The Quick and Easy Way to Analyze Numpy Arrays

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
Hello Srini
ReplyDeleteJust read your article on vault -v- vaultless, this question can only be answered depending on the vault itself - was it built to be scalable? Does it store every transaction? Quite simply no it does not, but like i say it all depends on how the vault was built. Is it more secure than vaultless - definately.
Vault-less is reversible security method that replaces sensitive data with fake data that looks and feels just like the real thing. So vault-less is advanced than Vault.
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