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               This is a branch of mathematics. It collects the data, analyses it and present it accordingly. It has many methods which is used to find sum, average, minimum , maximum and so on. Let us implement the list of functions given below….. Sum: to find the total of all elements. Mean: it find the average value. Median :it gives you the middle value after sorting. Maximum :this function gives you maximum value of the array. Minimum: it provides the minimum value of the array. Standard deviation: it finds the standard deviation value. Variance: it is the square of the standard deviation. Product: it multiplies all elements. Cumulative sum: it gives the running total. Cumulative product: it finds the running product value. Program to find values for all functions: import numpy as np1 s_arr = np1.array([11,12,13,14,15,16]) s_sum= np1.sum(s_arr) s_prod = np1.prod(s_arr) s_mean = np1.mean(s_arr) s_medi = np1....

                              It is one of the advanced indexing methods in Numpy. It makes you to find arbitrary elements in the list or array of index. Let us implement in different ways. ·        1D array ·        Reordering the elements of 1D array ·        2D array ·        Selecting specific elements 👉1D array:               This deals with Single dimensional array. First, it reads the array elements. Find the arbitrary elements. Finally, print the output. Code: import numpy as np1 s_arr = np1.array([11, 22, 33, 44, 55, 66, 77]) indices = [0, 2, 4] result = s_arr[indices] print("The indices are:",result) Output: The indices are: [11 33 55] 👉 Reordering the elements of 1D array:   ...

               It is the advanced indexing technique which makes you to select the elements of an array. It uses a Boolean mask (has two values True/False). Let us create this in 3 different ways as follows. Boolean indexing in 1D array Boolean indexing in 2D array Boolean indexing using Multiple conditions. Each one is explained with sample code as below. 👉Boolean indexing in 1D array               This program reads a single dimensional array as input. It uses a single condition to find Boolean mask. It finds the value and displays the result. 𜰘Code: import numpy as np1 s_arr = np1.array([11, 22, 33, 40, 55 ,66, 75]) mask = s_arr > 33 print("The resultant values are:\n",mask) result = s_arr[mask] print("The result which satisfies the condition s_arr>33:\n",result) 🖵Output: The resultant values are:   [False False False   True ...

 What is Linear algebra?               It is a branch of mathematics which manipulates on matrices, vectors and linear equations.it isa foundation for Science, Engineering, Machine Learning, Data Science and so on.   Key Terms: Linear algebra deals with Scalars, Vectors, Matrices and Linear Equations. Let us discuss each one of it. Scalars: It deals with Single numbers.Eg :-5,3,4.5. Vector: It is an ordered lists of numbers. Matrices : It is a rectangular array of numbers. Linear Algebra: Equations where variables have power 1. Python: import numpy as np1 def main():     try:         # Example matrix         X = np1.array([[1, 1, 2],                       [1, 3, 2],      ...

               Arrays in numpy has variety of operations like element-wise arithmetic, Scalar Operations, Aggregate and advanced functions. The basic operations are already discussed. Now,we discuss about the aggregate and advanced functions. Python Numpy Code for Aggregate and Advanced Operations:               This code starts from importing Numpy library. First,create two arrays with elements. Next,Aggregate functions like sum of all elements,Mean value,max and min values are calculated with built-in functions. Advanced functions like Dot product,square root and exponential are also calculated with built-in function. Code:   import numpy as np # Let us Create two arrays s_arr1 = np.array([1, 3, 5, 7]) s_arr2 = np.array([2, 4, 6, 8]) #Aggregate functions print("Sum:", np.sum(s_arr1))             ...

               Here, we use Scalar multiplications in different ways. Let us try three methods listed as follows.. ·        1D array scalar multiplication ·        2D array scalar multiplication ·        Different scalars for each column The sample code is given below. 1D array scalar multiplication:   This code multiplies the array by 3. First, it creates an array of elements. Each element is multiplied by 3. The result is displayed. Code: import numpy as np1 # Create an array with elements s_arr = np1.array([1, 3, 5, 7, 9, 11, 13, 15]) # Scalar multiplication by 3 result = s_arr * 3 print("Original array:", s_arr)    print("Multiplied by 3:", result) Output: Original array: [ 1   3   5   7   9 11 13 15] Multiplied by 3: [ 3   9 15 21 27 33 39 45] 2D array scalar multiplication: ...