CODEWITHSUNDEEP
pythonpandaspercentile
tel
tel

Reputation: 13997

Rank Pandas dataframe by quantile

I have a Pandas dataframe in which each column represents a separate property, and each row holds the properties' value on a specific date:

import pandas as pd

dfstr = \
'''         AC        BO         C       CCM        CL       CRD        CT        DA        GC        GF
2010-01-19  0.844135 -0.194530 -0.231046  0.245615 -0.581238 -0.593562  0.057288  0.655903  0.823997  0.221920
2010-01-20 -0.204845 -0.225876  0.835611 -0.594950 -0.607364  0.042603  0.639168  0.816524  0.210653  0.237833
2010-01-21  0.824852 -0.216449 -0.220136  0.234343 -0.611756 -0.624060  0.028295  0.622516  0.811741  0.201083'''
df = pd.read_csv(pd.compat.StringIO(dfstr), sep='\s+')

Using the rank method, I can find the percentile rank of each property with respect to a specific date:

df.rank(axis=1, pct=True)

Output:

             AC   BO    C  CCM   CL  CRD   CT   DA   GC   GF
2010-01-19  1.0  0.4  0.3  0.7  0.2  0.1  0.5  0.8  0.9  0.6
2010-01-20  0.4  0.3  1.0  0.2  0.1  0.5  0.8  0.9  0.6  0.7
2010-01-21  1.0  0.4  0.3  0.7  0.2  0.1  0.5  0.8  0.9  0.6

What I'd like to get instead is the quantile (eg quartile, quintile, decile, etc) rank of each property. For example, for quintile rank my desired output would be:

             AC   BO    C  CCM   CL  CRD   CT   DA   GC   GF
2010-01-19   5    2     2  4     1   1     3    4    5    3
2010-01-20   2    2     5  1     1   3     4    5    3    4
2010-01-21   5    2     2  4     1   1     3    4    5    3

I might be missing something, but there doesn't seem to a built-in way to do this kind of quantile ranking with Pandas. What's the simplest way to get my desired output?

Upvotes: 3

Views: 3407

Answers (2)

mr.polden2010
mr.polden2010

Reputation: 11

You can use now pd.qcut

df.apply(lambda x: pd.qcut(x, 5, labels=False)+1, axis=1)

Completed test case code

import pandas as pd
from io import StringIO

dfstr = \
'''         AC        BO         C       CCM        CL       CRD        CT        DA        GC        GF
2010-01-19  0.844135 -0.194530 -0.231046  0.245615 -0.581238 -0.593562  0.057288  0.655903  0.823997  0.221920
2010-01-20 -0.204845 -0.225876  0.835611 -0.594950 -0.607364  0.042603  0.639168  0.816524  0.210653  0.237833
2010-01-21  0.824852 -0.216449 -0.220136  0.234343 -0.611756 -0.624060  0.028295  0.622516  0.811741  0.201083'''

df = pd.read_csv(StringIO(dfstr), sep='\s+')

print('input:','\n',df)

input

                   AC        BO         C       CCM        CL       CRD   
2010-01-19  0.844135 -0.194530 -0.231046  0.245615 -0.581238 -0.593562  \
2010-01-20 -0.204845 -0.225876  0.835611 -0.594950 -0.607364  0.042603   
2010-01-21  0.824852 -0.216449 -0.220136  0.234343 -0.611756 -0.624060   

                  CT        DA        GC        GF  
2010-01-19  0.057288  0.655903  0.823997  0.221920  
2010-01-20  0.639168  0.816524  0.210653  0.237833  
2010-01-21  0.028295  0.622516  0.811741  0.201083  


df_out = df.apply(lambda x: pd.qcut(x, 5, labels=False)+1, axis=1)

print('\n','output:','\n', df_out)

output

             AC  BO  C  CCM  CL  CRD  CT  DA  GC  GF
2010-01-19   5   2  2    4   1    1   3   4   5   3
2010-01-20   2   2  5    1   1    3   4   5   3   4
2010-01-21   5   2  2    4   1    1   3   4   5   3

Upvotes: 0

Erfan
Erfan

Reputation: 42946

Method 1 mul & np.ceil

You were quite close with the rank. Just multiplying by 5 with .mul to get the desired quantile, also rounding up with np.ceil:

np.ceil(df.rank(axis=1, pct=True).mul(5))

Output

             AC   BO    C  CCM   CL  CRD   CT   DA   GC   GF
2010-01-19  5.0  2.0  2.0  4.0  1.0  1.0  3.0  4.0  5.0  3.0
2010-01-20  2.0  2.0  5.0  1.0  1.0  3.0  4.0  5.0  3.0  4.0
2010-01-21  5.0  2.0  2.0  4.0  1.0  1.0  3.0  4.0  5.0  3.0

If you want integers use astype:

np.ceil(df.rank(axis=1, pct=True).mul(5)).astype(int)

Or even better Since pandas version 0.24.0 we have nullable integer type: Int64.
So we can use :

np.ceil(df.rank(axis=1, pct=True).mul(5)).astype('Int64')

Output

            AC  BO  C  CCM  CL  CRD  CT  DA  GC  GF
2010-01-19   5   2  2    4   1    1   3   4   5   3
2010-01-20   2   2  5    1   1    3   4   5   3   4
2010-01-21   5   2  2    4   1    1   3   4   5   3

Method 2 scipy.stats.percentileofscore

d = df.apply(lambda x: [np.ceil(stats.percentileofscore(x, a, 'rank')*0.05) for a in x], axis=1).values

pd.DataFrame(data=np.concatenate(d).reshape(d.shape[0], len(d[0])), 
             columns=df.columns, 
             dtype='int', 
             index=df.index)

Output

            AC  BO  C  CCM  CL  CRD  CT  DA  GC  GF
2010-01-19   5   2  2    4   1    1   3   4   5   3
2010-01-20   2   2  5    1   1    3   4   5   3   4
2010-01-21   5   2  2    4   1    1   3   4   5   3

Upvotes: 6

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