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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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How to Use Arguments in Real Python Programs

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Here are four types of exclusive arguments in python. The arguments are passers to functions. These are Required arguments, Keyword arguments, Default arguments and Variable-length arguments. Here're 4 Important Arguments in Python The arguments supply input to functions . Because these are various types, herein you will know the details and usage of those types. Argument#1: Required arguments Positional arguments are known as  required arguments  and are passed to a function in the correct order. The arguments in the function call should match the number in the function definition. Sample program: def cal(a,b):     """program to calculate sum of 2 numbers"""     sum=a+b     return sum a=int(input("enter 1st number")) b=int(input("enter 2nd number")) print(cal(a,b)) Output enter 1st number 10 enter 2nd number 22 32 ** Process exited - Return Code: 0 ** Press Enter to exit terminal Argument#2: Keyword arguments A default argument is
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