Skip to main content

NumPy (Part 1): Introduction to NumPy arrays

Following our Introduction to Data Science in Python, this article introduces NumPy (Part 1): Introduction to NumPy arrays. NumPy is the fundamental package for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.

📚 Prerequisites​

  • Basic understanding of Python lists.

🎯 Article Outline: What You'll Master​

  • ✅ Installation: How to install NumPy.
  • ✅ Core Concepts: What a NumPy array is and how it differs from a Python list.
  • ✅ Creating Arrays: Different ways to create NumPy arrays.
  • ✅ Array Attributes: Understanding the shape, size, and data type of arrays.

🧠 Section 1: The Core Concepts of NumPy Arrays​

A NumPy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension.

NumPy arrays vs. Python lists:

  • Performance: NumPy arrays are much faster and more memory-efficient than Python lists.
  • Homogeneous: All elements in a NumPy array must be of the same data type.
  • Functionality: NumPy provides a large number of functions that operate on arrays.

💻 Section 2: Deep Dive - Implementation and Walkthrough​

2.1 - Installation​

pip install numpy

2.2 - Creating NumPy Arrays​

import numpy as np

# Create a 1D array from a list
a = np.array([1, 2, 3])

# Create a 2D array
b = np.array([[1, 2, 3], [4, 5, 6]])

# Create an array of zeros
c = np.zeros((2, 3))

# Create an array of ones
d = np.ones((3, 2))

# Create an array with a range of elements
e = np.arange(10, 25, 5)

# Create an array of random values
f = np.random.random((2, 2))

2.3 - Array Attributes​

# Shape of the array
print(b.shape)  # (2, 3)

# Number of dimensions
print(b.ndim)   # 2

# Number of elements
print(b.size)   # 6

# Data type of elements
print(b.dtype)  # int64

💡 Conclusion & Key Takeaways​

You've learned the basics of NumPy arrays, including how to create them and how to inspect their attributes.

Let's summarize the key takeaways:

  • NumPy arrays are the foundation of numerical computing in Python.
  • They are faster and more memory-efficient than Python lists.
  • You can create arrays in various ways, including from lists, with zeros or ones, or with random values.

➡️ Next Steps​

In the next article, "NumPy (Part 2): Array indexing, slicing, and operations", we will explore how to work with the data inside NumPy arrays.

Glossary​

  • NumPy: A Python library for numerical computing.
  • Array: A grid of values, all of the same type.
  • Rank: The number of dimensions of an array.
  • Shape: The size of the array along each dimension.

Further Reading​