Unit-VI: Introduction to NumPy

1. NumPy Overview

NumPy (Numerical Python) is the fundamental package for scientific computing in Python. It provides:

N-dimensional array object (ndarray)
Mathematical functions for array operations
Tools for working with large datasets

import numpy as np  # Standard import convention

# Create a simple array
arr = np.array([1, 2, 3, 4, 5])
print("Array:", arr)
print("Type:", type(arr))

2. Creating Arrays

NumPy provides several ways to create arrays:

# From a list
arr1 = np.array([1, 2, 3, 4, 5])

# Multi-dimensional array
arr2 = np.array([[1, 2, 3], [4, 5, 6]])

# Using built-in functions
zeros = np.zeros(5)        # Array of zeros
ones = np.ones((2, 3))     # 2x3 array of ones
range_arr = np.arange(5)   # Similar to range()

print("Zeros:", zeros)
print("Ones array:\n", ones)
print("Range array:", range_arr)

3. Array Attributes

Important array attributes:

Attribute	Description	Example
`ndim`	Number of dimensions	`arr.ndim`
`shape`	Dimensions as a tuple	`arr.shape`
`size`	Total number of elements	`arr.size`
`dtype`	Data type	`arr.dtype`

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

print("Dimensions:", arr.ndim)     # 2
print("Shape:", arr.shape)         # (2, 3)
print("Size:", arr.size)           # 6
print("Data type:", arr.dtype)     # int64

4. Data Types

NumPy supports various data types:

# Creating arrays with specific data types
int_array = np.array([1, 2, 3], dtype=np.int32)
float_array = np.array([1.0, 2.5, 3.7], dtype=np.float32)
bool_array = np.array([True, False, True])

# Type conversion
converted = int_array.astype(np.float64)

print("Integer array:", int_array.dtype)
print("Converted to float:", converted.dtype)

Common Data Types: int32, int64, float32, float64, bool_, string_

5. Basic Array Operations

Element-wise operations with arrays:

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

# Element-wise operations
print("Addition:", a + b)      # [5 7 9]
print("Multiplication:", a * 2) # [2 4 6]
print("Dot product:", np.dot(a, b))  # 32

# Universal functions (ufuncs)
print("Square root:", np.sqrt(a))    # [1.         1.41421356 1.73205081]
print("Exponential:", np.exp(a))     # [ 2.71828183  7.3890561  20.08553692]

6. Practice Exercise

Create a 3x3 matrix with values ranging from 1 to 9, then:

Calculate the sum of all elements
Find the mean of each row
Multiply it by a scalar value

# Create 3x3 matrix (1-9)
matrix = np.arange(1, 10).reshape(3, 3)

# 1. Sum of all elements
total_sum = np.sum(matrix)

# 2. Mean of each row
row_means = np.mean(matrix, axis=1)

# 3. Multiply by scalar
scaled = matrix * 2

print("Matrix:\n", matrix)
print("\nSum of all elements:", total_sum)
print("Mean of each row:", row_means)
print("\nMultiplied by 2:\n", scaled)

Unit-VI: Data Analysis with Python