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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

RDBMS Vs Key-value Four Top Differences

This post tells you differences between rdbms and distributed key-value storage.

Rdbms is quite  different from key-value storage.

RDBMS Vs Key-value Four Top Differences

RDBMS (Relational Database)

  1. You have already used a relational database management system — a storage product that's commonly referred to as RDBMS
  2. It is basically a structured data.
  3. RDBMS systems are fantastically useful to handle moderate data.
  4. The BIG challenge is in scaling beyond a single server. 
  5. You can't maintain redundant data in rdbms.
  6. All the data available on single server.
  7. The entire database runs on single server. So when server is down then database may not be available to normal business operations.
  8. Outages and server downs are common in this rdbms model of database.

Key-Value Database

  1. Key-value storage systems often make use of redundancy within hardware resources to prevent outages. This concept is important when you're running thousands of servers because they're bound to suffer hardware breakdowns. 
  2. Multiple copies same data available on multiple servers.
  3. The use of redundancy makes the key-value system always available — and, more importantly, your data is always available because it's protected from hardware outages.
  4. Literally, dozens of key-value storage products are available. Many of them were first developed by so-called webscale companies, such as Facebook and LinkedIn, to ensure that they can handle massive amounts of traffic. 
  5. Currently key-value storages under open source licenses are available. Now you (or anyone else) can use them in other environments too.

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