NumPy Array Attributes

Every NumPy array comes with built-in attributes that describe its structure - its dimensions, size, and the type of data it holds - which are essential to check whenever you're debugging shape mismatches or preparing data for a model.

Key Array Attributes

import numpy as np

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

print(arr.shape)   # (2, 3) - 2 rows, 3 columns
print(arr.ndim)    # 2 - number of dimensions
print(arr.size)    # 6 - total number of elements
print(arr.dtype)   # int64 - the data type of the elements

Reshaping an Array

The reshape() method changes an array's shape without changing its data, as long as the total number of elements stays the same.

import numpy as np

arr = np.arange(12)          # [0, 1, 2, ..., 11]
reshaped = arr.reshape(3, 4)  # reshape into 3 rows, 4 columns
print(reshaped)

flattened = reshaped.flatten()   # convert back to a 1D array

Changing Data Type

import numpy as np

arr = np.array([1, 2, 3])
float_arr = arr.astype(float)
print(float_arr)   # [1. 2. 3.]

Coming Up Next

To close out this section, the final topic covers Matrix Product - how NumPy performs matrix multiplication, a core operation behind many Machine Learning algorithms.