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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

5 Top features of Sqoop in the age of Big data

The ‘Sqoop’ is a command-line user interface program for conveying information amid relational databases and Hadoop.


It aids increasing stacks of a sole table either a gratis shape SQL request as well like preserved appointments that may be run numerous periods to ingress upgrades produced to a database ever since the final ingress.

Imports may as well be applied to inhabit boards in Apache Hive|Hive either HBase. Exports may be applied to put information as of Hadoop into a relational database.

Apache Foundation

Sqoop grew to be a top-level Apache Software Foundation, Apache program in March 2012. Microsoft utilizes a Sqoop-based connector to aid transference information as of Microsoft SQL Server databases to Hadoop.

Couchbase, Inc. As well delivers a Couchbase Server-Hadoop connector by intents of Sqoop.


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