Day 20 of 30: 📊 NumPy & Data Manipulation

Learn to handle numerical data efficiently with NumPy arrays

May 20, 2025

🔄 Quick Recap (Day 19)

You mastered web scraping: fetching pages with requests, parsing HTML with BeautifulSoup, and handling errors.

🎯 What You’ll Learn Today

Why NumPy arrays outperform Python lists for numerical tasks.
How to create NumPy arrays from lists or range.
Basic array operations: arithmetic, indexing, and slicing.
Common NumPy functions for statistics and reshaping.

📖 Why NumPy?

NumPy is the fundamental package for numerical computing in Python. Its ndarray provides:

Speed: Implemented in C, operations on arrays are vectorized and much faster than Python loops.
Memory efficiency: Arrays are stored compactly in contiguous memory.
Convenience: Do math on entire arrays without explicit loops.

Use NumPy to process large datasets, perform matrix math, and prepare data for libraries like pandas and scikit-learn.

📖 Creating NumPy Arrays

Install NumPy:
```
pip install numpy
```
Import the library:
```
import numpy as np
```

From a Python list:

data = [1, 2, 3, 4, 5]
arr = np.array(data)
print(arr, type(arr))  # array([1,2,3,4,5]) <class 'numpy.ndarray'>

With arange():

arr2 = np.arange(0, 10, 2)  # 0,2,4,6,8
print(arr2)

With zeros() / ones():

zeros = np.zeros(5)
ones = np.ones((2,3))  # 2x3 matrix
print(zeros)
print(ones)

📖 Array Operations & Indexing

NumPy supports elementwise arithmetic:

arr = np.array([1,2,3])
print(arr + 5)    # [6 7 8]
print(arr * 2)    # [2 4 6]

Statistical functions

print(arr.mean())  # 2.0
print(arr.std())   # ~0.816
print(arr.sum())   # 6

Indexing and slicing

matrix = np.arange(9).reshape(3,3)
print(matrix)
# [[0 1 2]
#  [3 4 5]
#  [6 7 8]]
print(matrix[1,2])    # 5
print(matrix[:,1])    # [1 4 7]
print(matrix[0])      # [0 1 2]

🧙‍♂️ Take the Wand and Try Yourself

Create a file numpy_practice.py.
Array creation:
- Build an array of numbers 0–15.
- Reshape it into a 4x4 matrix.
Elementwise math:
- Add 10 to every element.
- Multiply the matrix by 2.
Statistics & slicing:
- Compute the mean of the entire matrix.
- Extract the second column and print it.
Aggregation:
- Compute the sum of each row.

Solution Example (numpy_practice.py):

import numpy as np

# Create and reshape
arr = np.arange(16).reshape(4,4)
print(arr)

# Elementwise operations
arr_plus = arr + 10
print(arr_plus)
arr_times2 = arr * 2
print(arr_times2)

# Statistics & slicing
print("Mean:", arr.mean())
print("2nd column:", arr[:,1])

# Row sums
print("Row sums:", arr.sum(axis=1))

Expected output:

[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]]
[[10 11 12 13]
 [14 15 16 17]
 [18 19 20 21]
 [22 23 24 25]]
[[ 0  2  4  6]
 [ 8 10 12 14]
 [16 18 20 22]
 [24 26 28 30]]
Mean: 7.5
2nd column: [ 1  5  9 13]
Row sums: [ 6 22 38 54]

Run:

python numpy_practice.py

Once your output matches, you’ve harnessed NumPy for powerful numerical computations!

Up next: Day 21: Pandas Essentials

Day 20 of 30: 📊 NumPy & Data Manipulation

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