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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

Top 20 highest paying working from home jobs

U.S. businesses will spend half a billion dollars hiring remote freelance workers in 2014. "The growth is a clear indicator of a transition of work online," says Fabio Rosati, CEO of Elance-oDesk. The company's platform — which serves as a broker between companies that need skills and freelancers eager to supply them — sees 2.7 millio n freelance jo bs posted a year. According to Elance-oDesk , the total earnings of freelancers have grown 50% compounded annually over the past five years. From Rosati's perspective, so many businesses are hiring these workers because they need work done on demand, typically for a short stint. In those cases... Read more