選擇某些行的值，使它們列大熊貓

Question

我有一個數據集看起來像下面：

+-------------------------+-------------+------+--------+-------------+--------+--+ 
|       | impressions | name | shares | video_views | diff | | 
+-------------------------+-------------+------+--------+-------------+--------+--+ 
| _ts      |    |  |  |    |  | | 
| 2016-09-12 23:15:04.120 |   1 | Vidz |  7 |  10318 | 15mins | | 
| 2016-09-12 23:16:45.869 |   2 | Vidz |  7 |  10318 | 16mins | | 
| 2016-09-12 23:30:03.129 |   3 | Vidz |  18 |  29291 | 30mins | | 
| 2016-09-12 23:32:08.317 |   4 | Vidz |  18 |  29291 | 32mins | | 
+-------------------------+-------------+------+--------+-------------+--------+--+ 

我試圖建立一個數據幀喂到迴歸模型，我想將特定行解析爲特徵。要做到這一點，我希望數據框類似於這個

+-------------------------+------+--------------+-------------------+-------------------+--------------+-------------------+-------------------+ 
|       | name | 15min_shares | 15min_impressions | 15min_video_views | 30min_shares | 30min_impressions | 30min_video_views | 
+-------------------------+------+--------------+-------------------+-------------------+--------------+-------------------+-------------------+ 
| _ts      |  |    |     |     |    |     |     | 
| 2016-09-12 23:15:04.120 | Vidz |   7 |     1 |    10318 |   18 |     3 |    29291 | 
+-------------------------+------+--------------+-------------------+-------------------+--------------+-------------------+-------------------+ 

什麼是最好的方式來做到這一點？我認爲這會更容易，如果我只是試圖選擇1行（15分鐘），只是解析出不需要的行和主鍵。

不過，我需要15分鐘和30分鐘的功能和我有關如何繼續的需要對這些列

不確定

Answer 1

你可以把你的DF的子集，以包括15分鐘和30分鐘行和回填NaN值將它們連接起來第一行（15分鐘）與它的是下一個行（30分鐘）和脫落的下一行（30分鐘），如下所示：

prefix_15="15mins" 
prefix_30="30mins" 

fifteen_mins = (df['diff']==prefix_15) 
thirty_mins = (df['diff']==prefix_30) 

df = df[fifteen_mins|thirty_mins].drop(['diff'], axis=1) 

df_ = pd.concat([df[fifteen_mins].add_prefix(prefix_15+'_'),   \ 
       df[thirty_mins].add_prefix(prefix_30+'_')], axis=1) \ 
       .fillna(method='bfill').dropna(how='any') 

del(df_['30mins_name']) 
df_.rename(columns={'15mins_name':'name'}, inplace=True) 
df_ 

Answer 2

堆疊樞轉和摺疊的列

df1 = df.set_index('diff', append=True).stack().unstack(0).T 
df1.columns = df1.columns.map('_'.join) 

---

要看到的只是第一行

df1.iloc[[0]].dropna(1)