如何在 Pandas DataFrame 中移动几行？
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How to shift several rows in a pandas DataFrame?
提问by ShanZhengYang
I have the following pandas Dataframe:
我有以下Pandas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
I know how to shift elements by column upwards/downwards, e.g.
我知道如何按列向上/向下移动元素，例如
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
However, I would like to shift all elements in row aover two columns and all elements in row bover one column. The final data frame would look like this:
但是，我想将行中的所有元素a移到两列上，并将行中的所有元素b移到一列上。最终的数据框如下所示：
one two three
a NaN NaN 1.0
b NaN NaN 2.0
c NaN NaN 3.0
d NaN NaN 4.0
How does one do this in pandas?
如何在Pandas中做到这一点？
采纳答案by Nickil Maveli
You can transpose the initial DFso that you have a way to access the row labels as column names inorder to perform the shiftoperation.
您可以转置首字母，DF以便您有办法将行标签作为列名称访问，以便执行shift操作。
Shift the contents of the respective columns downward by those amounts and re-transpose it back to get the desired result.
将相应列的内容向下移动这些数量，然后将其重新调回以获得所需的结果。
df_t = df.T
df_t.assign(a=df_t['a'].shift(2), b=df_t['b'].shift(1)).T
