将两列长度相等的列合并为一列答案

【问题标题】：Merge two columns of equal length into one将两列长度相等的列合并为一列
【发布时间】：2018-12-31 15:58:39
【问题描述】：

我有一个包含多列的 pandas DataFrame。我想要完成的是将两列的值组合/堆叠成一列，将每一列的值逐行堆叠（不幸的是，这个要求阻止我使用类似联合的解决方案）。其他剩余列的内容可以复制。非常感谢任何帮助

#Current DataFrame
print(df)
Stock Ticker    Index Ticker    Price   Date
AAPL            INDX            100     12/31/2018 8:57  
GOOG            RSL             123     12/31/2018 8:57
GM              COMP            90      12/31/2018 8:57
MMM             NIKK            340     12/31/2018 8:57
INVD            EUR             30      12/31/2018 8:57 

#Desired results
print(df2)
Stock and Bench   Price   Date
AAPL              100     12/31/2018 8:57
INDX              100     12/31/2018 8:57
GOOG              123     12/31/2018 8:57
RSL               123     12/31/2018 8:57
GM                90      12/31/2018 8:57
COMP              90      12/31/2018 8:57
MMM               340     12/31/2018 8:57
NIKK              340     12/31/2018 8:57
INVD              30      12/31/2018 8:57
EUR               30      12/31/2018 8:57

【问题讨论】：

标签： python pandas dataframe series

【解决方案1】：

您可以将价格和日期列设置为索引并堆叠股票和股票代码。最后使用 reset_index 进行一些清理。

df.set_index(['Date', 'Price'])[['Stock Ticker','Index Ticker']].stack()\
.reset_index(2,drop = True).reset_index(name = 'Stock and Bench')


    Date    Price   Stock and Bench
0   12/31/2018 8:57 100 AAPL
1   12/31/2018 8:57 100 INDX
2   12/31/2018 8:57 123 GOOG
3   12/31/2018 8:57 123 RSL
4   12/31/2018 8:57 90  GM
5   12/31/2018 8:57 90  COMP
6   12/31/2018 8:57 340 MMM
7   12/31/2018 8:57 340 NIKK
8   12/31/2018 8:57 30  INVD
9   12/31/2018 8:57 30  EUR

【讨论】：

【解决方案2】：

您可以使用pd.melt 设置Date 和Price 为id_vars：

(df.melt(id_vars=['Date', 'Price'], 
         value_name='Stock and Bench')
         .drop('variable', axis=1))

         Date        Price     Stock and Bench
0  12/31/2018/8:57    100            AAPL
1  12/31/2018/8:57    123            GOOG
2  12/31/2018/8:57     90              GM
3  12/31/2018/8:57    340             MMM
4  12/31/2018/8:57     30            INVD
5  12/31/2018/8:57    100            INDX
6  12/31/2018/8:57    123             RSL
7  12/31/2018/8:57     90            COMP
8  12/31/2018/8:57    340            NIKK
9  12/31/2018/8:57     30             EUR

或者使用pd.wide_to_long:

(pd.wide_to_long(df.reset_index(), stubnames='Ticker', i = 'index', 
                j = 'num', suffix='\w+')
                .reset_index(drop=True)
                .rename({'Ticker':'Stock and Bench'}, axis=1))

        Date         Price Stock and Bench
0  12/31/2018-8:57    100   AAPL
1  12/31/2018-8:57    123   GOOG
2  12/31/2018-8:57     90     GM
3  12/31/2018-8:57    340    MMM
4  12/31/2018-8:57     30   INVD
5  12/31/2018-8:57    100   INDX
6  12/31/2018-8:57    123    RSL
7  12/31/2018-8:57     90   COMP
8  12/31/2018-8:57    340   NIKK
9  12/31/2018-8:57     30    EUR

【讨论】：