ebbr | Empirical Bayes binomial estimation | Time Series Database library
kandi X-RAY | ebbr Summary
kandi X-RAY | ebbr Summary
Methods for empirical Bayes shrinkage and estimation on data with many observations of success/total counts. These methods are described in this series of blog posts on baseball batting averages, but can be applied to a variety of data types.
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QUESTION
I am trying to make an hourly mean of a dataframe in python, by taking into account the date info but also string info in a certain column. Please see the example below.
...ANSWER
Answered 2020-May-07 at 12:12Aggregate mean
with convert datetimes
to dates by Series.dt.date
:
QUESTION
I have the following lists:
...ANSWER
Answered 2020-Feb-20 at 12:03Lists are not really ideal for that kind of data. You should consider storing the data in e.g. dicts in the first place. However given input and output lists as above, we could first convert to dicts (using dict comprehensions) and then back to a double list using a list comprehension:
QUESTION
I'm looking to create a plot that looks similar to this one on David Robinson's variance explained blog:
I think I have it down except for the fill that goes between the credible intervals and under the posterior curve. If anyone knows how to do this it would be great to get some advice.
Here's some sample code:
...ANSWER
Answered 2017-Jan-27 at 12:17I usually do it by generating the data to describe the curve manually, adding zero y values for the min and max of the shaded region, and using geom_polygon()
.
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