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The dataframe below has 4 columns: runner_name,race_date, height_in_inches,top_ten_finish.

I want to groupby race_date, and if the runner finished in the top ten for that race_date, rank his height_in_inches among only the other runners who finished in the top ten for that race_date. How would I do this?

This is the original dataframe:

>>> import pandas as pd >>> d = {"runner":['mike','paul','jim','dave','douglas'], ... "race_date":['2019-02-02','2019-02-02','2020-02-02','2020-02-01','2020-02-01'], ... "height_in_inches":[72,68,70,74,73], ... "top_ten_finish":["yes","yes","no","yes","no"]} >>> df = pd.DataFrame(d) >>> df runner race_date height_in_inches top_ten_finish 0 mike 2019-02-02 72 yes 1 paul 2019-02-02 68 yes 2 jim 2020-02-02 70 no 3 dave 2020-02-01 74 yes 4 douglas 2020-02-01 73 no >>>

and this is what I'd like the result to look like. Notice how if they didn't finish in the top 10 of a race, they get a value of 0 for that new column.

 runner race_date height_in_inches top_ten_finish if_top_ten_height_rank 0 mike 2019-02-02 72 yes 1 1 paul 2019-02-02 68 yes 2 2 jim 2020-02-02 70 no 0 3 dave 2020-02-01 74 yes 1 4 douglas 2020-02-01 73 no 0

Thank you!

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2 Answers 2

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4

We can do groupby + filter with rank

df['rank']=df[df.top_ten_finish.eq('yes')].groupby('race_date')['height_in_inches'].rank(ascending=False) df['rank'].fillna(0,inplace=True) df Out[87]: runner race_date height_in_inches top_ten_finish rank 0 mike 2019-02-02 72 yes 1.0 1 paul 2019-02-02 68 yes 2.0 2 jim 2020-02-02 70 no 0.0 3 dave 2020-02-01 74 yes 1.0 4 douglas 2020-02-01 73 no 0.0
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2

You can filter and rank on groupby() then assign back:

df['if_top_ten_height_rank'] = (df.loc[df['top_ten_finish']=='yes','height_in_inches'] .groupby(df['race_date']).rank(ascending=False) .reindex(df.index, fill_value=0) .astype(int) )

Output:

 runner race_date height_in_inches top_ten_finish if_top_ten_height_rank -- -------- ----------- ------------------ ---------------- ------------------------ 0 mike 2019-02-02 72 yes 1 1 paul 2019-02-02 68 yes 2 2 jim 2020-02-02 70 no 0 3 dave 2020-02-01 74 yes 1 4 douglas 2020-02-01 73 no 0
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