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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: Store Multiple Values in a Variable

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In Python, you can assign values to the variables. This post tells you how to assign values to multiple variables. It is common assigning values in any programming language. But Python simplifies your job by introducing this technic. Here is  Python For Loop Tricky Example . Python Assigning the Values At once. a = 1 b = 1 c = 1 d = 1 or, you can assign simultaneously as; a=b=c=d=1 Python Assigning Multiple Values at Once The other way you can assign as; a, b, c, d = 10, 1, 3.5, 'Srini' From the above, you can understand as below: The '10' assigns to a. The '1' assigns to b. The '3.5' assigns to  c. Then 'Srini' to d.  References Python Basics Variable Assignment
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