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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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The Perfect Way to Swap two Strings in Python

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Here is the perfect way swap two strings in python. Without a third variable, you can swap strings in Python. With the swap function, you can achieve this. Here's the sample logic. Swap two strings Multiple arguments you can use in the same function. Here,  a  and b  are arguments for the swap function. You'll get output as swapped when you use the swap function. def swap(a, b):  return b,a  Logic to swap strings. i = "Hello world" j = "This is ApplyBigAnalytics"  (i, j) = swap(i, j)  print(i)  print(j)  Logic to Swap two numbers. i = 1  j = 2  (i, j) = swap(i,j)  print(i)   print(j) Here is output This is ApplyBigAnalytics  Hello world  2  1 Related Posts The Real Purpose of Underscore in Python 
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