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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

Three top big data trainings useful to get deep insights

I have selected 3 top professional training programs for all working engineers. ISB- Indian School of Business:  started a course in "Certificate Program" in Analytics: The use of Business Analytics is widespread across all industries and functions, including Information Technology, Web/E-commerce, Healthcare, Law Enforcement, Banking and Insurance, Biotechnology, Human Resource Management. WIPRO-Started deepening: its Analytical Business with " OPERA SOLUTIONS":Opera Solutions will allow Wipro engineers to offer solutions for customers based on the predictive analytics company's technology platform, according to a senior executive of the New Jersey based company.  SAAS - Software as a service changed INDIA IT indutsry: Empowering non-tech employees.The SaaS revolution in India is also bringing about another important change in organizations. "The CMO today is even more empowered than before," said Gartner's Padmanabhan. "Previously,