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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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Here is Sample Logic to get Random numbers in Bash

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How to generate random number



Here's a bash script to generate a random number. You can use this logic to generate a random number, and it is useful for AWS engineers.

Random number


Script - Here's sample logic to get a random number



RANDOM=$$ # Set the seed to the PID of the script
UPPER_LIMIT=$1
RANDOM_NUMBER=$(($RANDOM % $UPPER_LIMIT + 1))
echo "$RANDOM_NUMBER"



If you select UPPER_LIMIT as 100, then the result would be a pseudo-random number between 1 and 100.

Her is the output after executing the script





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