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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 best answer for 'Efficient Workbook' in Tableau

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There are several factors that define an “efficient” workbook. Some of these factors are technical and some more user-focused. An efficient workbook is:

  • A workbook that takes advantage of the “principles of visual analysis” to effectively communicate the message of the author and the data, possibly by engaging the user in an interactive experience.
  • A workbook that responds in a timely fashion. This can be a somewhat subjective measure, but in general we would want the workbook to provide an initial display of information and to respond to user interactions within a couple of (< 5) seconds. 
  • Tableau latest version is 9.1.2 as on writing this post
  • Tableau version 8 and Version 9 differences
  1. Individual Query time improved by 10x
  2. Dashboard Query times improved by 9x
  3. Query Fusion improving times by 2x
  4. And Query Caching improving times by 50x

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