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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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IT Skills you need to get top jobs in e-commerce

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Technology and digital marketing-related skills are in huge demand as e-commerce companies have got big-ticket investments.

IT Skills for e-comm jobs
IT Skills for e-comm jobs
Technologies in Demand

Domestic consumption of technology, especially in mobile telephony, is on the rise. The traditional IT services sector is likely to see a spurt in hiring as demand revives in Europe, along with expected economic turnaround at home.

Hot in Demand IT areas

According to Teamlease, this category will provide opportunities for a wide range of people:
  • beginning with IT infrastructure managers
  • mobile developers on IOS, Android, and Windows
  • languages like Python, Ruby on Rails, HTML, and CSS
  •  IT security
  • cloud computing professionals.
  • Experience developing applications using technologies such as Java, Node.js, HTML5, JavaScript, SpringBoot, Tomcat, and Selenium.

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