NumPy Tutorial for Data Science

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Introduction

NumPy (Numerical Python) is the foundation of scientific computing in Python. It provides multi-dimensional arrays and high-performance functions to operate on them.

1. Installing NumPy

pip install numpy
  

2. Creating NumPy Arrays

import numpy as np

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

3. Multi-Dimensional Array

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

4. Array Properties

• Shape
• Size
• Datatype

print(arr.shape)
print(arr.dtype)
  

5. Array Operations

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

print(a + b)
print(a * b)
print(a.mean())
  

6. Indexing and Slicing

arr = np.array([10, 20, 30, 40])
print(arr[1])       # 20
print(arr[1:3])     # [20, 30]
  

7. Reshaping Arrays

arr = np.arange(6)
arr = arr.reshape(2, 3)
print(arr)
  

8. Mathematical Functions

np.sqrt(arr)
np.sum(arr)
np.std(arr)
  

Conclusion

NumPy is essential for efficient numerical computation and forms the backbone of most data science libraries.