ML/numpy_Pract at main · dineshkalal/ML · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48

Basics of NumPy

    Create a 1D NumPy array with elements from 1 to 10.
    Create a 3x3 NumPy array filled with zeros.
    Create a 4x4 identity matrix using NumPy.
    Generate a 5x5 array filled with random numbers between 0 and 1.
    Create a NumPy array of 10 elements, all initialized to the value 7.
    Reshape a 1D array of size 12 into a 3x4 2D array.
    Find the dimensions, shape, and size of a given NumPy array.
    Extract all even numbers from a NumPy array [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].

Indexing and Slicing

    Access the second row and third column of a 2D array.
    Slice a 3x3 array to extract the top-left 2x2 sub-array.
    Replace all negative elements in an array with zero.
    Reverse the rows of a given 2D array.
    Retrieve all elements in an array greater than a given value 3.

Array Operations

    Perform element-wise addition, subtraction, multiplication, and division of two arrays.
    Compute the dot product of two 2D arrays.
    Normalize an array so that its values lie between 0 and 1.
    Calculate the cumulative sum of elements in a NumPy array.
    Compute the determinant and inverse of a 2x2 matrix.

Statistical and Mathematical Functions

    Calculate the mean, median, standard deviation, and variance of a NumPy array.
    Find the index of the maximum and minimum values in a NumPy array.
    Sort a NumPy array in ascending and descending order.
    Generate 10 random integers between 50 and 100 and find their sum.
    Compute the row-wise and column-wise sum of a 3x3 matrix.

Boolean Indexing and Masking

    Create a mask for elements greater than a specified value in an array.
    Replace all elements in an array that are divisible by 3 with -1.
    Extract all unique elements from a NumPy array.

Advanced Topics

    Perform matrix multiplication on two matrices and verify the result manually.
    Use NumPy to solve a system of linear equations.
    Write a function to calculate the Fibonacci sequence using NumPy arrays.
    Generate a 2D grid of coordinates for plotting (e.g., using np.meshgrid).