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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

Why MySQL You Need to Master for Data Analytics Jobs

MySQL Before you can start analysing data, you are going to actually have to have some data on hand. That means a database – preferably a relational one. If you had your sights set on a non-relational, NoSQL database solution, you might want to step back and catch your breath. NoSQL databases are unique because of their independence from the Structured Query Language (SQL) found in relational databases. Relational databases all use SQL as the domain-specific language for ad hoc queries, whereas non-relational databases have no such standard query language, so they can use whatever they want –including SQL. Non-relational databases also have their own APIs designed for maximum scalability and flexibility. When You Need to Learn NoSQL Databases? NoSQL databases are typically designed to excel in two specific areas: speed and scalability. But for the purposes of learning about data concepts and analysis, such super-powerful tools are pretty much overkill. In other words, you