How can I construct a Pandas DataFrame from individual 1D Numpy arrays without copying

Question

Unlike every other question I can find, I do not want to create a DataFrame from a homogeneous Numpy array, nor do I want to convert a structured array into a DataFrame.

Answer 1

pandas.DataFrame ({"col": nparray, "col": nparray})

This works if you try list (nparray) instead. Here's a generic example:

import numpy as np
import pandas as pd

alpha = np.array ([1, 2, 3])
beta = np.array ([4, 5, 6])
gamma = np.array ([7, 8, 9])

dikt = {"Alpha" : list (alpha), "Beta" : list (beta), "Gamma":list (gamma)}

data_frame = pd.DataFrame (dikt)
print (data_frame)

Answer 2

May I suggest adding the columns one by one. It might help with efficiency. Like this for example,

import numpy as np
import pandas as pd

df = pd.DataFrame()

col1 = np.array([1, 2, 3])
col2 = np.array([4, 5, 6])

df['col1'] = col1
df['col2'] = col2

>>> df
   col1  col2
0     1     4
1     2     5
2     3     6

Answer 3

I don't think this fully answers the question but it might help.

1-when you initialize your dataframe directly from 2D array, a copy is not made.

2-you don't have 2D arrays, you have 1D arrays, how do you get 2D arrays from 1D arrays without making copies, I don't know.

To illustrate the points, see below:

a = np.array([1,2,3])
b = np.array([4,5,6])
c = np.array((a,b))
df = pd.DataFrame(c)
a = np.array([1,2,3])
b = np.array([4,5,6])
c = np.array((a,b))
df = pd.DataFrame(c)

print(c)
[[1 2 3]
 [4 5 6]]

print(df)
   0  1  2
0  1  2  3
1  4  5  6

c[1,1]=10
print(df)
   0   1  2
0  1   2  3
1  4  10  6

So, changing c indeed changes df. However if you try changing a or b, that does not affect c (or df).