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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

JSON Material to Download Now

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JSON or JavaScript Object Notation is a lightweight is a text-based open standard designed for human-readable data interchange. Conventions used by JSON are known to programmers which include C, C++, Java, Python, Perl, etc. Photo Credit: Srini JSON Material to Download Now. JSON stands for JavaScript Object Notation. This format was specified by Douglas Crockford. This was designed for human-readable data interchange JSON has been extended from the JavaScript scripting language. JSON filename extension is .json . JSON Internet Media type is application/JSON The Uniform Type Identifier is public.json. JSON Quick Guide to Download JSON Quick Guide download here