Rolling comparison between a value and a past window, with percentile/quantile

Rolling comparison between a value and a past window, with percentile/quantile

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Rolling comparison between a value and a past window, with percentile/quantile
Tag : python , By : msugar
Date : November 28 2020, 08:01 AM

seems to work fine Your code is so slow because you're using Python's own sum() instead of numpy.sum() or numpy.array.sum(); Python's sum() has to convert all the raw values to Python objects before doing the calculations, which is really slow. Just by changing sum(...) to np.sum(...) or (...).sum(), the runtime drops to under 20 seconds.

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Panda rolling window percentile rank

Tag : python , By : Kbotei
Date : March 29 2020, 07:55 AM
Hope this helps Your lambda receives a numpy array, which does not have a .rank method — it is pandas's Series and DataFrame that have it. You can thus change it to
pctrank = lambda x: pd.Series(x).rank(pct=True).iloc[-1]
def pctrank(x):
    n = len(x)
    temp = x.argsort()
    ranks = np.empty(n)
    ranks[temp] = (np.arange(n) + 1) / n
    return ranks[-1]

Use quantile to get percentile in r

Tag : r , By : Chris Woods
Date : March 29 2020, 07:55 AM
I hope this helps . It's basically what the error says - In the second example, you are providing 2 groups (probs = c(.3,.7)), which is fewer than the amount of labels you provide (c("L","M","B"))
Notice that in your first code snippet, seq(0,1 , by= 0.5) You generate 3 distinct values (0.0, 0.5, 1.0)

How to compute moving (or rolling, if you will) percentile/quantile for a 1d array in numpy?

Tag : pandas , By : micaleel
Date : March 29 2020, 07:55 AM
wish of those help We could create the sliding windows with np.lib.stride_tricks.as_strided, implemented as a function as strided_app -
In [14]: a = np.array([1, 5, 7, 2, 4, 6, 9, 3, 8, 10]) # input array

In [15]: W = 3 # window length

In [16]: np.percentile(strided_app(a, W,1), 50, axis=-1)
Out[16]: array([ 5.,  5.,  4.,  4.,  6.,  6.,  8.,  8.])
In [39]: np.pad(_, 1, 'constant', constant_values=(np.nan)) #_ is previous one
Out[39]: array([ nan,   5.,   5.,   4.,   4.,   6.,   6.,   8.,   8.,  nan])

calculate percentile using rolling window pandas

Tag : python , By : Val
Date : March 29 2020, 07:55 AM
it helps some times This is a bug, referenced in GH9413 and GH16211.
The reason, as given by the devs -
df.rolling(window=3, center=False).apply(lambda x: pd.Series(x).quantile(0.75))

0  NaN
1  NaN
2  2.5
3  2.5
4  2.5
5  2.5
6  2.5
7  2.5
8  2.5

when to use np.quantile and np.percentile?

Tag : python , By : user150744
Date : March 29 2020, 07:55 AM
hope this fix your issue They are equivalent. If you'd rather specify q from [0, 1], use np.quantile. For [0, 100], use np.percentile.
Docs: https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html
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