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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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A Quick guide to Amazon RDS

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Amazon Aurora is a MySQL-compatible relational database management system (RDBMS) that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases.

It provides up to 5X the performance of MySQL at one tenth the cost of a commercial database. Amazon Aurora allows you to encrypt data at rest as well as in transit for your mission-critical workloads.

Key points on Amazon Aurora


  1. Amazon Aurora is a relational database engine that combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. It delivers up to five times the throughput of standard MySQL running on the same hardware.
  2. Amazon Aurora is designed to be compatible with MySQL 5.6, so that existing MySQL applications and tools can run without requiring modification. 
  3. Amazon Aurora joins MySQL, Oracle, Microsoft SQL Server, and PostgreSQL as the fifth database engine available to customers through Amazon RDS. 
  4. Amazon RDS handles time-consuming tasks such as provisioning, patching, backup, recovery, failure detection, and repair. You pay a simple monthly charge for each Amazon Aurora database instance you use. There are no upfront costs or long-term commitments.

What is RDS on Amazon Aurora

Amazon RDS makes it easy to manage your Amazon Aurora database by automating most of the common administrative tasks associated with running a database. 

With a few clicks in the AWS Management Console, you can quickly launch an Amazon Aurora database instance. Amazon Aurora scales storage automatically, growing storage and rebalancing I/Os to provide consistent performance without the need for over-provisioning.

For example, you can start with a database of 10GB and have it automatically grow up to 64TB without requiring availability disruptions to resize or restripe data.

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