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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

6 Exclusive Differences Between Structured and Unstructured data

Here's a basic interview question for Big data engineers. Why it's basic means many Bachelor degrees now offering courses on Big data, as a beginner, understanding of data is a little tricky. So interviewers stress this point.

Don't worry, I made it simplified. So you get a clear concept. I share here a total of six differences between these. In today's world, we have a lot of data. That data is the unstructured format.

Structured Vs Unstructured data - 6 Top Differences

Structured Data

  1. The major data format is text, which can be string or numeric. The date is also supported.
  2. The data model is fixed before inserting the data.
  3. Data is stored in the form of a table, making it easy to search.
  4. Not easy to scale.
  5. Version is maintained as a column in the table.
  6. Transaction management and concurrency are easy to support.

Unstructured data

  • The data format can be anything from text to images, audio to videos.
  • The data model cannot be fixed since the nature of the data can change. Consider a tweet message that could be text followed by images and audio.
  • Data is not stored in the form of a table.
  • Very easy to scale.
  • Versioning is at an entire level.
  • Transaction management and concurrency are difficult to support.



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