本文介绍了任务，根据下一行添加新列的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有这个DASK数据框，最后一列是这个问题的重要信息：
Dask DataFrame Structure:
asks[0].amount asks[1].amount asks[2].amount asks[3].amount asks[4].amount asks[5].amount asks[6].amount asks[7].amount asks[8].amount asks[9].amount asks[10].amount asks[11].amount asks[12].amount asks[13].amount asks[14].amount asks[15].amount asks[16].amount asks[17].amount asks[18].amount asks[19].amount asks[20].amount asks[21].amount asks[22].amount asks[23].amount asks[24].amount bids[0].amount bids[1].amount bids[2].amount bids[3].amount bids[4].amount bids[5].amount bids[6].amount bids[7].amount bids[8].amount bids[9].amount bids[10].amount bids[11].amount bids[12].amount bids[13].amount bids[14].amount bids[15].amount bids[16].amount bids[17].amount bids[18].amount bids[19].amount bids[20].amount bids[21].amount bids[22].amount bids[23].amount bids[24].amount currentPrice
npartitions=1
float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
row1: ask......, bids....., currentPrice(11), succPrice(12)
row2: ask......, bids....., currentPrice(12), succPrice(17)
row3: ask......, bids....., currentPrice(17), succPrice(.....)
如何才能获得此结果？数据帧非常大，因此我需要使用Dask
推荐答案
使用shift(-1)
Dasksshift的功能与 pandas shift的功能相同。也就是说，如果要使用下一行的值，则必须使用shift(-1)。
请记住，数据帧的最后一个值将是nan。
代码示例
import dask
# Create data
df = (dask.datasets.timeseries()
.drop(columns=['id', 'name', 'y'])
.rename(columns={'x': 'currentPrice'}))
# Assign `succPrice` equal to the next `currentPrice`
df = df.assign(succPrice=df['currentPrice'].shift(-1))
df.tail()
| timestamp | currentPrice | succPrice |
|:--------------------|---------------:|------------:|
| 2000-01-30 23:59:55 | -0.241575 | 0.65083 |
| 2000-01-30 23:59:56 | 0.65083 | 0.742577 |
| 2000-01-30 23:59:57 | 0.742577 | 0.313805 |
| 2000-01-30 23:59:58 | 0.313805 | 0.556262 |
| 2000-01-30 23:59:59 | 0.556262 | nan |
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