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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

Skills needed for NoSQL Data engineer

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The following Skills are needed to become successful NoSQL data engineer. NoSQL Skills Skills You Need to Become NoSQL Developer 3 + years of practical experience with distributed data analysis systems using parallel processes such as Hadoop. Experience working with batch processing/ real-time systems using various open source technologies like Solr, Hadoop, NoSQL, Spark, Hive, etc. Knowledgeable about data modeling, data access, and data storage techniques Experience in Linux or similar system is preferred Experience on MySQL, NoSQL and storage software is a plus Check here... NoSQL jobs skills and experience required Related:   NoSQL Interview Questions