Create a dataframe using prints in Python

Question

I have a code with for loop like below and it ends with some if statements;

def distribution_selection(csv_file=None,product_column=None,demand=None):
    out = [] 

    for num in df_all[product_column]:
            .
            .
            .

    if best_fit=="norm":
        print("Product:",num)
        print("Best fit:",best_fit)
        print("Likelihood:", likelihoods[best_fit])
        print("Parameters:", mean,std)

    if best_fit=="nbinom": 
        print("Product:",num)
        print("Best fit:",best_fit)
        print("Likelihood:", likelihoods[best_fit])
        print("Parameters:", p_nbinom,r_binom)

    if best_fit=="poisson": 
        print("Product:",num)
        print("Best fit:",best_fit)
        print("Likelihood:", likelihoods[best_fit])
        print("Parameters:", lambda_)

And let's say here's the results;

Product: 001.001
Best fit: nbinom
Likelihood: 6.317496035718443e-15
Parameters: 0.002660521439486909 0.41659311972644725
Product: 001.002
Best fit: nbinom
Likelihood: 5.902081129467898e-18
Parameters: 0.005335820123825622 0.7249662663271113
Product: 001.003
Best fit: nbinom
Likelihood: 2.871871246304317e-13
Parameters: 0.00743701201046538 0.45081292375812926
Product: 001.004
Best fit: poisson
Likelihood: 0.0002870492567273848
Parameters: 15.333333333333334

How can I make print output dataframe as follows? Could you please help me about this?

Products     BestFit      Likelihood              ParameterA             ParameterB
001.001      nbinom       6.317496035718443e-15   0.002660521439486909   0.41659311972644725
001.002      nbinom       5.902081129467898e-18   0.005335820123825622   0.7249662663271113
001.003      nbinom       2.871871246304317e-13   0.00743701201046538    0.45081292375812926
001.004      poisson      0.0002870492567273848   15.333333333333334     NA

Does this answer your question? Pretty-print an entire Pandas Series / DataFrame — amitchone
– amitchone, Commented Dec 19, 2019 at 10:11

jezrael · Accepted Answer · 2019-12-19 10:48:12Z

1

Create list of dictionaries in loop with append and then pass to DataFrame constructor:

df_all = pd.DataFrame({'a':[2,3]})

def distribution_selection(csv_file=None,product_column=None,demand=None):
    out = []

    #sample data
    best_fit = 'norm'
    num, mean, std = 1, .03, .05
    likelihoods = {'norm':2}

    for num in df_all[product_column]:

        if best_fit=="norm":
            print("Product:",num)
            print("Best fit:",best_fit)
            print("Likelihood:", likelihoods[best_fit])
            print("Parameters:", mean,std)
            d = {'Product':num,
                 "Best fit:":best_fit,
                 "Likelihood":likelihoods[best_fit],
                 "Parameters1":mean,
                 "Parameters2":std}
            out.append(d)

            ...
            #similar for each conditions

    return pd.DataFrame(out)

df = distribution_selection('file', 'a')
print (df)
   Product Best fit:  Likelihood  Parameters1  Parameters2
0        2      norm           2         0.03         0.05
1        3      norm           2         0.03         0.05

edited Dec 19, 2019 at 10:48

answered Dec 19, 2019 at 10:12

jezrael

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4 Comments

Salih Over a year ago

hi @jazrael, i' ve edited my question, that for loop is in a function and dataframe is empty when i do what you say.

jezrael Over a year ago

@Salih - There is also out.append(d) ?

Salih Over a year ago

yes,and i got this; NameError: name 'out' is not defined

jezrael Over a year ago

@Salih - answer was edited, for me working nice (sample data only, so same values in both rows in output DataFrame)

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