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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

Big data real role to help Real estate business

How big data helps real estate is trending today. When people buy real estate and its dependencies you can get from analytics

Advantages of Big-data in Real estate

  1. Study the data from real estate consume
  2. Understand the buyers
  3. Loan dependencies and role of consumers
  4. Sale activities by agents
  5. Sales boost

Role of Big Data

Real estate agents need to check lot of data sources to identify sales pitch and formula to boost sales. The first point is agents should understand the requirements of consumers or buyers of real estate.



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