python - Counting multiple values in a groupby object -


this question has answer here:

i'd count multiple values (contained in list per cell) on groupby object.

i have following dataframe:

| | record respondent’s sex | 7. use phone for? | |---|-----------------------------|---------------------------------------------| | 0 | male | sending texts;calls;receiving sending texts | | 1 | female | sending texts;calls;whatsapp;facebook | | 2 | male | sending texts;calls;receiving texts | | 3 | female | sending texts;calls |

i count every value in column 7. use phone for?, after grouping on record respondent’s sex.

i have no problem doing when there 1 value per cell. 

grouped = df.groupby(['record respondent’s sex'], sort=true)  question_counts = grouped['2. teacher, caregiver, or young adult ?'].value_counts(normalize=false, sort=true)  question_data = [       {'2. teacher, caregiver, or young adult ?': question, 'record respondent’s sex': group, 'count': count*100}       (group, question), count in dict(question_counts).items()]  df_question = pd.dataframe(question_data)

gives me table looks this:

| 7. use phone for? | record respondent's sex | count | |-----------------------------------|-----------------------------|-------| | sending texts | male | 2 | | calls | male | 2 | | receiving texts | male | 2 | | sending texts | female | 2 | | calls | female | 2 | | whatsapp | female | 1 | | facebook | female | 1 |

if working multiple values!

value_counts() doesn't work on lists multiple values, throws typeerror: unhashable type: 'list' error. question counting occurrence of values in panda series? shows how deal in various ways, can't seem work on groupby object.

# initialize sample data. df = pd.dataframe({'record respondent’s sex': ['male', 'female'] * 2,                     '7. use phone for?': [                        "sending texts;calls;receiving sending texts",                        "sending texts;calls;whatsapp;facebook",                        "sending texts;calls;receiving texts",                        "sending texts;calls"                    ]})  # split values on ';' , separate columns.  melt result. df2 = pd.melt(     pd.concat([df['record respondent’s sex'],                df.loc[:, "7. use phone for?"].apply(                    lambda series: series.split(';')).apply(pd.series)], axis=1),     id_vars='record respondent’s sex')[['record respondent’s sex', 'value']]  # group on gender , rename columns. result = df2.groupby('record respondent’s sex')['value'].value_counts().reset_index() result.columns = ['record respondent’s sex', '7. use phone for?', 'count']  # reorder columns. >>> result[['7. use phone for?', 'record respondent’s sex', 'count']]   7. use phone for? record respondent’s sex  count 0                             calls                      female      2 1                     sending texts                      female      2 2                          facebook                      female      1 3                          whatsapp                      female      1 4                             calls                        male      2 5                     sending texts                        male      2 6           receiving sending texts                        male      1 7                   receiving texts                        male      1

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