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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

New Wave in Data Analytics in 2014

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 SrnimfJobs N ow that we’re in the swing of a new year, we’ve taken stock of the data analytics trends that are brewing and developed a list of the Top 5 trends we believe are going to dominate the industry this year. Even if some of them don’t realize their full potential in 2014, it promises to be an important year in which consumer trends and technology innovation will further shape a future in which companies make data-driven decisions. 1. Data Visualization Goes Mainstream In the mid-90s, e-mail introduced the Internet to consumers, made it more accessible, and catalyzed user adoption. Similarly, data visualization will make data analytics more accessible in 2014. Visual analytics allows business users to ask interactive questions of their prepared data sets and get immediate visual responses, which makes the whole process engaging. This trend will democratize access to data and foster a strong data analysis culture where business users will look for data and perform