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

[fmorillas85]

Hi people, so far can get the values, just want to get done the final layout as requested. The dictionary doesnt show the key values plus the lists are shown in vertical and i want to set them as the challenge requires (each axes and flattened outputs horizontal and separated by a comma). Thanks very much

Your code so far
import numpy as np

a = [int(x) for x in input('Intro 9 int separated by a space: ').split()]
if len(a) < 9 or len(a) > 9:
print (‘List must contain nine numbers’)
else: # can be removed
#print(a) #
arr = numpy.asarray(a) #turn list into array/matrix
#print(arr) # can be removed
arr = arr.reshape((3, 3)) # np.arange(9).reshape((3, 3)) for generating generic array from 0 to 8
#print(arr)
m1 = print(np.mean(arr, axis=0))
m2 = print(np.mean(arr, axis=1))
m3 = print(np.mean(arr.flatten()))
thisdict = dict[(
(‘mean’, m1, m2, m3),
(‘variance’, [print(np.var(arr, axis=0)), print(np.var(arr, axis=1)), print(np.var(arr.flatten()))]),
(‘standard deviation’, [print(numpy. std(arr, axis=0)), print(numpy. std(arr, axis=1)), print(numpy. std(arr.flatten()))]),
(‘max’, [print(np.max(arr, axis=0)), print(np.max(arr, axis=1)), print(np.max(arr.flatten()))]),
(‘min’, [print(np.min(arr, axis=0)), print(np.min(arr, axis=1)), print(np.min(arr.flatten()))]),
(‘sum’, [print(np.sum(arr, axis=0)), print(np.sum(arr, axis=1)), print(np.sum(arr.flatten()))]),
)]

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

Link to the challenge:

freeCodeCamp.org

[pkdvalis]

Can you link to the replit?

Problem looks like it’s with all the print statements:

'variance', [print(np.var(arr, axis=0)), print(np.var(arr, axis=1)), print(np.var(arr.flatten()))]),

Try taking those out.

I don’t think the tests will pass with this input, as well, you just need to call the function and pass the 9 digit array as an argument

a = [int(x) for x in input('[Intro 9 int separated by a space: ').split()]

Here’s how you enter test data from main.py:

#print(mean_var_std.calculate([0,1,2,3,4,5,6,7,8]))
# print(mean_var_std.calculate([2,6,2,8,4,0,1,5,7]))

The boilerplate has a function that passes an array as an argument, but you’ve removed that.

def calculate(list):

    #Your code goes here


    return calculations

[fmorillas85]

Thanks for te quick answer pkdvalis

im newby and dont know what you mean with linking to the replit…

I have been looking into it and worked almost exactly as requested

import numpy as np

def calculate(a):  
    if len(a) == 9:
        arr = np.asarray(a) #turn list into array/matrix
        arr = arr.reshape((3, 3)) # np.arange(9).reshape((3, 3)) for generating generic array from 0 to 8        
        meaxis1 = np.mean(arr[ :,0]), np.mean(arr[ :,1]), np.mean(arr[ :,2])
        meaxis2 = np.mean(arr[0]), np.mean(arr[1]), np.mean(arr[2])
        meflat = np.mean([arr])
        varaxis1 = np.var(arr[ :,0]), np.var(arr[ :,1]), np.var(arr[ :,2])
        varaxis2 = np.var(arr[0]), np.var(arr[1]), np.var(arr[2])
        varflat = np.var([arr])
        stdaxis1 = np.std(arr[ :,0]), np.std(arr[ :,1]), np.std(arr[ :,2])
        stdaxis2 = np.std(arr[0]), np.std(arr[1]), np.std(arr[2])
        stdflat = np.std([arr])
        maxaxis1 = np.max(arr[ :,0]), np.max(arr[ :,1]), np.max(arr[ :,2])
        maxaxis2 = np.max(arr[0]), np.max(arr[1]), np.max(arr[2])
        maxflat = np.max([arr])
        minaxis1 = np.min(arr[ :,0]), np.min(arr[ :,1]), np.min(arr[ :,2])
        minaxis2 = np.min(arr[0]), np.min(arr[1]), np.min(arr[2])
        minflat = np.min([arr])
        sumaxis1 = np.sum(arr[ :,0]), np.sum(arr[ :,1]), np.sum(arr[ :,2])
        sumaxis2 = np.sum(arr[0]), np.sum(arr[1]), np.sum(arr[2])
        sumflat = np.sum([arr])
        dict = {
            "mean": [[meaxis1], [meaxis2], meflat],
            "variance": [[varaxis1], [varaxis2], varflat],
            "standard deviation": [[stdaxis1], [stdaxis2], stdflat],
            "max": [[maxaxis1], [maxaxis2], maxflat],
            "min": [[minaxis1], [minaxis2], minflat],
            "sum": [[sumaxis1], [sumaxis2], sumflat]
        }
        return dict
    else:              
        print ('List must contain nine numbers')

a = [int(x) for x in input('[Intro 9 int separated by a space: ').split()]
calculate(a)

Just the format is slightly different

• requested output:
  ‘mean’: [[3.0, 4.0, 5.0], [1.0, 4.0, 7.0], 4.0]
• my output:
  ‘mean’: [[(3.0, 4.0, 5.0)], [(1.0, 4.0, 7.0)], 4.0]

There are some extra brackets i wanna get rid of and sure there are many other things to express better, please let me know!

[fmorillas85]

The link is Mean-Variance-Standard Deviation Calculator - Replit
As well is working on Jupiter notebook but not on replit…

[pkdvalis]

I also work on a notebook (Google Colab) so much easier to test and write!

It looks like some of the variables are tuples () instead of Lists []

Print out meaxis1 and see what it is. When you assign meaxis1 into the dict, make it into a list()

“The values in the returned dictionary should be lists and not Numpy arrays.”

[fmorillas85]

Correct, they are assigned as tuples by default, just used the list() and match exactly now, thanks!