[Numpy] How to store different data type in one numpy array?

[paul18fr]

agree with @deanhystad

For instance, if the goal is to recover data per type, I would imagine the following if you can use 2 arrays (possible?):

import numpy as np

Array1 = np.array([['2024-03-22', 71.0, 'ceh'], 
                   ['2024-03-23', 63.0, 'abc'],
                   ['2024-03-24', -50.6, 'zzzzzzz'],
                   ['2024-03-25', 13.8, 'lkj'],
                   ['2024-03-26', 05.2, 'dsfdssss'],
                   [935.2, 'hgjhg', '2024-03-27']                   
                   ])

TypeArray = np.array([['datetime64[D]', 'float64', 'string'],
                      ['datetime64[D]', 'float64', 'string'],
                      ['datetime64[D]', 'float64', 'string'],
                      ['datetime64[D]', 'float64', 'string'],
                      ['datetime64[D]', 'float64', 'string'],
                      ['float64', 'string', 'datetime64[D]']   # !!!!!!!!!!!!!!                  
                     ])


NumberOfTypes = np.unique(TypeArray)


# results are stored in a dictionary PER type but you can proceed differently
RecoveringDictionary = {}

for ntype in NumberOfTypes:
    Index = np.where(TypeArray == ntype)
    Extract = Array1[Index]
    
    if ntype == 'float64': Extract = Extract.astype(np.float64)
    # if ntype == 'datetime64[D]': Extract = Extract.astype(np.datetime64)   
    
    RecoveringDictionary.update({ ntype: Extract, })

    
# print results
for ntype in NumberOfTypes:
    print(f"{ntype} = {RecoveringDictionary[ntype]}\n")

Output:
datetime64[D] = ['2024-03-22' '2024-03-23' '2024-03-24' '2024-03-25' '2024-03-26' '2024-03-27']
float64 = [ 71.   63.  -50.6  13.8   5.2 935.2]
string = ['ceh' 'abc' 'zzzzzzz' 'lkj' 'dsfdssss' 'hgjhg']