Error while creating a pandas dataframe out of numpy ndarrays

I'm trying to concatenate numpy ndarrays of size (19200,) to make a data frame. Each 1D array would be a row in my data frame. My code looks like that:

new_array_1 = pd.DataFrame(new_array_1, index=['new_array_1'])
new_array_2 = pd.DataFrame(new_array_2, index=['new_array_2'])
new_array_3 = pd.DataFrame(new_array_3, index=['new_array_3'])

df = pd.concat([df, new_array_1, new_array_2, new_array_3])

but I got the error:

Shape of passed values is (19200, 1), indices imply (1, 1)

But then after I add the square brackets around the new_arrays like this:

new_array_1 = pd.DataFrame([new_array_1], index=['new_array_1'])
df = pd.concat([df, new_array_1])

and I got the error:

Must pass 2-d input. shape=(1, 1, 19200)

What should I do to solve the problem, please?
Please note that I cannot add all my arrays at the same time, I update my data frame by adding the rows of data whenever I got the data.

Try creating them as a Series instead of a DataFrame, as they are all individuals for now:

s1 = pd.Series(np.array([1,2,3,4]), name='Array_1')
s2 = pd.Series(np.array([5,6,7,8]), name='Array_2')

Then you can use Pandas to concatenate them into a new DataFrame:

df = pd.concat([s1, s2], axis=1)
# Output
# Array_1   Array_2
#   1	      5
#   2	      6
#   3	      7
#   4         8

If all of your arrays are of the same length, you don't need to use concat at all:

array_1 = np.array([1,2,3,4])
array_2 = np.array([5,6,7,8])

df = pd.DataFrame({'Array_1': array_1, 'Array_2': array_2})