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The Quick and Easy Way to Analyze Numpy Arrays

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

Why You need DevOps: Quick 5 Minutes Quiz

Here's a quiz According to EDUREKA, DevOps is a philosophy, a cultural shift that merges operations with development and demands a linked toolchain of technologies to facilitate collaborative change.

DevOps Quiz

Why DevOps needed?

Since the DevOps philosophy is still at a very nascent stage, the application of DevOps, as well as the bandwidth required to adapt and collaborate, varies from organization to organization, yet we can talk about a winning formula of skills that can present you as a perfect candidate for any type of organization.

Role of DevOps

Which Skill DevOps Engineer should have?

ON TOP OF web languages such as Ruby, Python, PHP, or Java, the ideal DevOps engineer should have some experience using infrastructure automation tools like Chef, Puppet, Ansible, SaltStack or Windows PowerShell DSC.


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