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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

Google Analytics - Training.PDF

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Google Analytics can track data from a shopping cart on your own or other, domains with the addition of some code. If your website initiates a purchase checkout process on a separate store site (for example, if you send customers from www.mystore.com to www.securecart.com), you just have to add some tracking code to your store site and the shopping cart pages on the host site. The specific code can be found in the Analytics Help Center in the article titled, “How do I use Google Analytics to track a 3rd-party shopping cart?” Read more here: http://static.googleusercontent.com/media/www.google.com/en//grants/education/Google_Analytics_Training.pdf