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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
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Infosys Looking for New Opportunities in Cutting Edge Software

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Analytical skills
Analytical skills
"I want us to be there in the great problems that are emerging around artificial intelligence techniques, deep data science and big data techniques, analytics and so on.

New Opportunities


Finding new energy sources, digital oil fields. This is the big thing in the minds of people in the oil and gas industry," Sikka said.

Vishal Sikka also told that Infosys could build a computer like IBM's Watson from scratch. Watson is an artificially intelligent computer system that can answer questions posed in normal language and can be used to help make decisions.

Watson Power


In January this year, IBM announced that it would create a business unit around Watson. IBM CEO Virginia Rometty has said she wants Watson to be a $10 billion business.

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