Python pandas: преобразование значений столбца в другие столбцы

Question

У меня есть фрейм данных, который выглядит следующим образом:

df:

    Review_Text                                                 Noun                                        Thumbups
    Would be nice to be able to import files from ...   [My, Tracks, app, phone, Google, Drive, import...   1.0
    No Offline Maps! It used to have offline maps ...   [Offline, Maps, menu, option, video, exchange,...   18.0
    Great application. Designed with very well tho...   [application, application]                          16.0
    Great App. Nice and simple but accurate. Wish ...   [Great, App, Nice, Exported]                        0.0
    Save For Offline - This does not work. The rou...   [Save, Offline, route, filesystem]                  12.0
    Since latest update app will not run. Subscrip...   [update, app, Subscription, March, application]     9.0
    Great app. Love it! And all the things it does...   [Great, app, Thank, work]                           1.0
    I have paid for subscription but keeps telling...   [subscription, trial, period]                       0.0
    Error: The route cannot be save for no locatio...   [Error, route, i, GPS]                              0.0
    When try to restore my tracks it says "unable ...   [try, file, locally-1]                              0.0
    Was a good app but since the update it only re...   [app, update, metre]                                2.0

на основе значений столбца «Существительное», я хочу создать другие столбцы. Например, все значения столбца существительного из первой строки становятся столбцами, и эти столбцы содержат значение столбца «Thumbups». Если имя столбца уже присутствует в кадре данных, тогда оно добавляет значение Thumbups в существующее значение столбца.

Я пытался реализовать с помощью pivot_table:

pd.pivot_table(latest_review,columns='Noun',values='Thumbups')

Но получил следующее ошибка:

TypeError: unhashable type: 'list'

Может ли кто-нибудь помочь мне в решении проблемы?

Answer 1

Используйте Series.str.join с Series.str.get_dummies для манекенов, а затем несколько по столбцу Thumbups на DataFrame.mul:

df1 = df['Noun'].str.join('|').str.get_dummies().mul(df['Thumbups'], axis=0)

print (df1)
   App  Drive  Error  Exported  GPS  Google  Great   Maps  March    My  Nice  \
0   0.0   10.0    0.0       0.0  0.0    10.0    0.0    0.0    0.0  10.0   0.0   
1   0.0    0.0    0.0       0.0  0.0     0.0    0.0  180.0    0.0   0.0   0.0   
2   0.0    0.0    0.0       0.0  0.0     0.0    0.0    0.0    0.0   0.0   0.0   
3   0.0    0.0    0.0       0.0  0.0     0.0    0.0    0.0    0.0   0.0   0.0   
4   0.0    0.0    0.0       0.0  0.0     0.0    0.0    0.0    0.0   0.0   0.0   
5   0.0    0.0    0.0       0.0  0.0     0.0    0.0    0.0   90.0   0.0   0.0   
6   0.0    0.0    0.0       0.0  0.0     0.0   10.0    0.0    0.0   0.0   0.0   
7   0.0    0.0    0.0       0.0  0.0     0.0    0.0    0.0    0.0   0.0   0.0   
8   0.0    0.0    0.0       0.0  0.0     0.0    0.0    0.0    0.0   0.0   0.0   
9   0.0    0.0    0.0       0.0  0.0     0.0    0.0    0.0    0.0   0.0   0.0   
10  NaN    NaN    NaN       NaN  NaN     NaN    NaN    NaN    NaN   NaN   NaN   

    Offline   Save  Subscription  Thank  Tracks   app  application  exchange  \
0       0.0    0.0           0.0    0.0    10.0  10.0          0.0       0.0   
1     180.0    0.0           0.0    0.0     0.0   0.0          0.0     180.0   
2       0.0    0.0           0.0    0.0     0.0   0.0        160.0       0.0   
3       0.0    0.0           0.0    0.0     0.0   0.0          0.0       0.0   
4     120.0  120.0           0.0    0.0     0.0   0.0          0.0       0.0   
5       0.0    0.0          90.0    0.0     0.0  90.0         90.0       0.0   
6       0.0    0.0           0.0   10.0     0.0  10.0          0.0       0.0   
7       0.0    0.0           0.0    0.0     0.0   0.0          0.0       0.0   
8       0.0    0.0           0.0    0.0     0.0   0.0          0.0       0.0   
9       0.0    0.0           0.0    0.0     0.0   0.0          0.0       0.0   
10      NaN    NaN           NaN    NaN     NaN   NaN          NaN       NaN   

    file  filesystem    i  import  locally-1   menu  metre  option  period  \
0    0.0         0.0  0.0    10.0        0.0    0.0    0.0     0.0     0.0   
1    0.0         0.0  0.0     0.0        0.0  180.0    0.0   180.0     0.0   
2    0.0         0.0  0.0     0.0        0.0    0.0    0.0     0.0     0.0   
3    0.0         0.0  0.0     0.0        0.0    0.0    0.0     0.0     0.0   
4    0.0       120.0  0.0     0.0        0.0    0.0    0.0     0.0     0.0   
5    0.0         0.0  0.0     0.0        0.0    0.0    0.0     0.0     0.0   
6    0.0         0.0  0.0     0.0        0.0    0.0    0.0     0.0     0.0   
7    0.0         0.0  0.0     0.0        0.0    0.0    0.0     0.0     0.0   
8    0.0         0.0  0.0     0.0        0.0    0.0    0.0     0.0     0.0   
9    0.0         0.0  0.0     0.0        0.0    0.0    0.0     0.0     0.0   
10   NaN         NaN  NaN     NaN        NaN    NaN    NaN     NaN     NaN   

    phone  route  subscription  trial  try  update  video  work  
0    10.0    0.0           0.0    0.0  0.0     0.0    0.0   0.0  
1     0.0    0.0           0.0    0.0  0.0     0.0  180.0   0.0  
2     0.0    0.0           0.0    0.0  0.0     0.0    0.0   0.0  
3     0.0    0.0           0.0    0.0  0.0     0.0    0.0   0.0  
4     0.0  120.0           0.0    0.0  0.0     0.0    0.0   0.0  
5     0.0    0.0           0.0    0.0  0.0    90.0    0.0   0.0  
6     0.0    0.0           0.0    0.0  0.0     0.0    0.0  10.0  
7     0.0    0.0           0.0    0.0  0.0     0.0    0.0   0.0  
8     0.0    0.0           0.0    0.0  0.0     0.0    0.0   0.0  
9     0.0    0.0           0.0    0.0  0.0     0.0    0.0   0.0  
10    NaN    NaN           NaN    NaN  NaN     NaN    NaN   NaN  

Answer 2

rows = []
#_unpacking Noun column row list values and storing it in rows list
_ = df.apply(lambda row: [rows.append([row['Review_Text'],row['Thumbups'], nn]) 
                         for nn in row.Noun], axis=1)

#_creates new dataframe with unpacked values
df_new = pd.DataFrame(rows, columns=df.columns)

#_now doing pivot operation on df_new
pivot_df = df_new.pivot(index='Review_Text', columns='Noun')