如何在 pandas DataFrame中移动几行? [英] How to shift several rows in a pandas DataFrame?

问题描述

我有以下熊猫数据框:

import pandas as pd
data = {'one' : pd.Series([1.], index=['a']), 'two' : pd.Series([1., 2.], index=['a', 'b']), 'three' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(data)
df = df[["one", "two", "three"]]


   one  two  three
a  1.0  1.0    1.0
b  NaN  2.0    2.0
c  NaN  NaN    3.0
d  NaN  NaN    4.0

我知道如何按列上下移动元素，例如

I know how to shift elements by column upwards/downwards, e.g.

df.two = df.two.shift(-1)

   one  two  three
a  1.0  2.0    1.0
b  NaN  NaN    2.0
c  NaN  NaN    3.0
d  NaN  NaN    4.0

但是，我想将行a中的所有元素移到两列上，并将行b中的所有元素移到一列上.最终的数据帧如下所示:

However, I would like to shift all elements in row a over two columns and all elements in row b over one column. The final data frame would look like this:

   one  two  three
a  NaN  NaN    1.0
b  NaN  NaN    2.0
c  NaN  NaN    3.0
d  NaN  NaN    4.0

如何在熊猫中做到这一点?

How does one do this in pandas?

推荐答案

您可以转置初始的DF，以便您可以使用行标签作为列名来执行

You can transpose the initial DF so that you have a way to access the row labels as column names inorder to perform the shift operation.

将各列的内容向下移动这些量，然后将其重新移回以获得所需的结果.

Shift the contents of the respective columns downward by those amounts and re-transpose it back to get the desired result.

df_t = df.T
df_t.assign(a=df_t['a'].shift(2), b=df_t['b'].shift(1)).T

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