CODEWITHSUNDEEP
pythonnumpy
Leon palafox
Leon palafox

Reputation: 2785

What does ".T" mean for a Numpy array?

I saw this example in the SciPy documentation:

x, y = np.random.multivariate_normal(mean, cov, 5000).T

What does the final .T actually do here?

Upvotes: 93

Views: 176172

Answers (3)

vk3who
vk3who

Reputation: 389

Example

import numpy as np
a = [[1, 2, 3]]
b = np.array(a).T  # ndarray.T The transposed array. [[1,2,3]] -> [[1][2][3]]
print("a=", a, "\nb=", b)
for i in range(3):
    print(" a=", a[0][i])  # prints  1 2 3
for i in range(3):
    print(" b=", b[i][0])  # prints  1 2 3

Upvotes: 2

Zhassulan Shaikhygali
Zhassulan Shaikhygali

Reputation: 313

.T is just np.transpose(). Best of luck

Upvotes: 15

Sven Marnach
Sven Marnach

Reputation: 602705

The .T accesses the attribute T of the object, which happens to be a NumPy array. The T attribute is the transpose of the array, see the documentation.

Apparently you are creating random coordinates in the plane. The output of multivariate_normal() might look like this:

>>> np.random.multivariate_normal([0, 0], [[1, 0], [0, 1]], 5)  
array([[ 0.59589335,  0.97741328],
       [-0.58597307,  0.56733234],
       [-0.69164572,  0.17840394],
       [-0.24992978, -2.57494471],
       [ 0.38896689,  0.82221377]])

The transpose of this matrix is:

array([[ 0.59589335, -0.58597307, -0.69164572, -0.24992978,  0.38896689],
       [ 0.97741328,  0.56733234,  0.17840394, -2.57494471,  0.82221377]])

which can be conveniently separated in x and y parts by sequence unpacking.

Upvotes: 98

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