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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

The In-and-Out of Nodes in Blockchain

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Blockchain is a decentralized technology or distributed ledger on which transactions are anonymously recorded. Which means the transaction ledger is maintained simultaneously across a network of unrelated computers or servers called “nodes”, like a spreadsheet that is duplicated thousands of times across a network of computers. The ledger contains a continuous and complete record (the “chain”) of all transactions performed which are grouped into blocks A block is only added to the chain if the nodes, which are members in the blockchain network with high levels of computing power, reach consensus on the next ‘valid’ block to be added to the chain. A transaction can only be verified and form part of a candidate block if all the nodes on the network confirm that the transaction is valid. Related 11 Useful Blockchain Advantages to Read now Blockchain Smart Contract The Perfect Example