Creating arrays from Python objects

From a list

import numpy as np
 
arr = np.array([1, 2, 3, 4])
print(arr)
print(type(arr))

import numpy as np
 
arr = np.array([1, 2, 3, 4])
print(arr)
print(type(arr))

From a nested list (2D)

matrix = np.array([
    [1, 2, 3],
    [4, 5, 6]
])
print(matrix)
print(matrix.shape)   # (2, 3)

matrix = np.array([
    [1, 2, 3],
    [4, 5, 6]
])
print(matrix)
print(matrix.shape)   # (2, 3)

From a tuple

arr = np.array((10, 20, 30))
print(arr)

arr = np.array((10, 20, 30))
print(arr)

Using built-in constructors

np.zeros()np.zeros()

Create an array filled with zeros.

arr = np.zeros((2, 3))
print(arr)

arr = np.zeros((2, 3))
print(arr)

np.ones()np.ones()

arr = np.ones((3, 2))
print(arr)

arr = np.ones((3, 2))
print(arr)

np.full()np.full()

Create an array filled with a constant value.

arr = np.full((2, 2), 7)
print(arr)

arr = np.full((2, 2), 7)
print(arr)

np.eye()np.eye() (identity matrix)

I = np.eye(3)
print(I)

I = np.eye(3)
print(I)

np.arange()np.arange() (range with step)

Similar to Python range()range(), but returns a NumPy array.

arr = np.arange(0, 10, 2)
print(arr)  # [0 2 4 6 8]

arr = np.arange(0, 10, 2)
print(arr)  # [0 2 4 6 8]

np.linspace()np.linspace() (even spacing)

Creates numnum values between start and stop.

arr = np.linspace(0, 1, 5)
print(arr)  # [0.   0.25 0.5  0.75 1.  ]

arr = np.linspace(0, 1, 5)
print(arr)  # [0.   0.25 0.5  0.75 1.  ]

Specifying dtype during creation

arr = np.array([1, 2, 3], dtype=np.float64)
print(arr)
print(arr.dtype)

arr = np.array([1, 2, 3], dtype=np.float64)
print(arr)
print(arr.dtype)

Quick recap: which function to use?

• np.array(...)np.array(...) → convert existing data (lists)
• np.zeros(shape)np.zeros(shape) / np.ones(shape)np.ones(shape) → initialize arrays
• np.full(shape, value)np.full(shape, value) → constant arrays
• np.eye(n)np.eye(n) → identity matrix
• np.arange(start, stop, step)np.arange(start, stop, step) → integer sequences
• np.linspace(start, stop, num)np.linspace(start, stop, num) → precise evenly spaced floats

Next

Continue to: NumPy Data Types (dtypes) to learn how dtypes affect memory, performance, and numeric precision.

🧪 Try It Yourself

Exercise 1 – Create a NumPy Array

Exercise 2 – Array Shape and Reshape

Exercise 3 – Array Arithmetic