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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par
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How to Use For loop List Option Quickly

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For loop is main concept in Python programming. Out of all the For Loop options, the list option is so popular. If you know, how to use this concept, you can write any number of programs quickly and with less effort. Using for loop you can get index for list items. You can print list elements using for loop. If you take any language , the loops play a vital role. How to Use For Loop When you want to deal with data of multiple values, you need loops. So, For Loop is so famous because, you can control multiple input values. Syntax to Write For Loop for i in my_list: print(i) The 'i' says variable that represents value in the 'my_list'. The for helps to iterate the list values one by one. Example Program For Loop List in Python my_list=[2,3,4] for i in my_list: print(i) Result 2 3 4 How to create Multiplication Table using Python For-Loop List O
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