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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

The awesome points to learn from DB2 NoSQL GraphStore

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 #db2 graphstore: One best example, prior to understanding the RDF format for Graph data model -  If the graph data model is the model the semantic web uses to store data, RDF is the format in which it is written.  Related: Highly Demanding Web Designer Jobs Summary of DB2 Graph Store: DB2-RDF support is officially called "NoSQL Graph Support".   The API extends the Jena API (Graph layer).  Developers familiar with Jena TDB will have the Model layer capabilities they are accustomed to. Although the DB2-RDF functionality is being released with DB2 LUW 10.1, it is also compatible with DB2 9.7. Full supports for SPARQL 1.0 and a subset of SPARQL 1.1.  Full SPARQL 1.1 support (which is till a W3C working draft) will be forthcoming. While RDBMS implementations of RDF graphs have typically been non-performant, that is not the case here*.  Some very impressive and innovative work has been put into optimization capabilities.  Out-of-the box performance is compara