pandas 如何从另一个数据框中用一行减去数据框中的所有行？
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How to subtract all rows in a dataframe with a row from another dataframe?
提问by jonas
I would like to subtract all rows in a dataframe with one row from another dataframe. (Difference from one row)
我想从另一个数据帧中减去一行数据帧中的所有行。（与一排不同）
Is there an easy way to do this? Like df-df2)?
是否有捷径可寻？喜欢df-df2）？
df = pd.DataFrame(abs(np.floor(np.random.rand(3, 5)*10)),
... columns=['a', 'b', 'c', 'd', 'e'])
df
Out[18]:
a b c d e
0 8 9 8 6 4
1 3 0 6 4 8
2 2 5 7 5 6
df2 = pd.DataFrame(abs(np.floor(np.random.rand(1, 5)*10)),
... columns=['a', 'b', 'c', 'd', 'e'])
df2
a b c d e
0 8 1 3 7 5
Here is an output that works for the first row, however I want the remaining rows to be detracted as well...
这是适用于第一行的输出，但是我希望其余行也被减损......
df-df2
a b c d e
0 0 8 5 -1 -1
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
回答by unutbu
Pandas NDFrames generally try to perform operations on items with matching indices. df - df2only performs subtraction on the first row, because the 0indexed row is the only row with an index shared in common.
Pandas NDFrames 通常尝试对具有匹配索引的项目执行操作。df - df2只对第一行进行减法运算，因为0索引行是唯一共享索引的行。
The operation you are looking for looks more like a NumPy array operation performed with "broadcasting":
您正在寻找的操作看起来更像是使用“广播”执行的 NumPy 数组操作：
In [21]: df.values-df2.values
Out[21]:
array([[ 0, 8, 5, -1, -1],
[-5, -1, 3, -3, 3],
[-6, 4, 4, -2, 1]], dtype=int64)
To package the result in a DataFrame:
要将结果打包到 DataFrame 中：
In [22]: pd.DataFrame(df.values-df2.values, columns=df.columns)
Out[22]:
a b c d e
0 0 8 5 -1 -1
1 -5 -1 3 -3 3
2 -6 4 4 -2 1
回答by Jeff
You can do this directly in pandas as well. (I used df2 = df.loc[[0]])
您也可以直接在 Pandas 中执行此操作。（我用过df2 = df.loc[[0]]）
In [80]: df.sub(df2,fill_value=0)
Out[80]:
a b c d e
0 0 0 0 0 0
1 7 6 0 7 8
2 4 4 3 6 2
[3 rows x 5 columns]
回答by IPV3001
Alternatively you could simply use the apply function on all rows of df.
或者，您可以简单地对 df 的所有行使用 apply 函数。
df3 = df.apply(lambda x: x-df2.squeeze(), axis=1)
# axis=1 because it should apply to rows instead of columns
# squeeze because we would like to substract Series
