1. Load the notebook with commands developed in step 2.1. (click on the link):

https://colab.research.google.com/drive/1Xo-2dWDgL-gmDJH3QmB6b4YMlntgQqtu?usp=sharing

2. Remember from previous section, the graph obtained:

A very useful library is one that automatically searches over a certain range of probability distribution and tries to find the one that best fits the data. First, let's install it.

!pip install fitter

The following will appear:

Collecting fitter Downloading fitter-1.5.2.tar.gz (27 kB) Preparing metadata (setup.py) ... done ...Successfully built fitter Installing collected packages: fitter Successfully installed fitter-1.5.2 

Now, it is possible to employ the command fit just after making a list with the distributions that should be tested.

from fitter import Fitter, get_common_distributions, get_distributions

f = Fitter(weight,

           distributions=['gamma',

                          'lognorm',

                          "beta",

                          "burr",

                          "norm"])

f.fit()

f.summary()

The following results will appear:

Fitting 5 distributions: 100%|██████████| 5/5 [00:01<00:00, 4.33it/s]