numpy add dimension to array

Question

I get a python object, which has dimension (10, ). I want to change it to array with shape (10, 1).

For example：

x = np.random.normal(size=(10, 3))
x = x[:, 0]
x.shape
# which is (10, )

y = np.random.normal(size=(10, 1))
y.shape
# which is (10, 1)

z = x + y
z.shape
# which is (10, 10)

How can I accomplish this? plus, why does the z variable above get a (10, 10) shape?

Answer 1

import numpy as np

x = np.random.normal(size=(10, 3))
x = np.array(x[:, 0])
x = np.reshape(x, (10, 1))  # this line added
x.shape
# which is (10, )

y = np.random.normal(size=(10, 1))
y.shape
# which is (10, 1)

z = x + y
z.shape
# which is (10, 10)

Answer 2

You don't need use numpy.array

x = x[:, 0] is equal to x = np.array(x[:, 0])

---

you can indicate that you want an additional dimension and you do not give up explicitly indexing the first column:

x = np.random.normal(size=(10, 3))
x = x[:, 0, None]
x.shape
#(10, 1) 

Or you can use it when you sum

x = np.random.normal(size=(10, 3))
y = np.random.normal(size=(10, 1))
#x[:, 0, None] + y
x = x[:, 0]
x[:, None] + y

We can also indicate that we want everything before the second column, this syntax is shorter but perhaps it is more readable 0 than :1

x = np.random.normal(size=(10, 3))
x = x[:, :1]
x.shape
#(10, 1) 

we could also use additional methods.

np.expand_dims

x = np.random.normal(size=(10, 3))
x = np.expand_dims(x[:, 0], 1)
x.shape
#(10, 1)

np.reshape

x = np.random.normal(size=(10, 3))
x = x[:, 0].reshape(-1, 1)
x.shape
#(10, 1)

Here the -1 allows this to work with another number of rows different from 10

Answer 3

To change the shape you could add this piece of code-

np.reshape(x, (10,1))

Which would resize the x to (10,1) and return it