Comments (3)
Note that (2) is worse because we always accumulate count
with xarray because min_count=1
by default. Potentially this could be optimized (I don't remember if I did)
from flox.
About ml31415/numpy-groupies#3 I'm not categorically against adding multiple aggregations in one go. It's mainly, that so far I considered the setup overhead of aggregate
as small enough to not be worth making the API more complicated. I'd argue this is still true for the 1D case, as it doesn't do more than the most necessary type and size checks. I didn't do any benchmarks, but if the raveling/unraveling should turn out to be a bottleneck, sure, we should try to find a better solution.
As you mentioned bincount
, there is still a 2x-4x speed up to be gained by using the numba
version compared to the bincount
-depending numpy-only version (1D case).
from flox.
if the raveling/unraveling should turn out to be a bottleneck, sure, we should try to find a better solution.
In my benchmarks this was ~25-30% of the time for nd array
, 1D group_idx
though ml31415/numpy-groupies#77 should reduce that
from flox.
Related Issues (20)
- Reporting a vulnerability HOT 1
- always factorize early
- optimize groupby for resample
- Add engine="numbagg"
- AttributeError: 'DataArrayResample' object has no attribute '_unique_coord'
- Test failure on i386: ValueError: bins must be monotonically increasing or decreasing HOT 4
- More Groupers / user stories / strategies HOT 2
- Support xarray grouper objects in xarray interface
- How to create two groups from two lists of dimension labels, and apply "sum" to each group HOT 2
- Using Xarray and Flox for custom non-aggregation functions HOT 3
- use engine flox if array is ordered? HOT 2
- "most common" Aggregator with Dask HOT 6
- Address repo-review comments
- more cohorts optimization when chunksize == 1
- add cftime benchmarks
- Optimize `split_every` HOT 4
- Error when data variables have different dimensions HOT 2
- Flox seems much slower in some cases? HOT 2
- possible support for sparse arrays HOT 2
- Examples in docs can be hard to read in dark mode HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from flox.