Pandas：两个数据帧的行乘法
[英]Pandas: rowwise multiplication of two dataframes
问题描述
I have two dataframes;
我有两个数据框； A contains allocation fractions and B contains hourly volumes. A 包含分配分数，B 包含每小时量。 To get the right volume for each bus for a given hour, I need to multiply A with each row of dataframe B. For a given hour (x), this would be a simple multiplication of A * B.loc[x] .为了在给定小时内为每辆公共汽车获得正确的音量，我需要将 A 与 dataframe B 的每一行相乘。对于给定的小时 (x)，这将是A * B.loc[x]的简单乘法。
A = col_a col_b col_c col_d col_e
0 0.0 0.0 0.0 0.0 1.0
1 0.0 0.0 1.0 0.0 0.0
2 0.0 1.0 0.0 0.5 0.0
3 0.5 0.0 0.0 0.5 0.0
4 0.5 0.0 0.0 0.0 0.0
B = col_a col_b col_c col_d col_e
0 12881 598 154 180 0.0
1 12881 680 154 180 0.0
2 11617 806 154 180 0.0
3 12991 100 154 180 0.0
However, I want to do this multiplication for each hour at once and create a large multiindex dataframe C.但是，我想一次对每个小时进行一次乘法运算并创建一个大型多索引 dataframe C。
C = col_a col_b col_c col_d col_e
hr bus
0 0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 154.0 0.0 0.0
2 0.0 598.0 0.0 90.0 0.0
3 6440.5 0.0 0.0 90.0 0.0
4 6440.5 0.0 0.0 0.0 0.0
1 0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 154.0 0.0 0.0
2 0.0 680.0 0.0 90.0 0.0
I managed to create this dataframe with a list operation and overwriting the index of the resulting dataframe.我设法通过列表操作创建了这个 dataframe 并覆盖了生成的 dataframe 的索引。 I would not consider this a very good practice and wonder if there's a better approach that doesn't require an overwrite of the index?我不会认为这是一个很好的做法，并想知道是否有更好的方法不需要覆盖索引？
dfs = [A.mul(B.loc[i]) for i in B.index]
C = pandas.concat(dfs)
C.index = pandas.MultiIndex.from_product([B.index, A.index], names=['hr', 'bus'])
1 个解决方案
解决方案1
2 已采纳 2020-12-04 17:32:36
First "replicate" B DataFrame, reformatting the index:首先“复制” B DataFrame，重新格式化索引：
BB = pd.DataFrame(np.repeat(B.values, A.index.size, axis=0), columns=B.columns,
index=pd.MultiIndex.from_product((B.index, A.index), names=['hr', 'bus']))
Then compute the result:然后计算结果：
result = A.mul(BB, level=1)
The result is:结果是：
col_a col_b col_c col_d col_e
hr bus
0 0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 154.0 0.0 0.0
2 0.0 598.0 0.0 90.0 0.0
3 6440.5 0.0 0.0 90.0 0.0
4 6440.5 0.0 0.0 0.0 0.0
1 0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 154.0 0.0 0.0
2 0.0 680.0 0.0 90.0 0.0
3 6440.5 0.0 0.0 90.0 0.0
4 6440.5 0.0 0.0 0.0 0.0
2 0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 154.0 0.0 0.0
2 0.0 806.0 0.0 90.0 0.0
3 5808.5 0.0 0.0 90.0 0.0
4 5808.5 0.0 0.0 0.0 0.0
3 0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 154.0 0.0 0.0
2 0.0 100.0 0.0 90.0 0.0
3 6495.5 0.0 0.0 90.0 0.0
4 6495.5 0.0 0.0 0.0 0.0
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