Pivoting pandas dataframe by rank on id

user_11538255 0
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I'm currently trying to pivot my pandas DataFrame by 'id' on 'rank'

print(df)

     id  rank  year  
0   key0  1    2011  
1   key0  2    2012  
2   key0  3    2013  
3   key1  1    2014  
4   key1  2    2015  
5   key1  3    2016  
6   key2  1    2017 
7   key2  2    2018 
8   key2  3    2019

Depending on the max('rank'), I want to create as many 'years' columns and give them values according to the ascending rank

print(df)

     id  rank1  year1  rank2  year2  rank3   year3  
0   key0   1     2011    2     2012    3      2013
1   key1   1     2014    2     2015    3      2016  
2   key2   1     2017    2     2018    3      2019

I tried my own solution (currently working, but I have ~2M rows and is not very effective)

df2= df.melt(id_vars=["id", "rank"], value_vars=[elem for elem in df.columns if elem not ['id','rank']])
df2['col_name'] =df2['variable']+ (df2['rang']-1).astype('str')
df2.value.fillna(0, inplace = True)
df2= pd.pivot_table(df2, index=["id"], columns=["col_name"], values="value", aggfunc=max)

I know that it is not the optimal solution and is memory consuming, here is why I'm asking for a better solution

Thanks in advance

pandas pivot python-3.x python
4 Answers

Use DataFrame.sort_values with DataFrame.pivot, sorting MultiIndex by DataFrame.sort_index and then flatten it by f-strings:

df1 = (df.sort_values(['id','rank'])
         .pivot(index="id",columns="rank", values=["year","rank"])
         .sort_index(axis=1, level=1))
df1.columns = [f'{a}{b}' for a, b in df1.columns]
df1 = df1.reset_index()
print (df1)
     id  rank1  year1  rank2  year2  rank3  year3
0  key0      1   2011      2   2012      3   2013
1  key1      1   2014      2   2015      3   2016
2  key2      1   2017      2   2018      3   2019
user_29010020

While this doesn't mimic the exact output, a simpler approach involves performing a pivot right off the bat.

df.pivot(index="id", columns="rank", values="year")

rank     1     2     3
id                    
key0  2011  2012  2013
key1  2014  2015  2016
key2  2017  2018  2019

I personally don't like having numbers as my column headers, so I would:

df.pivot(index="id", columns="rank", values="year").rename(columns="rank_{}".format)

rank  rank_1  rank_2  rank_3
id                          
key0  2011    2012    2013  
key1  2014    2015    2016  
key2  2017    2018    2019
user_142784480

Let's use .unstack with cumcount() to create a pseudo-index before unpivoting and sort_index().

Also assuming your rank column is already sorted.

df1 = df.set_index(['id',df.groupby('id').cumcount() + 1]).unstack(1).sort_index(1,1)

#flatten columns.
df1.columns = [f'{x}_{y}' for x,y in df1.columns]

print(df1)
      rank_1  year_1  rank_2  year_2  rank_3  year_3
id                                                  
key0       1    2011       2    2012       3    2013
key1       1    2014       2    2015       3    2016
key2       1    2017       2    2018       3    2019
user_93751020

You can do this:

f=pd.DataFrame()
for name,group in df.groupby('rank'):
    group=group.rename(columns={'rank':'rank{}'.format(name),'year':'year{}'.format(name)})
    f=pd.concat([f.reset_index(drop=True),group.reset_index(drop=True)],axis=1)


#then, drop duplicate columns
f = f.loc[:,~f.columns.duplicated()]

f

     id  rank1  year1  rank2  year2  rank3  year3
0  key0      1   2011      2   2012      3   2013
1  key1      1   2014      2   2015      3   2016
2  key2      1   2017      2   2018      3   2019
user_71834440
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