Introduction to NumPy

NumPy is a powerful Python library for numerical computations. It provides support for arrays, mathematical functions, and operations that are much faster than traditional Python lists.

In this lesson, you’ll learn how to use NumPy for efficient numerical operations.

Installing NumPy

To install NumPy, use the following command in your terminal:

pip install numpy

Once installed, you can import it in your Python scripts:

# Importing numpy
import numpy as np

print("NumPy imported successfully")

Creating NumPy Arrays

NumPy arrays are faster and more memory-efficient than Python lists. You can create arrays from lists or using built-in functions:

# Creating arrays
import numpy as np

arr = np.array([1, 2, 3, 4])
print(arr)  # Outputs: [1 2 3 4]

Array Operations

NumPy supports element-wise operations, which are much faster than looping through lists:

# Element-wise operations
arr = np.array([1, 2, 3, 4])
print(arr + 10)  # Outputs: [11 12 13 14]

Array Indexing and Slicing

NumPy arrays support advanced indexing and slicing:

# Indexing and slicing
arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])  # Outputs: [20 30 40]

Working with Multi-dimensional Arrays

NumPy supports multi-dimensional arrays (matrices):

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

Mathematical Operations

NumPy provides built-in mathematical functions for operations like mean, median, and standard deviation:

# Mathematical operations
arr = np.array([1, 2, 3, 4, 5])
print(np.mean(arr))  # Outputs: 3.0

Generating Arrays

NumPy can generate arrays with functions like arange and linspace:

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

Common Mistakes with NumPy

Here are some mistakes to avoid:

• Using Python lists for large datasets instead of NumPy arrays.
• Forgetting to check the shape of multi-dimensional arrays before performing operations.
• Not taking advantage of vectorized operations.

What Did We Learn?

In this lesson, you learned:

• How to install and import NumPy.
• The advantages of NumPy arrays over Python lists.
• How to perform array operations, indexing, and slicing.
• How to work with multi-dimensional arrays and built-in mathematical functions.

Great job! Let’s move to the next topic.