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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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Data Science Real Advantages to Read Today

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Real business solution you can get from data science analysis. Your sales is the main requirement. No sales means no business. Data Analytics helps to boost your organization sales.

Real Advantages of Data Science

My presentation here gives you complete business picture of data analytics, and skills companies expecting from analytics team.
  1. Data analytics-The data can be from web, user devices, own databases and Social media
  2. AI-Delivering products based on artificial intelligence
  3. Big data-Data of any format and you need to make ready for analysis.
  4. You can understand patterns of people
  5. You can explore current market
  6. You can explore solutions to Agriculture
  7. You can show answers to weather
  8. Predicting the disaster like earth-quake 

Data Analytics what else you can read


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