给出下表
+----+--------+--------+--------------+
| Nr | Price | Volume | Transactions |
+----+--------+--------+--------------+
| 1 | 194.6 | 100 | 1 |
| 2 | 195 | 10 | 1 |
| 3 | 194.92 | 100 | 1 |
| 4 | 194.92 | 52 | 1 |
| 5 | 194.9 | 99 | 1 |
| 6 | 194.86 | 74 | 1 |
| 7 | 194.85 | 900 | 1 |
| 8 | 194.85 | 25 | 1 |
| 9 | 194.85 | 224 | 1 |
| 10 | 194.6 | 101 | 1 |
| 11 | 194.85 | 19 | 1 |
| 12 | 194.6 | 10 | 1 |
| 13 | 194.6 | 25 | 1 |
| 14 | 194.53 | 12 | 1 |
| 15 | 194.85 | 14 | 1 |
| 16 | 194.6 | 11 | 1 |
| 17 | 194.85 | 93 | 1 |
| 18 | 195 | 90 | 1 |
| 19 | 195 | 100 | 1 |
| 20 | 195 | 50 | 1 |
| 21 | 195 | 50 | 1 |
| 22 | 195 | 25 | 1 |
| 23 | 195 | 5 | 1 |
| 24 | 195 | 500 | 1 |
| 25 | 195 | 100 | 1 |
| 26 | 195.09 | 100 | 1 |
| 27 | 195 | 120 | 1 |
| 28 | 195 | 60 | 1 |
| 29 | 195 | 40 | 1 |
| 30 | 195 | 10 | 1 |
| 31 | 194.6 | 1 | 1 |
| 32 | 194.99 | 1 | 1 |
| 33 | 194.81 | 20 | 1 |
| 34 | 194.81 | 50 | 1 |
| 35 | 194.97 | 17 | 1 |
| 36 | 194.99 | 25 | 1 |
| 37 | 195 | 75 | 1 |
+----+--------+--------+--------------+
为了更快地进行测试，您还可以在熊猫数据框中找到同一表格
pd_data_before = pd.DataFrame([[1,194.6,100,1],[2,195,10,1],[3,194.92,100,1],[4,194.92,52,1],[5,194.9,99,1],[6,194.86,74,1],[7,194.85,900,1],[8,194.85,25,1],[9,194.85,224,1],[10,194.6,101,1],[11,194.85,19,1],[12,194.6,10,1],[13,194.6,25,1],[14,194.53,12,1],[15,194.85,14,1],[16,194.6,11,1],[17,194.85,93,1],[18,195,90,1],[19,195,100,1],[20,195,50,1],[21,195,50,1],[22,195,25,1],[23,195,5,1],[24,195,500,1],[25,195,100,1],[26,195.09,100,1],[27,195,120,1],[28,195,60,1],[29,195,40,1],[30,195,10,1],[31,194.6,1,1],[32,194.99,1,1],[33,194.81,20,1],[34,194.81,50,1],[35,194.97,17,1],[36,194.99,25,1],[37,195,75,1]],columns=['Nr','Price','Volume','Transactions'])
问题是我们如何根据相似的连续价格来总结交易量和交易量？最终结果将是这样的：
+----+--------+--------+--------------+
| Nr | Price | Volume | Transactions |
+----+--------+--------+--------------+
| 1 | 194.6 | 100 | 1 |
| 2 | 195 | 10 | 1 |
| 4 | 194.92 | 152 | 2 |
| 5 | 194.9 | 99 | 1 |
| 6 | 194.86 | 74 | 1 |
| 9 | 194.85 | 1149 | 3 |
| 10 | 194.6 | 101 | 1 |
| 11 | 194.85 | 19 | 1 |
| 13 | 194.6 | 35 | 2 |
| 14 | 194.53 | 12 | 1 |
| 15 | 194.85 | 14 | 1 |
| 16 | 194.6 | 11 | 1 |
| 17 | 194.85 | 93 | 1 |
| 25 | 195 | 920 | 8 |
| 26 | 195.09 | 100 | 1 |
| 30 | 195 | 230 | 4 |
| 31 | 194.6 | 1 | 1 |
| 32 | 194.99 | 1 | 1 |
| 34 | 194.81 | 70 | 2 |
| 35 | 194.97 | 17 | 1 |
| 36 | 194.99 | 25 | 1 |
| 37 | 195 | 75 | 1 |
+----+--------+--------+--------------+
您还可以在下面的pandas数据框中找到准备好的结果：
pd_data_after = pd.DataFrame([[1,194.6,100,1],[2,195,10,1],[4,194.92,152,2],[5,194.9,99,1],[6,194.86,74,1],[9,194.85,1149,3],[10,194.6,101,1],[11,194.85,19,1],[13,194.6,35,2],[14,194.53,12,1],[15,194.85,14,1],[16,194.6,11,1],[17,194.85,93,1],[25,195,920,8],[26,195.09,100,1],[30,195,230,4],[31,194.6,1,1],[32,194.99,1,1],[34,194.81,70,2],[35,194.97,17,1],[36,194.99,25,1],[37,195,75,1]],columns=['Nr','Price','Volume','Transactions'])
我设法在for循环中实现了这一点。但是问题在于，迭代每一行时它非常慢。我的数据集非常庞大，大约有5000万行。 有什么方法可以不循环而实现？
答案 0 :(得分：2)
对连续值进行分组的常见技巧如下：
df.col.ne(df.col.shift()).cumsum()
我们可以在这里使用它，然后使用agg保留我们不求和的列的第一个值，并对我们想要求和的值求和。
(df.groupby(df.Price.ne(df.Price.shift()).cumsum())
.agg({'Nr': 'last', 'Price': 'first', 'Volume':'sum', 'Transactions': 'sum'})
).reset_index(drop=True)
Nr Price Volume Transactions
0 1 194.60 100 1
1 2 195.00 10 1
2 4 194.92 152 2
3 5 194.90 99 1
4 6 194.86 74 1
5 9 194.85 1149 3
6 10 194.60 101 1
7 11 194.85 19 1
8 13 194.60 35 2
9 14 194.53 12 1
10 15 194.85 14 1
11 16 194.60 11 1
12 17 194.85 93 1
13 25 195.00 920 8
14 26 195.09 100 1
15 30 195.00 230 4
16 31 194.60 1 1
17 32 194.99 1 1
18 34 194.81 70 2
19 35 194.97 17 1
20 36 194.99 25 1
21 37 195.00 75 1