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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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Oracle 12C 'Bitmap Index' benefits over B-tree Index

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#Oracle 12C 'Bitmap Index' benefits over B-tree Index: A bitmap index has a significantly different structure from a B-tree index in the leaf node of the index. It stores one string of bits for each possible value (the cardinality) of the column being indexed. Note: One string of BITs means -Each tupple of possible value it assigns '1' bit in a string.So, all the BITs become a string ( This is an example, on which column you created BIT map index) The length of the string of bits is the same as the number of rows in the table being indexed. In addition to saving a tremendous amount of space compared to traditional indexes, a bitmap index can provide dramatic improvements in response time because Oracle can quickly remove potential rows from a query containing multiple WHERE clauses long before the table itself needs to be accessed. Multiple bitmaps can use logical AND and OR operations to determine which rows to access from the table. Although you ca
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