将行组合成单个单元格

Question

我目前有一个像这样的数据帧（df）：

name    info
alpha   foo,bar
alpha   bar,foo
beta    foo,bar
beta    bar,foo
beta    baz,qux

我想创建一个这样的数据帧：

name    info
alpha   (foo,bar),(bar,foo)
beta    (foo,bar),(bar,foo),(baz,qux)

我正在接近groupby.apply（list）。例如。

new_df=df.groupby('name')['info'].apply(list)

但是，我似乎无法弄清楚如何以原始数据帧格式获取输出。 （即有两列（如例）

我想我需要reset_index和unstack？感谢任何帮助！

Answer 1

尝试使用for循环：

uniqnames = df.name.unique() # get unique names
newdata = []                 # data list for output dataframe
for u in uniqnames:          # for each unique name
    subdf = df[df.name == u] # get rows with this unique name
    s = ""
    for i in subdf['info']:
        s += "("+i+"),"      # join all info cells for that name
    newdata.append([u, s[:-1]]) # remove trailing comma from infos & add row to data list

newdf = pd.DataFrame(data=newdata, columns=['name','info'])
print(newdf)

输出完全符合要求：

    name                           info
0  alpha            (foo,bar),(bar,foo)
1   beta  (foo,bar),(bar,foo),(baz,qux)

Answer 2

达蒙

df.assign(info='('+df['info']+')').groupby('name')['info'].apply(','.join).to_frame('info')
Out[267]: 
                                info
name                                
alpha            (foo,bar),(bar,foo)
beta   (foo,bar),(bar,foo),(baz,qux)



#df.assign(info='('+df['info']+')')# adding the ( and ) for your single string to match with the out put 
#groupby('name')# group by the name, you need merge info under the same name
#apply(','.join).to_frame('info') # this will combine each info into one string under the same group

Answer 3

达蒙：

df = pd.DataFrame({'name':['alpha']*2+['beta']*3,
                   'info':[['foo','bar'],['bar','foo'],
                           ['foo','bar'],['bar','foo'],
                           ['baz','qux']]})
print(df)

Inuput

         info   name
0  [foo, bar]  alpha
1  [bar, foo]  alpha
2  [foo, bar]   beta
3  [bar, foo]   beta
4  [baz, qux]   beta

现在，groupby并应用reset_index（）以返回到dataframe：

new_df = df.groupby('name')['info'].apply(list)

new_df = new_df.reset_index()

print(new_df)

输出：

    name                                  info
0  alpha              [[foo, bar], [bar, foo]]
1   beta  [[foo, bar], [bar, foo], [baz, qux]]