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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

Here is an Audio Post Explained About Blockchain

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According to Investopedia - Originally developed as the accounting method for the virtual currency Bitcoin , blockchains – which use what's known as distributed ledger technology (DLT) – are appearing in a variety of commercial applications today. Distributed Ledger 1 - What is the Current Trend All the transactions currently can be edited by the server owners. They have full control to change the transaction details. The current trend is either centralized or decentralized. 2- Distributed Trend No one can edit the transaction details. It is transparent to all stakeholders. Video on Distributed Systems References What is Centralized Server Processing Role of Distributed Server Processing