Shape of an array

It is the number of elements in each dimension. To be more specific, it is no. of elements along each axis.

arr = np.array([[1,2,3],[4,5,6]])
print(arr.shape) # (2,3)
# 2 rows and 3 columns

x = np.array([1,2,3])
print(x.shape) # (3,)
# 3 rows and 0 columns

`reshape()`

It lets you change the shape of the array.

arr = np.array([[1,2,3],[4,5,6]])
new_arr = arr.reshape(3,2)
print(new_arr)
"""
[[1 2]
 [3 4]
 [5 6]]
"""
# 3 rows and 2 columns

arr = np.array([1,2,3])
new_arr = arr.reshape(3,1)
print(new_arr)
"""
[[1]
 [2]
 [3]]
"""
# 3 rows and 1 column

It is used as placeholder for unknown dimension. NumPy will automatically infer the correct size if its used.

arr = np.array([[1,2,3],[4,5,6]])
new_arr = arr.reshape(3,-1)
print(new_arr)
"""
[[1 2]
 [3 4]
 [5 6]]
"""
# 3 rows and [2] columns

-1 can only be used for 1 dimension

It lets you convert multi-dimensional array into 1-D array.

arr = np.array([[1,2,3],[4,5,6]])
new_arr = arr.flatten()
print(new_arr) # [1 2 3 4 5 6]

An alternative to this is reshape(-1).

arr = np.array([[1,2,3],[4,5,6]])
new_arr = arr.reshape(-1)
print(new_arr) # [1 2 3 4 5 6]