Data Analysis with Python Projects - Mean-Variance-Standard Deviation Calculator

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  'mean': [axis1, axis2, flattened],
  'variance': [axis1, axis2, flattened],
  'standard deviation': [axis1, axis2, flattened],
  'max': [axis1, axis2, flattened],
  'min': [axis1, axis2, flattened],
  'sum': [axis1, axis2, flattened]

If a list containing less than 9 elements is passed into the function, it should raise a ValueError exception with the message: “List must contain nine numbers.” The values in the returned dictionary should be lists and not Numpy arrays.

For example, calculate([0,1,2,3,4,5,6,7,8]) should return:

  'mean': [[3.0, 4.0, 5.0], [1.0, 4.0, 7.0], 4.0],
  'variance': [[6.0, 6.0, 6.0], [0.6666666666666666, 0.6666666666666666, 0.6666666666666666], 6.666666666666667],
  'standard deviation': [[2.449489742783178, 2.449489742783178, 2.449489742783178], [0.816496580927726, 0.816496580927726, 0.816496580927726], 2.581988897471611],
  'max': [[6, 7, 8], [2, 5, 8], 8],
  'min': [[0, 1, 2], [0, 3, 6], 0],
  'sum': [[9, 12, 15], [3, 12, 21], 36]


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Challenge: Data Analysis with Python Projects - Mean-Variance-Standard Deviation Calculator

Link to the challenge:

Hi, you’ll need to get start with the first steps. Click the link to the Replit boilerplate code and then:

Start by importing the project on Replit.
Next, you will see a .replit window.
Select Use run command and click the Done button.

Then you can begin coding your solution. If you run into any problems that trip you up for more than a day, you can come to the forum and provide as detailed a description of the problem that you can, and someone will help you out.

Try googling any errors or questions you have first, you will learn more that way, and learn to solve problems on your own, which will be an invaluable skill in the future.

If you’re not sure how to get started I suggest going back over the lessons in the course and maybe that will spark an idea.

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