Arthur G - 2 months ago 7x

Python Question

Is it possible to directly compute the product (or for example sum) of two columns without using

?`grouped.apply(lambda x: (x.a*x.b).sum()`

It is much (less than half the time on my machine) faster to use

df['helper'] = df.a*df.b

grouped= df.groupby(something)

grouped['helper'].sum()

df.drop('helper', axis=1)

But I don't really like having to do this.

It is for example useful to compute the weighted average per group. Here the lambda approach would be

`grouped.apply(lambda x: (x.a*x.b).sum()/(df.b).sum())`

and again is much slower than dividing the helper by b.sum().

Answer

I want to eventually build an embedded array expression evaluator (Numexpr on steroids) to do things like this. Right now we're working with the limitations of Python-- if you implemented a Cython aggregator to do `(x * y).sum()`

then it could be connected with groupby, but ideally you could write the Python expression as a function:

```
def weight_sum(x, y):
return (x * y).sum()
```

and that would get "JIT-compiled" and be about as fast as groupby(...).sum(). What I'm describing is a pretty significant (many month) project. If there were a BSD-compatible APL implementation I might be able to do something like the above quite a bit sooner (just thinking out loud).

Source (Stackoverflow)

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