In [1]:

    

import numpy as np
import pandas as pd


    

In [2]:

    

df = pd.DataFrame(data=[[1, 2, 3], [4, 5, 6]], columns=['a', 'b', 'c'])
print(df)


    

    




   a  b  c
0  1  2  3
1  4  5  6

In [3]:

    

a = df.to_numpy()
print(a)


    

    




[[1 2 3]
 [4 5 6]]

In [4]:

    

print(type(a))


    

    




<class 'numpy.ndarray'>

In [5]:

    

print(np.shares_memory(df, a))


    

    




True

In [6]:

    

a[0, 0] = 100
print(a)


    

    




[[100   2   3]
 [  4   5   6]]

In [7]:

    

print(df)


    

    




     a  b  c
0  100  2  3
1    4  5  6

In [8]:

    

a_copy = df.to_numpy(copy=True)
print(a_copy)


    

    




[[100   2   3]
 [  4   5   6]]

In [9]:

    

print(np.shares_memory(df, a_copy))


    

    




False

In [10]:

    

a_copy[0, 0] = 10
print(a_copy)


    

    




[[10  2  3]
 [ 4  5  6]]

In [11]:

    

print(df)


    

    




     a  b  c
0  100  2  3
1    4  5  6

In [12]:

    

a_cols = df[['a', 'c']].to_numpy()
print(a_cols)


    

    




[[100   3]
 [  4   6]]

In [13]:

    

print(np.shares_memory(df, a_cols))


    

    




False

In [14]:

    

a_f = df.to_numpy(dtype=float)
print(a_f)


    

    




[[100.   2.   3.]
 [  4.   5.   6.]]

In [15]:

    

print(np.shares_memory(df, a_f))


    

    




False