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Questions - percentile

Percentile rank over multiple partitions averages

Assume I have a table like this id cnt tier 1 100 gold 2 200 silver 3 300 bronze 4 400 bronze 5 500 bronze 6 600 gold 7 700 silver 8 800 silver 9 900 silver 10 1000 silver ...
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xiaolong

sql

hive

analytics

window-functions

percentile

Votes: 0

Answers: 4

Latest Answer

I'd compute the averages and join it to the original table. To rank with respect to the averages, I added a placeholder row to hold the original row. select * from ( select *, PERCENT_RANK() OVER(PA...
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Clover

How to find the contour graph and highlighted with 90 percentiles?

I have a raster image whose value ranges from 1 to 10. I want to find the 90 percentile of mine raster data. And need to find the contour graph by highlighting the area having the 90 percentile. I wan...
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user_3264

r

raster

contour

percentile

Votes: 0

Answers: 1

Latest Answer

Obviously, don't have your data, but we can make an example raster like this: r <- raster(t(volcano[,ncol(volcano):1])) From now on, the following code should work with your own raster too. We can...
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Allan Cameron

PySpark use percentile_approx to get P95 value is much greater than the max value of same column

I want to get P95 value of a column after groupBy, but when I check the result, I find that the P95 value is greater than the max value. My usage is as follows: from pyspark.sql.types import StructTyp...
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Arik

apache-spark

pyspark

apache-spark-sql

aggregate-functions

percentile

Votes: 0

Answers: 1

Latest Answer

It's not because of percentile_approx. It's because your "maxvalue" column is actually not of float type. In your fixed code, the type of this column was changed to float, so then it worked ...
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ZygD

`groupby` - `qcut` but with condition

I have a dataframe as follow: key1 key2 val 0 a x 8 1 a x 6 2 a x 7 3 a x 4 4 a x 9 5 a x 1 6 a x 2 7 a x 3 8 a ...
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PTQuoc

pandas

dataframe

sorting

pandas-groupby

percentile

Votes: 0

Answers: 1

Latest Answer

A first approach is to create a custom function: def discretize(df): bins = pd.qcut(df.loc[df['key2'] == 'x', 'val'], 2, labels=False, retbins=True)[1] bins = [-np.inf] + bins[1:-1].tolist() +...
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Corralien

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