to convert a Pandas Series where each row contains an array into a 2D numpy array?
The Series looks like the following below when you display it in Python:
[array([ 58., -1., -1., -1., -1.])
array([ 77., 95., -1., -1., -1.])]
I would like to get a numpy matrix that looks like this:
[[ 58., -1., -1., -1., -1.]
[ 77., 95., -1., -1., -1.]]
Is there a simple way to do this? Any help is appreciated!
import pandas as pd
import numpy as np
s = pd.Series([np.array([ 58., -1., -1., -1., -1.]),
np.array([ 77., 95., -1., -1., -1.])])
rslt = np.array(s.tolist())
rslt
Out[16]:
array([[ 58., -1., -1., -1., -1.],
[ 77., 95., -1., -1., -1.]])
If:
s = pd.Series([np.array([ 58., -1., -1., -1., -1.]),
np.array([ 77., 95., -1., -1., -1.])])
Then
s.apply(pd.Series).values
l = [pd.Series([np.array([ 77., 95., -1., -1., -1.]),np.array([ 58., -1., -1., -1., -1.])])]
