BY USING NUMPY I DEMONSTRATE MY SKILLS
This is the boilerplate for the Mean-Variance-Standard Deviation Calculator project. Instructions for building my project can be found at https://www.freecodecamp.org/learn/data-analysis-with-python/data-analysis-with-python-projects/mean-variance-standard-deviation-calculator
Create a function named calculate() in mean_var_std.py that uses Numpy to output the mean, variance, standard deviation, max, min, and sum of the rows, columns, and elements in a 3 x 3 matrix.
The input of the function should be a list containing 9 digits. The function should convert the list into a 3 x 3 Numpy array, and then return a dictionary containing the mean, variance, standard deviation, max, min, and sum along both axes and for the flattened matrix.
The returned dictionary should follow this format:
{
'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]
}
Write your code in mean_var_std.py. For development, you can use main.py to test your code.
The unit tests for this project are in test_module.py. We imported the tests from test_module.py to main.py for your convenience.