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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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How Micro-services differ from SOA

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Here you will know the differences between microservices and SOA. Both are different architectures.

Micro-services these are Differ to SOA


1. Micro-services 

  • Microservices are interconnected using simple API
  • You can develop highly scalable and modular applications
  • Service-based architecture
  • It is distributed architecture
  • Here, security is a big challenge. Since there is no middleware
  • Functional services, basically this kind
  • No coordination between services.


Service Oriented Architecture

2. SOA

  • Service-based architecture
  • It is distributed architecture
  • Security is good
  • It is an infrastructure kind of service

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