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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 Delete an Item from a Set in Python: Best Example

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Set is a built-in data type in Python. Furthermore, it is an unordered collection without duplicate items. Here are the two methods that explain to delete an item from a Set. Methods to delete an item from a Set discard remove Discrd Vs. Remove discard() will not raise an error if the item to remove does not exist. The remove() will raise an error if the item does not exist. Explanation to discard and remove methods Python program: #Prints all the Set items food = {"pasta", "burger", "hot dog", "pizza"} print(food) # Prints the Set items without pasta food.discard("pasta") print(food) # Prints the Set items without burger and pasta food.remove("burger") print(food) # The next two lines try to remove an item that isn't in the set! food.discard("pasta")  # this will not report an error food.remove("pasta")   # this will report an error The output: {'pasta', 'burger', 'pizza', '
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