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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 to Use Blockchain in Internet of Things and Real Applications

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You do not need a central authority in the blockchain. The distributed nature of records makes the records visible to all the parties. Encrypted blocks link to each other. You cannot manipulate the stored records in the blockchain.


Blockchain changes
 

Why the peer-to-peer network and the absence of a central authority

  • Peer to peer network makes secure and transparent transactions.
  • The data in the blocks are encrypted.
  • The distributed nature is the main reason for not having central authority in the blockchain.

Blockchain in IoT

  • There are already blockchain applications in the context of the Internet of Things and some vendors have specific solutions to enable the use of blockchain for IoT too, among others increase trust, save costs and speed up transactions. 
  • IBM is a frontrunner, although several vendors and industry initiatives have been launched with new solutions and actual deployments.

References

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