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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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Top Tips You Need to Create Mobile Apps

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Before you start creating mobile applications, you need to understand two things. There are two mobile application types. Namely Native and Web applications.
Mobile applications are two types.Those are Native and Web applications. 
Mobile applications
Photo Credit: Srini

Native applications

  • Each mobile operating system you need to create one version. Native applications are platform dependent. The disadvantage is you need to create multiple versions.
  • Develop apps only on that platform
  • These apps not portable

Web applications

  1. The web applications are platform independent. They work for all mobile operating systems. These applications are browsable using any popular browsers.
  2. These apps are browsable from any popular web browsers

4 Top Mobile Platforms

  1. iOS 
  2. Android 
  3. Windows Phone 7 
  4. Blackberry OS 

4 Top Tablet Platforms

  1. iOS 
  2. Android 
  3. Blackberry OS 
  4. Windows 8

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