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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
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Python: Do While Loop Real Examples

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While it is one of the loops in Python. The specialty is it never be false. You already know that in my previous post I have shared For Loop in Python . The for loop can be false. I am giving here one best example: print("Help! My computer doesn't work!") while True: print("Does the computer make any sounds (fans, etc.)") choice = input(" or show any lights? (y/n):") In the above logic, while is always true. When in input user can give 'Y/N'. if choice == 'n': # The computer does not have power print (" Do not show lights") if choice == 'y': # It is power plugged in print ("show lights") So, while is always true. Based on input the while loop works. Python is having the below list of Keywords. Pythons Reserved Words The Python reserved words are: and, exec, not, assert, finally, or, break, for, pass, class, form, print, continue, global, raise, def, if,
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