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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

Cloud Computing: Horizontal Vs. Vertical Scaling

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The purpose of cloud computing is resource utilization. You can scale up the resources in two ways - vertical and horizontal. Adding resources, you can do either horizontally and vertically . The advantages and drawbacks you can find in simple words. Scaling 1. Horizontal Scaling Advantages You can increase workloads in small steps. The upgrade-cost is far less. Scale the system as much as needed. Drawbacks Dependency on software applications is more for Data distribution and parallel processing. On top of that, fewer software applications exist in the market. You May Also Like:  9 Top Services AWS Provided 2. Vertical Scaling Advantages Since it is a single machine, it is easy to manage. On the fly, you can increase workloads. Drawbacks It is expensive. You need a huge investment. The machine should be powerful to take more workloads - future use. Below is the List of Resources that You can do both Horizontal and Vertical Scaling Platform Scaling Network Scaling Container Scaling Dat