Day 10 Of Learning Python for Data Science – NumPy Array In Python

NumPy Array in Python is a powerful library for numerical computing in Python. It provides efficient support for multi-dimensional arrays, mathematical functions, and vectorized operations that make computations significantly faster compared to standard Python lists.

Day 9 of Learning Python for Data Science – Queries Related To Functions In Python

Why Learn NumPy?

NumPy is essential for data analysis, machine learning, and scientific computing because:

• It enables fast handling of large datasets.
• It forms the foundation for libraries like pandas, scikit-learn, TensorFlow, and PyTorch.
• It supports vectorized operations, making calculations more efficient than Python loops.

Getting Started with NumPy Array In Python

Importing NumPy

import numpy as np

Creating a 1D Array

arr = np.array([1,2,3,4,5])
print("NumPy array:", arr)

Creating NumPy Array In Python

NumPy provides several ways to create arrays:

2D Array using Nested Lists

arr_2d = np.array([[1, 2], [3, 4]])
print(arr_2d)

Creating Arrays with Default Values

# Array of zeros
zeros = np.zeros((2,3))
print(zeros)

# Array of ones
ones = np.ones((3,3))
print(ones)

Creating Arrays with a Range of Numbers

range_array = np.arange(0, 10, 2)  # 0 to 10 with step 2
print(range_array)

Reshaping Arrays

range_array = np.arange(0, 24, 2).reshape(3,4)
print(range_array)

Defining Data Type while Creating an Array

zeros = np.zeros((2,3), dtype=int)
print(zeros)

Array Operations

NumPy Array In Python allows easy mathematical operations on arrays:

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

# Element-wise addition
print(a + b)

# Element-wise multiplication
print(a * b)

# Scalar multiplication
print(a * 2)

Other operations:

# Addition
ad = np.add(a, b)
print(ad)

# Multiplication
ml = np.multiply(a, b)
print(ml)

# Power
p = np.power(a, 2)
print(p)

Indexing, Slicing, and Reshaping

Accessing Elements

arr = np.array([1,2,3,4,5,6])
print(arr[0])  # First element
print(arr[-1]) # Last element

Slicing

print(arr[1:5])

Reshaping

arr.reshape(2,3)

Mathematical and Statistical Functions

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

# Sum
print("Sum:", a.sum())

# Mean
print("Mean:", a.mean())

# Min and Max
print("Min:", a.min())
print("Max:", a.max())

# Standard Deviation
print("Std. Dev:", a.std())

# Unique Elements
print("Unique:", np.unique(a))

# Square Root
print("Square Root:", np.sqrt(a))

Data Manipulation and Filtering

Finding Unique Values and Their Frequencies

data = np.array([25, 32, 45, 28, 35, 40, 38, 22, 30, 42, 42, 100])
unique, count = np.unique(data, return_counts=True)
print(unique, count)

Filtering Data Based on Conditions

data = np.array([25, 32, 45, 28, 35, 40, 38, 22, 30, 42, 42, 100])
filtered_data = data[data > 30]
print(filtered_data)

Replacing Missing Values

data = np.array([18,22,np.nan,28,23,np.nan,30])
mean = np.nanmean(data)
data[np.isnan(data)] = mean
print(data)

Replacing Negative Values with Zero

data = np.array([-5, 12, -9, 45, -23, 30])
data[data < 0] = 0
print(data)

Conditional Replacement using np.where

data = np.array([10,55,25,70,40,80])
r = np.where(data > 50, 100, data)
print(r)

7. Practice Questions

Beginner Level

1. Create a 1D array with numbers from 10 to 50.
2. Create a 3×3 matrix with values ranging from 0 to 8.
3. Reverse an array of numbers from 1 to 10.
4. Find the mean and standard deviation of an array.
5. Replace all even numbers in an array with -1.
6. Find the unique elements and their frequencies in a dataset.
7. Extract all numbers greater than 25 from an array.
8. Reshape an array of size 16 into a 4×4 matrix.
9. Replace NaN values in an array with the column mean.
10. Find the cumulative sum of a given array.

Intermediate Level

11. Multiply two matrices using NumPy.
12. Normalize an array between 0 and 1.
13. Stack two arrays vertically and horizontally.
14. Compute the dot product of two vectors.
15. Create an identity matrix of size 5×5.
16. Replace values in an array based on a condition using np.where().
17. Create an array of 20 random numbers and find the top 5 highest values.
18. Perform element-wise division of two arrays and handle division by zero.
19. Extract the diagonal elements of a square matrix.
20. Convert a NumPy array to a Python list.

Advanced Level

21. Find the determinant and inverse of a matrix.
22. Create a random matrix and sort its rows by the second column.
23. Implement a moving average on a 1D NumPy array.
24. Create a function that removes outliers from an array using standard deviation.
25. Perform one-hot encoding on a categorical array.
26. Generate a 10×10 matrix with random values and normalize its columns.
27. Compute the Pearson correlation coefficient between two arrays.
28. Perform matrix multiplication without using NumPy’s built-in function.
29. Create a NumPy-based simple linear regression model.
30. Implement a function to find the Euclidean distance between two points using NumPy.

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