Is there a pure numpy way that I can use to get to this expected outcome?
Right now I have to use Pandas and I would like to skip it.

import pandas as pd
import numpy as np

listOfDicts = [{'key1': np.array(10), 'key2': np.array(10), 'key3': np.array(44)},
               {'key1': np.array(2), 'key2': np.array(15), 'key3': np.array(22)},
               {'key1': np.array(25), 'key2': np.array(25), 'key3': np.array(11)},
               {'key1': np.array(35), 'key2': np.array(55), 'key3': np.array(22)}]

Use pandas to parse:

# pandas can unpack simply
df = pd.DataFrame(listOfDicts)

# get all values under the same key
xd = df.to_dict('list')

# ultimate goal
np.stack([v for k, v in xd.items() if k not in ['key1']], axis=1)

array([[10, 44],
       [15, 22],
       [25, 11],
       [55, 22]])

# I would like listOfDicts to transform temporarily into this with pure numpy,
# from which I could do basically anything to it:
{'key1': [np.array([10, 2, 25, 35])],
 'key2': [np.array([10, 15, 25, 55])],
 'key3': [np.array([44, 22, 11, 22])]
 }

>Solution :

One way to turn your dataframe into a dictionary of numpy arrays, is to transpose it and use DataFrame.agg() to merge the columns:

import numpy as np
import pandas as pd

listOfDicts = [{'key1': np.array(10), 'key2': np.array(10), 'key3': np.array(44)},
               {'key1': np.array(2),  'key2': np.array(15), 'key3': np.array(22)},
               {'key1': np.array(25), 'key2': np.array(25), 'key3': np.array(11)},
               {'key1': np.array(35), 'key2': np.array(55), 'key3': np.array(22)}]

df.transpose().agg(np.stack, axis=1).to_dict()

# {'key1': array([10,  2, 25, 35]),
#  'key2': array([10, 15, 25, 55]),
#  'key3': array([44, 22, 11, 22])}

If you just want the values, you can pull those out and stack them and then slice and dice with numpy:

np.stack(df.transpose().agg(np.stack, axis=1).values)

# array([[10,  2, 25, 35],
#        [10, 15, 25, 55],
#        [44, 22, 11, 22]])