python 3.x - Pandas DataFrames- value counts from many columns and survey multiple answers -


i'm struggling 2 issues:

  1. i have exemplary dataset cars. 20 respondents asked favorite cars. enumerate maximally 5 cars (columns "answer 1"- "answer 5". how can number of mentions of each car?

  2. for cars mentioned in columns answer 1- answer 2 each respondent noted 3 advantages of each car (for example columns adv car 1_1, adv car 1_2, adv car 1_3 related first mentioned car, adv car 2_1, adv car 2_2, adv car 2_3 related second car etc.).
    how can show how many particular advantages related each car mentioned?
    need informations this:

    • ferrari - engine 3 times,
    • color - 5 times,
    • price - 3 times etc,
    • audi - engine - 4 times,
    • color - 3 times,
    • price - 2 times etc.

the second issue related spss , r variables cases, can't translate python. in case related melting dataframes, attempts weren't effective.

i'll thankful help. csv file available download or below:

no;answer 1;answer 2;answer 3;answer 4;answer 5;adv car 1_1;adv car 1_2;adv car 1_3;adv car 2_1;adv car 2_2;adv car 2_3;adv car 3_1;adv car 3_2;adv car 3_3;adv car 4_1;adv car 4_2;adv car 4_3;adv car 5_1;adv car 5_2;adv car 5_3 1;ferrari;volkswagen;audi;renault;toyota;color;longevity;brand;manufacturer;color;longevity;brand;manufacturer;color;longevity;brand;manufacturer;color;longevity;brand 2;volkswagen;toyota;audi;;;brand;color;price;engine;color;longevity;price;color;price;;;;;; 3;toyota;renault;;;;manufacturer;brand;engine;color;price;brand;;;;;;;;; 4;audi;;;;;engine;color;manufacturer;;;;;;;;;;;; 5;volkswagen;ferrari;renault;;;color;longevity;brand;manufacturer;color;longevity;brand;manufacturer;color;;;;;; 6;renault;toyota;;;;brand;engine;color;price;brand;manufacturer;;;;;;;;; 7;audi;renault;ferrari;toyota;;brand;color;price;engine;color;longevity;price;color;price;longevity;brand;manufacturer;;; 8;toyota;;;;;engine;color;longevity;;;;;;;;;;;; 9;audi;ferrari;volkswagen;;;engine;color;longevity;price;color;price;longevity;brand;manufacturer;;;;;; 10;renault;toyota;;;;color;longevity;price;color;price;;;;;;;;;; 11;ferrari;;;;;longevity;price;color;;;;;;;;;;;; 12;toyota;volkswagen;audi;;;engine;color;manufacturer;engine;color;longevity;price;color;price;;;;;; 13;audi;volkswagen;renault;;;manufacturer;color;longevity;brand;manufacturer;color;longevity;brand;manufacturer;;;;;; 14;volkswagen;;;;;color;longevity;price;;;;;;;;;;;; 15;toyota;volkswagen;;;;brand;color;price;engine;color;longevity;;;;;;;;; 16;ferrari;renault;volkswagen;;;price;engine;color;longevity;price;color;price;longevity;brand;;;;;; 17;audi;toyota;renault;;;color;longevity;brand;manufacturer;color;longevity;brand;manufacturer;color;;;;;; 18;toyota;audi;volkswagen;ferrari;audi;color;longevity;brand;manufacturer;color;longevity;brand;manufacturer;color;longevity;brand;manufacturer;color;longevity;brand 19;ferrari;;;;;engine;;;;;;;;;;;;;; 20;volkswagen;;;;;brand;;;;;;;;;;;;;; ;;;;;;;;;;;;;;;;;;;; ;;;;data2 = ['color','engine','price','manufacturer','brand','longevity',''];;;;;;;;;;;;;;;; 

df = pd.read_csv('cars.csv',sep=';')  df_car = df.melt(id_vars='no',value_vars=['answer 1','answer 2','answer 3','answer 4','answer 5'], value_name='cars').drop('variable',axis=1)  df_advantages = df.melt(id_vars='no',value_vars=['adv car 1_1', 'adv car 1_2', 'adv car 1_3', 'adv car 2_1',        'adv car 2_2', 'adv car 2_3', 'adv car 3_1', 'adv car 3_2',        'adv car 3_3', 'adv car 4_1', 'adv car 4_2', 'adv car 4_3',        'adv car 5_1', 'adv car 5_2', 'adv car 5_3'], value_name='advantages').drop('variable',axis=1)  df_car.merge(df_advantages, on='no').groupby(['cars','advantages']).count()

output:

                         no cars       advantages       audi       brand         20            color         29            engine         6            longevity     22            manufacturer  18            price         10 ferrari    brand         14            color         19            engine         4            longevity     17            manufacturer  10            price          9 renault    brand         17            color         21            engine         4            longevity     15            manufacturer  13            price         10 toyota     brand         18            color         27            engine         8            longevity     17            manufacturer  12            price         13 volkswagen brand         17            color         26            engine         6            longevity     20            manufacturer  13            price         12

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