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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

Trending IT Skills Grab Opportunities

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Great demand for Mobile developers and Security professionals. Read my other article on "15 Rapidly Growing IT Jobs". Growing areas are in Internet of things and 3D printing. Then we need to know what is 3D printing? 3D printing is not necessarily transforming IT. It’s that it's a new technology that's transforming other industries like manufacturing and biomedical sectors," Laura McGarrity, vice president of digital marketing strategy for Mondo, told eWEEK. "It does create new job growth within the IT industry. Benefits to consumers include the ability to try and re-work prototypes before going to the market - consumers can expect products made for their specific needs. An excellent growth for Data related and Statistics jobs. Data and statistics are everywhere now, and there is high demand for scientists who can analyze the data," she said. "A data scientist needs to be able to manage data, create predictions, and communicate the res