Vfrdtyky

I invented a little game that would bring up pictures of American legislators and ask the user to choose one of them only based on their looks (yes, it is an artistic comment on my generation's political engagement). After a few matches, the game would bring up an objection to the choice, ultimately based on some issues the user has indicated they care about. To test the game and its subsequent analytics, I wrote a function to return user data, but it is returning only an empty dataframe. Can anyone spot something incorrect with it? I realize the end of the function is wordy, but I was trying a "brute force" approach after my more elegant approaches didn't work.

Note: there is a dataframe called zipcodes that includes state, zipcode, and congressional district. There is also a dataframe claled legislators that includes information about legislators in the current Congress.

def play_game():



    #Create player

    #These probabilities are made up... 

    #...but it doesn't matter since I am just filling a DB as a 

    placeholder for real data

    user_id = str(uuid.uuid4()) #generate user_id

    gender = np.random.choice(genders, p=[0.495, 0.495, 0.01])

    age = np.random.choice(range(13, 75))

    cares_about = np.random.choice(cares, 

                  size=np.random.choice(range(len(cares))))

    user_party = np.random.choice(parties, p=[0.35, 0.35, 0.15, 0.15])

    zip_state_dist = zipcodes.sample(1, replace=True)

    zip_code = zip_state_dist.iloc[0]['Zipcode']

    state = zip_state_dist.iloc[0]['State']

    congressional_district = zip_state_dist.iloc[0]['Congressional District']



    #Getting the user's legislators   

    #sr_senator = legislators[

                 #(legislators['state'] == state) & 

                 #(legislators['state_rank'] == 'senior')].iloc[0]['name']

    #jr_senator = legislators[

                 #(legislators['state'] == state) & 

                 #(legislators['state_rank'] == 'junior')].iloc[0]['name']

    #representative = legislators[

                     #(legislators['state'] == state) & 

                     #(legislators['district'] == congressional_district)].iloc[0]['name']



    #create df for player's id

    user_id_columns = ['age', 'gender', 'cares', 'zipcode', 'state', 'congressional_district','party']

    user_id_info = pd.DataFrame(columns=user_id_columns)

    user_id_info['user_id'] = user_id

    user_id_info['age'] = age

    user_id_info['gender'] = gender

    user_id_info['cares'] = cares_about

    user_id_info['zipcode'] = zip_code

    user_id_info['congressional_district'] = congressional_district

    user_id_info['party'] = user_party







    #an objection is raised and answered with a probability distribution

    def objection(winner, picks):

        legislator_slice = legislators[legislators['name'] == winner].iloc[0]

        if legislator_slice['party'] == user_party: #if parties are same, 75% chance that winner will stay

            winner_after_objection = np.random.choice([winner, picks[1-picks.index(winner)]],

                                                  p = [0.75, 0.25])

        elif legislator_slice['party'] != user_party: #if parties are different, 25% chance that winner will stay

            winner_after_objection = np.random.choice([winner, picks[1-picks.index(winner)]],

                                                  p = [0.25, 0.75])

        return winner_after_objection



    #make DataFrame for results

    user_id_results = pd.DataFrame(columns=['user_id', 

                                        'winner1', 'loser1', 

                                        'winner2', 'loser2', 

                                        'winner3_before', 'loser3_before', 

                                        'winner3_after', 'loser3_after',

                                        'winner4_before', 'loser4_before', 

                                        'winner4_after', 'loser4_after',

                                        'winner5_before', 'loser5_before', 

                                        'winner5_after', 'loser5_after',])  

    user_id_results['user_id'] = user_id

    # show 2 choices without objections

    for i in range(2):

        picks = legislators.sample(2, replace=True)

        picks = [picks.iloc[0]['name'], picks.iloc[1]['name']]

        winner = np.random.choice(picks)

        loser = picks[1-picks.index(winner)]



        user_id_results['winner1'] = winner

        user_id_results['loser1'] = loser

        user_id_results['winner2'] = winner

        user_id_results['loser2'] = loser







    #show 3 choices with objections

    for i in range(2,5): 

        picks = legislators.sample(2, replace=True)

        picks = [picks.iloc[0]['name'], picks.iloc[1]['name']]

        winner = np.random.choice(picks)

        loser = picks[1-picks.index(winner)]

        new_winner = objection(winner, picks)

        new_loser = picks[1-picks.index(new_winner)]



        if i == 2:

            user_id_results['winner3_before'] = winner

            user_id_results['loser3_before'] = loser

            user_id_results['winner3_after'] = new_winner

            user_id_results['loser3_after'] = new_loser

        elif i == 3:

            user_id_results['winner4_before'] = winner

            user_id_results['loser4_before'] = loser

            user_id_results['winner4_after'] = new_winner

            user_id_results['loser4_after'] = new_loser

        elif i == 4:

            user_id_results['winner5_before'] = winner

            user_id_results['loser5_before'] = loser

            user_id_results['winner5_after'] = new_winner

            user_id_results['loser5_after'] = new_loser



    return user_id_results