我的培训数据train SFrame看起来像这样,有4列( "Store“列是非唯一的SFrame中的):
+-------+------------+---------+-----------+
| Store | Date | Sales | Customers |
+-------+------------+---------+-----------+
| 1 | 2015-07-31 | 5263.0 | 555.0 |
| 2 | 2015-07-31 | 6064.0 | 625.0 |
| 3 | 2015-07-31 | 8314.0 | 821.0 |
| 4 | 2015-07-31 | 13995.0 | 1498.0 |
| 3 | 2015-07-20 | 4822.0 | 559.0 |
| 2 | 2015-07-10 | 5651.0 | 589.0 |
| 4 | 2015-07-11 | 15344.0 | 1414.0 |
| 5 | 2015-07-23 | 8492.0 | 833.0 |
| 2 | 2015-07-19 | 8565.0 | 687.0 |
| 10 | 2015-07-09 | 7185.0 | 681.0 |
+-------+------------+---------+-----------+
[986159 rows x 4 columns]给定第二个store SFrame ( "Store“列在此SFrame中是唯一的):
+-------+-----------+
| Store | StoreType |
+-------+-----------+
| 1 | c |
| 2 | a |
| 3 | a |
| 4 | c |
| 5 | a |
| 6 | a |
| 7 | a |
| 8 | a |
| 9 | a |
| 10 | a |
+-------+-----------+我可以将适当的StoreType附加到我的train SFrame上,方法是遍历train中的每一行,然后从store中找到合适的StoreType,然后保留该列,然后再修改SFrame.add_column():
store_type_col = []
for row in train:
row_store = row['Store']
row_storetype = next(i for i in store if i['Store'] == row_store)['StoreType']
store_type_col.append(row_storetype)
train.add_column(graphlab.SArray(store_type_col, dtype=str), name='StoreType')得到:
+-------+------------+---------+-----------+-----------+
| Store | Date | Sales | Customers | StoreType |
+-------+------------+---------+-----------+-----------+
| 1 | 2015-07-31 | 5263.0 | 555.0 | c
| 2 | 2015-07-31 | 6064.0 | 625.0 | a
| 3 | 2015-07-31 | 8314.0 | 821.0 | a
| 4 | 2015-07-31 | 13995.0 | 1498.0 | c
| 3 | 2015-07-20 | 4822.0 | 559.0 | a
| 2 | 2015-07-10 | 5651.0 | 589.0 | a
| 4 | 2015-07-11 | 15344.0 | 1414.0 | c
| 5 | 2015-07-23 | 8492.0 | 833.0 | a
| 2 | 2015-07-19 | 8565.0 | 687.0 | a
| 10 | 2015-07-09 | 7185.0 | 681.0 | a
+-------+------------+---------+-----------+-----------+
[986159 rows x 5 columns]但我确信,使用Graphlab有一种更简单、更快的方法来实现这一点。目前的方法存在O(n*m)的最坏情况,其中n= no。train和m= no中的行。m中的行。
假设我的store SFrame有8个列,我想将它们附加到train中。上面的代码会非常低效。
,否则如何将数据列从一个SFrame添加到另一个SFrame? (Pandas解决方案也是受欢迎的)
发布于 2015-11-18 08:00:55
您可以使用join操作来完成此操作。
out = train.join(store, on = 'Store')
https://stackoverflow.com/questions/33768546
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