将一个 Pandas 数据帧除以另一个 - 忽略索引但尊重列
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Divide One Pandas Dataframe by Another - Ignore index but respect columns
提问by Dickster
I have 2 dataframes. I would like to broadcast a divide operation
我有 2 个数据框。我想广播一个除法操作
df1= pd.DataFrame([[1.,2.,3.,4.], [5.,6.,7.,8.], [9.,10.,11.,12.]],
columns=['A','B','C','D'], index=['x','y','z'])
df2= pd.DataFrame([[0.,1.,2.,3.]], columns=['A','B','D','C'], index=['q'])
Notice that the columns are aligned slightly differently in df2.
请注意，列在 df2 中的对齐方式略有不同。
I would like to divide df1 by df2 where the row is broadcast but the column labels are respected.
我想将 df1 除以 df2，其中广播行但尊重列标签。
A B C D
x 1 2 3 4
y 5 6 7 8
z 9 10 11 12
A B D C
q 0 1 2 3
This would be wrong.
这是错误的。
df1.values/df2.values
[[ inf 2. 1.5 1.33333333]
[ inf 6. 3.5 2.66666667]
[ inf 10. 5.5 4. ]]
Answer I desire is:
我想要的答案是：
A B C D
x inf 2 1 2
y inf 6 2.33 4
z inf 10 3.66 6
采纳答案by joris
If you divide by a Series (by selecting that one row of the second dataframe), pandas will align this series on the columns of the first dataframe, giving the desired result:
如果您除以一个系列（通过选择第二个数据帧的那一行），pandas 将在第一个数据帧的列上对齐这个系列，给出所需的结果：
In [75]: df1 / df2.loc['q']
Out[75]:
A B C D
x inf 2 1.000000 2
y inf 6 2.333333 4
z inf 10 3.666667 6
If you don't know/want to use the name of that one row, you can use squeezeto convert the one-column dataframe to a series: df1 / df2.squeeze()(see answer of @EdChum).
如果您不知道/想使用该行的名称，您可以使用squeeze将一列数据df1 / df2.squeeze()框转换为一系列：（请参阅@EdChum 的回答）。
回答by Zero
May be, you could order your df2columns same of df1and then divide on values
可能是，您可以对df2列进行相同的排序df1，然后对值进行除法
In [53]: df1.values/df2[df1.columns].values
Out[53]:
array([[ inf, 2. , 1. , 2. ],
[ inf, 6. , 2.33333333, 4. ],
[ inf, 10. , 3.66666667, 6. ]])
回答by EdChum
You can reorder the column and then call squeezeto flatten the array and then call div:
您可以对列重新排序，然后调用squeeze以展平数组，然后调用div：
In [114]:
df1= pd.DataFrame( [[1.,2.,3.,4.],[5.,6.,7.,8.],[9.,10.,11.,12.]] ,columns = ['A','B','C','D'], index = ['x','y','z'])
df2= pd.DataFrame( [[0.,1.,2.,3.]] ,columns = ['A','B','D','C'], index = ['q']) ?
df1.div(df2.ix[:,df1.columns].squeeze())
Out[114]:
A B C D
x inf 2 1.000000 2
y inf 6 2.333333 4
z inf 10 3.666667 6
df1/df2.ix[:,df1.columns].squeeze()also works but @Joris's answer is much nicer
df1/df2.ix[:,df1.columns].squeeze()也有效，但@Joris 的回答要好得多
EDIT
编辑
As pointed out by @joris the column reordering is unnecessary as pandas will naturally align against the columns anyway so:
正如@joris 所指出的，列重新排序是不必要的，因为无论如何Pandas都会自然地与列对齐：
df1.div(df2squeeze())
or
或者
df1./df2squeeze()
would work
会工作
