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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 10 SCALA Quiz Questions for Programmers

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Scala is an acronym for “Scalable Language” . This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission-critical systems , as many companies, including Twitter, LinkedIn, or Intel does. To some, Scala feels like a scripting language. Its syntax is a concise and low ceremony; its types get out of the way because the compiler can infer them. There are a REPL and IDE worksheets for quick feedback. Developers like it so much that Scala won the ScriptBowl contest at the 2012 JavaOne conference. At the same time, Scala is the preferred workhorse language for many mission-critical server systems. The generated code is on a par with Java’s and its precise typing means that many problems are caught at compile-time rather than after deployment. ✅ SCALA Quiz Link At the root, the language’s scalability is the result of a careful integration of object-oriented and functional language concepts