[英]Fastest way to compute weighted sum of dataframe across rows
I have a dataframe with some columns.我有一个包含一些列的数据框。 I'd like to apply some transformation to one column and use it as a weight for computing a weighted sum of the other columns.
我想对一列应用一些转换,并将其用作计算其他列的加权和的权重。 The issue is the way I'm doing it is currently taking too long.
问题是我现在做的方式太长了。 Is there a faster way to do this?
有没有更快的方法来做到这一点?
I'm currently calculating a new column, transposing, and using df.dot
as suggested by almost all answers .我目前正在计算一个新列,转置,并按照几乎所有答案的建议使用
df.dot
。 The issue is that I have an extremely large dataframe and so this method is taking a long time.问题是我有一个非常大的数据框,所以这种方法需要很长时间。
For example, given the following df例如,给定以下 df
col1 col2 col3
0.1 0.2 0.3
1.4 1.5 1.6
1.9 1.8 1.7
I create a new column, weights, that is 1/col3
我创建了一个新列,权重,即
1/col3
col1 col2 col3 weight
0.1 0.2 0.3 3.333
1.4 1.5 1.6 0.625
1.9 1.8 1.7 0.588
and then I transpose and df.dot
against the weight to get然后我对重量进行转置和
df.dot
以获得
col1 col2
2.32 2.66
I check linked answers and there is not usednp.dot
, but DataFrame.dot
, I hope this should be faster , but if use large DataFrames without huge RAM, it should be still slow:我检查了链接的答案,没有使用
np.dot
,而是DataFrame.dot
,我希望这应该更快,但是如果使用没有巨大 RAM 的大型 DataFrames,它应该仍然很慢:
w = 1 / df.col3
arr = np.dot(df.to_numpy().T, w.to_numpy())
df1 = pd.DataFrame([arr], columns=df.columns)
print (df1)
col1 col2 col3
0 2.32598 2.66299 3.0
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