groupbyオブジェクトの複数の値を集計する

Question

groupByオブジェクトの複数の値（セルあたりのリストに含まれる）を数えたいと思います。私はRecord the respondent’s sexにグループ分けした後、列7. What do you use the phone for?のすべての値をカウントしたい

| | Record the respondent’s sex | 7. What do you use the 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 |

：

は、私は、次のデータフレームを持っています。

セルあたり1つの値しかない場合は、この操作は問題ありません。

| 7. What do you use the phone for? | Record the 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 |

場合にのみ、私は複数の値を扱うこれを取得することができます：

grouped = df.groupby(['Record the respondent’s sex'], sort=True) 

question_counts = grouped['2. Are you a teacher, caregiver, or young adult ?'].value_counts(normalize=False, sort=True) 

question_data = [ 
     {'2. Are you a teacher, caregiver, or young adult ?': question, 'Record the respondent’s sex': group, 'count': count*100} for 
     (group, question), count in dict(question_counts).items()] 

df_question = pd.DataFrame(question_data) 

は私にまさにこのようになりますテーブルを提供します！

value_counts()複数の値を持つリストでは機能しません。TypeError: unhashable type: 'list'というエラーがスローされます。質問Counting occurrence of values in a Panda series?は、これをさまざまな方法で処理する方法を示していますが、GroupByオブジェクトで動作させることはできません。列に複数の値を爆発/複製

Answer 1

# Initialize sample data. 
df = pd.DataFrame({'Record the respondent’s sex': ['Male', 'Female'] * 2, 
        '7. What do you use the phone for?': [ 
         "sending texts;calls;receiving sending texts", 
         "sending texts;calls;WhatsApp;Facebook", 
         "sending texts;calls;receiving texts", 
         "sending texts;calls" 
        ]}) 

# Split the values on ';' and separate into columns. Melt the result. 
df2 = pd.melt(
    pd.concat([df['Record the respondent’s sex'], 
       df.loc[:, "7. What do you use the phone for?"].apply(
        lambda series: series.split(';')).apply(pd.Series)], axis=1), 
    id_vars='Record the respondent’s sex')[['Record the respondent’s sex', 'value']] 

# Group on gender and rename columns. 
result = df2.groupby('Record the respondent’s sex')['value'].value_counts().reset_index() 
result.columns = ['Record the respondent’s sex', '7. What do you use the phone for?', 'count'] 

# Reorder columns. 
>>> result[['7. What do you use the phone for?', 'Record the respondent’s sex', 'count']] 
    7. What do you use the phone for? Record the 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