I was just looking at the following numpy infographic.
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf
I am wondering if there is any difference between np.copy(a) and a.copy() - or are they just synonyms for the same operation?
If a is a numpy.array, the result will be the same. But if a is something else, a.copy() will return the same type as a or fail depending on its type, and np.copy(a) will always return numpy.array. Try, e.g. the following:
import pandas as pd
for x in (list(range(3)), np.array(range(3)), pd.Series(range(3))):
print()
print(repr(x.copy()))
print(repr(np.copy(x)))
UPD: There is another difference. Both methods have an additional order argument defining the memory order in the copy with different default values. In np.copy it is 'K', which means "Use the order as close to the original as possible", and in ndarray.copy it is 'C' (Use C order). E.g.
x = np.array([[1,2,3],[4,5,6]], order='F')
for y in [x, np.copy(x), x.copy()]:
print(y.flags['C_CONTIGUOUS'], y.flags['F_CONTIGUOUS'])
Will print
False True
False True
True False
And in both cases the copies are deep in the sense that the array data itself are copied, but shallow in the sense that in case of object arrays the objects themselves are not copied. Which can be demonstrated by
x = np.array([1, [1,2,3]])
y = x.copy()
z = np.copy(x)
y[1][1] = -2
z[1][2] = -3
print(x)
print(y)
print(z)
All the three printed lines are
[1 list([1, -2, -3])]
a is of type np.array, do both methods np.copy(a) and a.copy() make deep copies?
copy.deepcopy.