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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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12 Top Tableau Small Projects for Practice

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Tableau is one of the best visualization tools. With Tableau Desktop you can connect to multiple data sources. Tableau Desktop is based on breakthrough technology from Stanford University that lets you drag & drop to analyze data.

Good Tableau Exercises to excell your skills
Best 12 Tableau Exercises:

Small Projects for Practice.


You can connect to data in a few clicks, then visualize and create interactive dashboards with a few more. We’ve done years of research to build a system that supports people’s natural ability to think visually. 

Shift fluidly between views, following your natural train of thought. You’re not stuck in wizards or bogged down writing scripts. 

You just create beautiful, rich data visualizations. It's so easy to use that any Excel user can learn it. Get more results for less effort. And it’s 10 –100x faster than existing solutions

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