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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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These Tips Helpful to Remove Python List and Dictionary Duplicates

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In this post, I have shared top ideas to remove duplicates from the list. Those are with Append and Dictionary. 1. How to Remove Duplicates with Append # Here is a list with duplicates list_with_duplicates = [1,2,3,12,1,2,3,4,5,6,1,2,3,7,8,9] It is simple if you follow the first-approach - brute force approach: list_without_duplicates = [] for pd in list_with_duplicates:   if pd not in list_without_duplicates:       list_without_duplicates.append(pd) print(list_without_duplicates) Result: [1, 2, 3, 12, 4, 5, 6, 7, 8, 9] This method has performance issues when the list is bigger in size.  Real-time. 2. How to Remove Duplicates with Dictionary # Here is you can convert a list to a dictionary dict_without_duplicates = dict(zip(list_with_duplicates, list_with_duplicates)) print(dictionary_without_duplicates) Result: {1: 1, 2: 2, 3: 3, 12: 12, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9} Real-time. Once again, this works and has the advantage of taking less space than duplicating the entire list. 
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