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Questions - rolling-computation

Rolling and keep the threshold

I have a database in pandas with a date and a variable. I need to compare the values of the variable with its next three days and if any of its values are increased with respect to the initial value, ...
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Luisa Fernanda Velasquez Camac

python

pandas

database

rolling-computation

Votes: 0

Answers: 1

Latest Answer

Given the following toy dataframe: import pandas as pd df = pd.DataFrame( { "date": { 1: "2020-04-01", 2: "2020-04-02", 3...
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Laurent

Pandas rolling removes imaginary part of complex... defect or feature?

Pandas rolling appears only to return the real part of complex values... Am I missing something? a=np.array([[1+0j,2+0j,3+0j],[4j,5j,10+6j]]) pd.DataFrame(a.T).rolling(3).apply(lambda x: print(x) is N...
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alps

python

pandas

complex-numbers

rolling-computation

Votes: 0

Answers: 1

Latest Answer

I just changed the mentioned line in the warning ensure_float64 because it checks the values to be float64, as follows: values = values Then changed the dtype as it produces buffer error, as follows:...
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keramat

Python - subtraction inside rolling window

I need to make subtractions inside red frames as [20-10,60-40,100-70] that results in [10,20,30] Current code makes subtractions but I don't know how to define red frames seq = [10, 20, 40, 60, 7...
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leonardik

python

rolling-computation

Votes: 0

Answers: 2

Latest Answer

You can build a quick solution using the fact that seq[0::2] will give you every other element of seq starting at zero. So you can compute seq[1::2] - seq[0::2] to get this result. Without using any p...
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Didymus Prime

Pandas returns incorrect groupby rolling sum of zeros for float64 when having many groups

When doing groupby rolling in pandas with dtype float64, sum of zeros become an arbitrary small float when number of groups is large. For example, import pandas as pd import numpy as np np.random.see...
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Tim

python

pandas

dataframe

pandas-groupby

rolling-computation

Votes: 0

Answers: 1

Latest Answer

TLDR: this is a side effect of optimization; the workaround is to use a non-pandas sum. The reason is that pandas tries to optimize. Naive rolling window functions will take O(n*w) time. However, if w...
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Marat

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