NumPy:

NumPy (Numerical Python) is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these data structures.

1. Installation of NumPy

To install NumPy, use the following command:

pip install numpy

If you are using Jupyter Notebook, install it using:

!pip install numpy

To verify the installation, run:

import numpy as np
print(np.__version__)

2. Creating NumPy Arrays

2.1 Creating a Basic Array

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)

Output: [1 2 3 4 5]

2.2 Creating a 2D Array (Matrix)

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

Output:

[[1 2 3]
 [4 5 6]]

2.3 Creating an Array of Zeros and Ones

zeros = np.zeros((3, 3))
ones = np.ones((2, 4))
print("Zeros Array:\n", zeros)
print("Ones Array:\n", ones)

2.4 Creating an Array with a Range of Numbers

arr = np.arange(1, 10, 2)
print(arr)

Output: [1 3 5 7 9]

2.5 Creating an Array with Linearly Spaced Numbers

arr = np.linspace(0, 10, 5)
print(arr)

Output: [ 0. 2.5 5. 7.5 10. ]

3. Array Attributes

arr = np.array([[1, 2, 3], [4, 5, 6]])
print("Shape:", arr.shape)
print("Size:", arr.size)
print("Data Type:", arr.dtype)
print("Number of Dimensions:", arr.ndim)

4. Reshaping and Resizing

4.1 Reshaping Arrays

arr = np.arange(1, 10)
reshaped_arr = arr.reshape(3, 3)
print(reshaped_arr)

4.2 Flattening an Array

flattened = reshaped_arr.flatten()
print(flattened)

5. Indexing and Slicing

5.1 Indexing

arr = np.array([10, 20, 30, 40, 50])
print(arr[2])

Output: 30

5.2 Slicing

print(arr[1:4])  # Output: [20 30 40]

5.3 Indexing in a 2D Array

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

6. Mathematical Operations

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print(arr1 + arr2)

7. Broadcasting

arr = np.array([1, 2, 3])
scalar = 10
print(arr + scalar)

8. Random Number Generation

8.1 Generating Random Numbers

rand_nums = np.random.rand(3)
print(rand_nums)

9. Linear Algebra with NumPy

A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
print(np.dot(A, B))

10. Saving and Loading Data

10.1 Saving to a File

arr = np.array([1, 2, 3, 4, 5])
np.save("data.npy", arr)

10.2 Loading from a File

loaded_arr = np.load("data.npy")
print(loaded_arr)

Conclusion

NumPy is a powerful library for numerical computing in Python. It provides essential features for handling large datasets, performing mathematical operations, and conducting data analysis efficiently.