Store numpy.array in cells of a Pandas.DataFrame

I have a dataframe in which I would like to store 'raw' numpy.array:

df['COL_ARRAY'] = df.apply(lambda r: np.array(do_something_with_r), axis=1)

but it seems that pandas tries to 'unpack' the numpy.array.

Is there a workaround? Other than using a wrapper (see edit below)?

I tried reduce=False with no success.

EDIT

This works, but I have to use the 'dummy' Data class to wrap around the array, which is unsatisfactory and not very elegant.

class Data:
    def __init__(self, v):
        self.v = v
        
meas = pd.read_excel(DATA_FILE)
meas['DATA'] = meas.apply(
    lambda r: Data(np.array(pd.read_csv(r['filename'])))),
    axis=1
)

Use a wrapper around the numpy array i.e. pass the numpy array as list

a = np.array([5, 6, 7, 8])
df = pd.DataFrame({"a": [a]})

Output:

a
0  [5, 6, 7, 8]

Or you can use apply(np.array) by creating the tuples i.e. if you have a dataframe

df = pd.DataFrame({'id': [1, 2, 3, 4],
                   'a': ['on', 'on', 'off', 'off'],
                   'b': ['on', 'off', 'on', 'off']})

df['new'] = df.apply(lambda r: tuple(r), axis=1).apply(np.array)

Output :

a    b  id            new
0   on   on   1    [on, on, 1]
1   on  off   2   [on, off, 2]
2  off   on   3   [off, on, 3]
3  off  off   4  [off, off, 4]

df['new'][0]

Output :

array(['on', 'on', '1'], dtype='<U2')

If you first set a column to have type object, you can insert an array without any wrapping:

df = pd.DataFrame(columns=[1])
df[1] = df[1].astype(object)
df.loc[1, 1] = np.array([5, 6, 7, 8])
df

Output:

	1
1	[5, 6, 7, 8]

You can wrap the Data Frame data args in square brackets to maintain the np.array in each cell:

one_d_array = np.array([1,2,3])
two_d_array = one_d_array*one_d_array[:,np.newaxis]
two_d_array

array([[1, 2, 3],
       [2, 4, 6],
       [3, 6, 9]])


pd.DataFrame([
    [one_d_array],
    [two_d_array] ])

                                   0
0                          [1, 2, 3]
1  [[1, 2, 3], [2, 4, 6], [3, 6, 9]]

Suppose you have a DataFrame ds and it has a column named as 'class'. If ds['class'] contains strings or numbers, and you want to change them with numpy.ndarrays or lists, the following code would help. In the code, class2vector is a numpy.ndarray or list and ds_class is a filter condition.

ds['class'] = ds['class'].map(lambda x: class2vector if (isinstance(x, str) and (x == ds_class)) else x)

choose eval buildin function is easy to use and easy to read.

# First ensure use object store str
df['col2'] = self.df['col2'].astype(object)
# read
arr_obj = eval(df.at[df[df.col_1=='xyz'].index[0], 'col2']))
# write
df.at[df[df.col_1=='xyz'].index[0], 'col2'] = str(arr_obj)

real store display perfect human readable value:

col_1,  col_2
xyz,    "['aaa', 'bbb', 'ccc', 'ddd']"

Just wrap what you want to store in a cell to a list object through first apply, and extract it by index 0of that list through second apply:

import pandas as pd
import numpy as np

df = pd.DataFrame({'id': [1, 2, 3, 4],
                   'a': ['on', 'on', 'off', 'off'],
                   'b': ['on', 'off', 'on', 'off']})


df['new'] = df.apply(lambda x: [np.array(x)], axis=1).apply(lambda x: x[0])

df

output:

    id 	a   	b   	new
0  	1 	on 	    on 	    [1, on, on]
1 	2 	on 	    off 	[2, on, off]
2 	3 	off 	on 	    [3, off, on]
3 	4 	off 	off 	[4, off, off]