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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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PostgreSQL is beyond NoSQL database

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The PostegreSQL is a popular database for web applications. You can manage user data in this database conveniently.
#postgreSQL
#postgreSQL

What is PostgreSQL

Web applications started using NoSQL databases. PostgreSQL is updating their database to meet the requirements of web applications. So PostgreSQL is almost equal to NoSQL database.

Java Script

PostgreSQL supports JSON (JavaScript Simple Object Notation) . JSON is portable data format to share data. The MongoDB follows JSON.
PostgreSQL's structured format for saving JSON, called JSONB, eliminates the need for restructuring a document before it is committed to the database.

Benefits 

  1. PostgreSQL is similar to MongoDB to ingest documents 
  2. PostgreSQL follows ACID compatibility 
  3. PostgreSQL have all the features and options to edit JSON data.

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