Day 12: NumPy Basics – Powerful Numerical Computing 🚀

NumPy (Numerical Python) is a fundamental library in Python for numerical computations. It’s widely used for handling arrays, performing mathematical operations, and serving as a backbone for libraries like Pandas, TensorFlow, and scikit-learn.

Table of Contents

1. Introduction to NumPy
2. Why NumPy?
3. Key Features
4. Importing NumPy
5. Creating Arrays
6. Array Properties
7. Basic Operations
8. Indexing and Slicing
9. Broadcasting
10. Real-world Example: Temperature Conversion
11. Advantages of NumPy over Python Lists
12. Wrap-Up Day 11: Python for Data Science – Let’s Analyze and Visualize Data!
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    Day 13: Pandas – Data Manipulation and Analysis

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Why NumPy?

• Efficient: NumPy arrays (ndarray) are faster and use less memory than traditional Python lists.
• Versatile: It offers a variety of mathematical functions, random number generation, linear algebra tools, and more.
• Interoperable: Integrates seamlessly with other libraries and systems.

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Key Features

• Multi-dimensional arrays (ndarray).
• Broadcasting for operations on arrays with different shapes.
• Mathematical and statistical functions.
• Efficient memory storage and manipulation.
• Tools for linear algebra, Fourier transforms, and random number generation.

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Importing NumPy

To get started, import the library:

import numpy as np

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Creating Arrays

1. From a List

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

2. Using Built-in Functions

zeros = np.zeros((2, 3))  # Creates a 2x3 array of zeros
ones = np.ones((3, 3))    # Creates a 3x3 array of ones
range_array = np.arange(1, 10, 2)  # Array from 1 to 9 with step 2
linspace_array = np.linspace(0, 1, 5)  # 5 equally spaced values from 0 to 1

3. Random Arrays

rand_array = np.random.rand(3, 3)  # 3x3 array with random values between 0 and 1
rand_ints = np.random.randint(1, 10, size=(3, 3))  # 3x3 array with random integers from 1 to 9

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Array Properties

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)  # (2, 3) - rows and columns
print(arr.size)   # 6 - total elements
print(arr.dtype)  # Data type of elements

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Basic Operations

1. Element-wise Operations

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)  # [5, 7, 9]
print(a * b)  # [4, 10, 18]

2. Matrix Multiplication

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
print(np.dot(a, b))  # Matrix multiplication

3. Statistical Operations

data = np.array([1, 2, 3, 4, 5])
print(data.mean())  # Average
print(data.sum())   # Total sum
print(data.std())   # Standard deviation

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Indexing and Slicing

1. Basic Indexing

arr = np.array([10, 20, 30, 40])
print(arr[2])  # 30

2. Slicing

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

3. Boolean Indexing

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

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Broadcasting

Broadcasting allows operations on arrays of different shapes:

a = np.array([[1, 2], [3, 4]])
b = np.array([10, 20])
print(a + b)
# Output:
# [[11, 22],
#  [13, 24]]

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Real-world Example: Temperature Conversion

Convert a list of temperatures in Celsius to Fahrenheit:

celsius = np.array([0, 20, 30, 40])
fahrenheit = (celsius * 9/5) + 32
print(fahrenheit)  # [32., 68., 86., 104.]

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Advantages of NumPy over Python Lists

Feature	NumPy	Python List
Speed	Faster	Slower
Memory Usage	Optimized	Higher
Functionality	Rich mathematical operations	Limited
Multi-dimensional	Supports n-dimensional arrays	No direct support

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Wrap-Up

NumPy is a powerhouse for numerical computing in Python, offering speed, efficiency, and extensive functionalities. Whether you’re a beginner or an expert, mastering NumPy is crucial for data analysis, machine learning, and scientific computing.

Stay tuned for Day 13 as we dive deeper into Pandas – the ultimate data manipulation library! 🚀

Day 11: Python for Data Science – Let’s Analyze and Visualize Data!
Back to Home
Day 13: Pandas – Data Manipulation and Analysis