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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

Why Amazon Web services AWS Cloud computing is so popular

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Amazon its Cloud computing services started in three stages: S3 (Simple storage service) SQS (Simple Que service) EC2 (Elastic compute cloud) Amazon Web Services was officially revealed to the world on March 13, 2006. On that day, AWS offered the Simple Storage Service, its first service. (As you may imagine, Simple Storage Services was soon shortened to S3.) The idea behind S3 was simple: It could offer the concept of object storage over the web, a setup where anyone could put an object — essentially, any bunch of bytes — into S3. Those bytes may comprise a digital photo or a file backup or a software package or a video or audio recording or a spreadsheet file or — well, you get the idea. S3 was relatively limited when it first started out. Though objects could, admittedly, be written or read from anywhere, they could be stored in only one region: the United States. Moreover, objects could be no larger than 5 gigabytes — not tiny by any means, but certainly smaller than ma