Showing posts with the label set comprehension

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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

Python Set comprehension - How to Use it Read now

In python, Set does not allow duplicates, and  you can't modify an existing set with a comprehension. But using the Set comprehension you can create a new Set. Set Comprehension  In addition, the comprehension must result in a valid set.  Likewise Dictionary, a set does not allow entries of the same value. If you try to add values to the set that are already there, it will replace the old one with the new one. Explained syntax Set comprehensions using the {} syntax only exist in Python 3. Before that, you'll have to use the set() function to create and work with sets. You might guess, therefore, that one of the best uses of a set is to eliminate duplicates. In fact, this is one of the most basic forms of set comprehension. Given a list, we can duplicate it as a list with a simple list comprehension like this: Details of logic if we change the list comprehension to a set comprehension, we get the same result, but as a set. That means without duplicates. list_copy = [x for x in o