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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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3 top IT Skills every new IT Professionals learn to progress in software career

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What are the skills needed by the new IT professionals or job seekers who help the  organisation  transition to IT-as-a-Service.  In order  to lead their  organisations  to the cloud, IT professionals must focus on three fundamental areas: Core  Virtualisation  Skill Sets IT professionals must think and operate in the virtual world. No longer can they be tied to the old paradigm of physical assets dedicated to specific users or applications. They must think in terms of “services” riding on top of a fully virtualized infrastructure, and how applications will take advantage of shared resources with both servers and storage. This requires comprehensive skills in both server and storage virtualization technology, and enough experience as a practitioner to understand the intricacies and critical elements of managing virtual platforms. Rules of Old IT and New IT Cross-training Competency Leaders of IT innovation cannot be completely siloed and hyper-focused. Although there will
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