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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

Mainframe study materials for interviews

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Mainframe self-study materials provided to refresh quickly before your next interview.   Top Links for Mainframe Self Study Materials - You Can Download Free of Cost VSAM 1.0 REXX 1.0 MVSQuest Second Edition MVSQuest Second Edition – Tools MQSeries 1.0 MAINFRAME Q&A Mainframe reference questions JCL 1.0 ISPF V 1.0 DB2 1.0 IMS 1.0 IDMS 1.0 COBOL 1.0 CICS V1.0 Abend codes in MF ALL interview questions References Mainframe-srini blogs