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ajcr
Computationally efficient rolling window iterators for Python
It would be interesting to try and implement an algorithm to find the longest increasing subsequence in a sliding window. Such an approach is described in *Albert et al.*, 2004:...
Essentially a rolling version of Python's `in` operator. Return True if the window contains a given string, e.g.: ```python >>> seq = 'rollrollingndsfw' >>> r = rolling.Contains(seq, window_size=10, match='rolling') >>>...
TLDR; don't use skip lists I've found that a sorted list implemented with standard list + bisect module will be faster for tracking median than rolling's implementation up to about...
The rolling summation of float values is not precise enough in the calculation of variance/std. ```python >>> import rolling >>> list(rolling.Std([0,1,1,1], 3)) [0.5773502691896258, 7.450580596923828e-09] ``` The first value is correct....
Now rolling.apply can roll window at one-dimensional,will be able to support multidimensional in the future?Like that:  I find it will be finish in that "https://gist.github.com/seberg/3866040".
Hi, thanks for the good effort you have put in this project already. May I ask for an enhancement of the documentation? It would be good to have an overview...
Possible algorithms to implement over a rolling window: - [ ] palindromic / longest palindrome (Manacher's algorithm) - [x] monotonicity - [ ] convexity
Short question, is it somehow possible to extend this to handle NaN, like numpy `nanmedian`?
It seems that pandas has not provided iterators for rolling/ewm functions, and your project is really nice structured. Maybe another base class is needed for iterators of ewm statistics.
- [ ] PROD, rolling mean of X * Y: 1/window * sum(x*y) - [ ] COV, rolling covariance of X, Y, PROD - mean(X) * mean(Y) - [ ]...