iterrows大 pandas 获取下一行值

问题描述

我在熊猫上有一个df

I have a df in pandas

import pandas as pd
df = pd.DataFrame(['AA', 'BB', 'CC'], columns = ['value'])

我想遍历df中的行.对于每一行，我都希望row s value and next row的值诸如此类(无效):

I want to iterate over rows in df. For each row i want rows value and next rows valueSomething like(it does not work):

for i, row in df.iterrows():
     print row['value']
     i1, row1 = next(df.iterrows())
     print row1['value']

因此，我想要

'AA'
'BB'
'BB'
'CC'
'CC'
*Wrong index error here

在这一点上，我有解决这个问题的混乱方法

At this point i have mess way to solve this

for i in range(0, df.shape[0])
   print df.irow(i)['value']
   print df.irow(i+1)['value']

有没有更有效的方法来解决此问题?

Is there more efficient way to solve this issue?

推荐答案

首先，您的混乱方式"是可以的，在数据​​帧中使用索引没有任何问题，这不会太慢. iterrows()本身并不快.

Firstly, your "messy way" is ok, there's nothing wrong with using indices into the dataframe, and this will not be too slow. iterrows() itself isn't terribly fast.

第一个可行的想法是:

row_iterator = df.iterrows()
_, last = row_iterator.next()  # take first item from row_iterator
for i, row in row_iterator:
    print(row['value'])
    print(last['value'])
    last = row

第二种方法可以做类似的事情，将一个索引保存到数据框中:

The second method could do something similar, to save one index into the dataframe:

last = df.irow(0)
for i in range(1, df.shape[0]):
    print(last)
    print(df.irow(i))
    last = df.irow(i)

当速度至关重要时，您可以随时尝试并设置代码时间.

When speed is critical you can always try both and time the code.

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