Creating NumPy Arrays

From Python Lists

The most common way to create arrays is from lists.

import numpy as np

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

Output: [1 2 3 4 5]

Or directly:

import numpy as np

arr = np.array([10, 20, 30, 40])
print(arr)

2D Arrays (Matrices)

Create arrays with rows and columns.

import numpy as np

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

Output:

What this creates: 2 rows, 3 columns. Like a spreadsheet.

Check dimensions:

print("Shape:", matrix.shape)
print("Rows:", matrix.shape[0])
print("Columns:", matrix.shape[1])

Output:

Array of Zeros

Create array filled with zeros.

import numpy as np

zeros = np.zeros(5)
print(zeros)

Output: [0. 0. 0. 0. 0.]

2D zeros:

zeros_2d = np.zeros((3, 4))
print(zeros_2d)

What this creates: 3 rows, 4 columns, all zeros.

Why use zeros:

• Initialize data before filling
• Create placeholder arrays
• Start calculations at zero

Array of Ones

Create array filled with ones.

import numpy as np

ones = np.ones(4)
print(ones)

Output: [1. 1. 1. 1.]

2D ones:

ones_2d = np.ones((2, 3))
print(ones_2d)

Use cases:

• Initialize weights in machine learning
• Create masks
• Probability calculations

Array with Range

Create sequences of numbers.

Using arange()

import numpy as np

numbers = np.arange(10)
print(numbers)

Output: [0 1 2 3 4 5 6 7 8 9]

With start and stop:

numbers = np.arange(5, 15)
print(numbers)

Output: [5 6 7 8 9 10 11 12 13 14]

With step:

evens = np.arange(0, 20, 2)
print(evens)

Output: [0 2 4 6 8 10 12 14 16 18]

What step does: Jump by 2 each time instead of 1.

Using linspace()

Create evenly spaced numbers.

import numpy as np

numbers = np.linspace(0, 10, 5)
print(numbers)

Output: [0. 2.5 5. 7.5 10.]

What this creates: 5 numbers evenly spaced between 0 and 10.

Difference between arange and linspace:

• arange: You set the step size
• linspace: You set how many numbers you want

Array with Same Value

Fill array with specific value.

import numpy as np

fives = np.full(6, 5)
print(fives)

Output: [5 5 5 5 5 5]

2D with same value:

matrix = np.full((3, 3), 7)
print(matrix)

Creates: 3x3 matrix filled with 7.

Identity Matrix

Special matrix with 1s on diagonal, 0s elsewhere.

import numpy as np

identity = np.eye(4)
print(identity)

Output:

Why identity matrix:

• Linear algebra operations
• Matrix multiplication
• Machine learning transformations

Random Arrays

Create arrays with random numbers.

Random Floats (0 to 1)

import numpy as np

random_floats = np.random.random(5)
print(random_floats)

Output: [0.34 0.78 0.12 0.91 0.56] (different each time)

Random Integers

import numpy as np

random_ints = np.random.randint(1, 100, size=10)
print(random_ints)

What this creates: 10 random integers between 1 and 99.

Parameters:

• 1: Minimum value (included)
• 100: Maximum value (excluded)
• size: How many numbers

Random Normal Distribution

import numpy as np

normal = np.random.randn(5)
print(normal)

What this creates: Random numbers following normal distribution (bell curve).

2D random array:

random_2d = np.random.random((3, 4))
print(random_2d)

Creates: 3 rows, 4 columns of random floats.

Specifying Data Type

Control the type of numbers in array.

import numpy as np

integers = np.array([1, 2, 3], dtype=int)
print(integers.dtype)

floats = np.array([1, 2, 3], dtype=float)
print(floats.dtype)

print(floats)

Output:

Common data types:

• int: Whole numbers
• float: Decimal numbers
• bool: True/False
• str: Text

Converting Lists to Arrays

import numpy as np

python_list = [5, 10, 15, 20]
numpy_array = np.array(python_list)

print("List type:", type(python_list))
print("Array type:", type(numpy_array))

Why convert:

• NumPy operations are faster
• Access to NumPy functions
• Better for numerical work

Practice Example

The scenario: Create different arrays for data analysis project.

import numpy as np

student_ids = np.arange(1, 101)
print("Student IDs:", student_ids[:10])
print("Total students:", len(student_ids))

initial_scores = np.zeros(100)
print("Initial scores (first 5):", initial_scores[:5])

attendance = np.ones(100)
print("Attendance initialized:", attendance[:5])

test_scores = np.random.randint(60, 100, size=100)
print("Random test scores (first 10):", test_scores[:10])

grade_weights = np.array([0.3, 0.3, 0.4])
print("Grade weights:", grade_weights)
print("Weights sum:", np.sum(grade_weights))

percentiles = np.linspace(0, 100, 11)
print("Percentiles:", percentiles)

grade_matrix = np.zeros((100, 3))
print("Grade matrix shape:", grade_matrix.shape)
print("(100 students, 3 assignments)")

random_sample = np.random.choice(student_ids, size=10, replace=False)
print("Random sample of 10 students:", random_sample)

What this creates:

1. Student IDs from 1 to 100
2. Initial scores of zero for all students
3. Attendance array (all present = 1)
4. Random test scores between 60-99
5. Grade weights for different assignments
6. Percentile markers from 0 to 100
7. Empty grade matrix for recording scores
8. Random sample of 10 student IDs

Array from Function

Create array using a function.

import numpy as np

def square(x):
    return x * x

numbers = np.arange(5)
squared = np.array([square(x) for x in numbers])
print(squared)

Or use NumPy's way:

numbers = np.arange(5)
squared = numbers ** 2
print(squared)

Output: [0 1 4 9 16]

Combining Arrays

Join multiple arrays together.

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
combined = np.concatenate([arr1, arr2])
print(combined)

Output: [1 2 3 4 5 6]

Key Points to Remember

Create arrays from lists with np.array([1, 2, 3]). Most common method.

Use np.zeros() and np.ones() to create arrays filled with 0s or 1s. Great for initialization.

np.arange() creates sequences with step size. np.linspace() creates sequences with specific count.

np.random.random() creates random floats, np.random.randint() creates random integers.

Use shape attribute to check dimensions. (rows, columns) for 2D arrays.

Common Mistakes

Mistake 1: Wrong parentheses for 2D

zeros = np.zeros(3, 4)  # Error!
zeros = np.zeros((3, 4))  # Correct - note double parentheses

Mistake 2: arange doesn't include end

arr = np.arange(1, 10)  # Goes from 1 to 9, not 10
arr = np.arange(1, 11)  # This gets 1 to 10

Mistake 3: linspace includes end

arr = np.linspace(0, 10, 5)  # Includes both 0 and 10

Mistake 4: Random seed not set

np.random.random(5)  # Different every time

For reproducible results:

np.random.seed(42)
np.random.random(5)  # Same results every time

What's Next?

You now know how to create NumPy arrays in many ways. Next, you'll learn about array attributes and methods - properties like shape, size, and useful operations you can perform on arrays.