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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

These are energy analytics top areas to focus

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Energy Analytics is a new area started recently. The below are the key points in analytics. According to Siemens, Outsource your energy data management as a service and benefit from regular analyses performed by our energy experts. Evaluations show that this service makes it possible to easily achieve savings potential of as much as 5 percent. Headlines of energy analytics Market Forecast: Energy Data & Analytics Energy Data Management & Analytics Business Skills Big Data in an Utilities Environment: Real Case Studies Advantages of Big Data & Analytics: Smart Metering & Cloud Computing Innovative Solutions & Technologies Data Analytics in a Smart Grid Perspective - DONG Energy Case Study Big Data in the TSO Business Achieving Benefits From Smart Meters Deployment With Advanced Analytics - Return on Experience From Large Scale Deployments in the US Smart Grid Cybersecurity, Frameworks and Standards Adopting Open Source Software in Energy Anal