如何在 Pandas dataframe 的每一行中获取包含值的预定义等价类名称的列？

Question

# import package
import pandas as pd

---

The problem

I have a dataframe:

data = {'row1': ['a', 'A', 'B', 'b'],
        'row2': ['a', 'b', 'c', 'd'],
        'row3': ['a', 'b', 'd', 'D']}
df = pd.DataFrame.from_dict(data, orient='index', columns=['col'+str(x) for x in range(4)])

which looks like:

I also have a list of equivalence classes. Each equivalence class consists of items which are taken as equivalent.

equivalenceClasses={'classA':['a','A'],
                    'classB':['b','B'],
                    'classC':['c','C'],
                    'classD':['d','D']}

I would like to create a dataframe in which the rows in the above dataframe are replaced by the names of the equivalence classes the letters in the row belong to . (Each equivalence class should appear no more than once in a row, and we should use NaN s to post-pad rows in which not all columns are fille by a name of an equivalence class). Ie I want this output:

---

My method

I achieve the goal by:

def differentClasses(colvalues):
    return list(set([equivalenceClassName for colvalue in colvalues
                                          for equivalenceClassName, equivalenceClass in zip(equivalenceClasses.keys(),
                                                                                   equivalenceClasses.values())
                                          if colvalue in equivalenceClass]))

( On list comprehension , on nested list comprehension .)

df['classes'] = df.apply(lambda row : differentClasses(row['col'+str(x)] for x in range(4)), axis = 1) 

(Influenced by this .)

The df at this point looks like this:

Finish by:

result_df = pd.DataFrame(df['classes'].tolist(),index=df.index,columns=['classcol'+str(x) for x in range(4)])

result_df is the desired output above.

---

The question

Is there a more standard way of doing this? Something like:

df.equivalenceClassify(equivalenceClassList)

and I get my output?

Answer 1

We need create the new dict based on your original equivalenceClasses , then just do replace

from collections import ChainMap
d = dict(ChainMap(*[dict.fromkeys(y,x) for x , y in equivalenceClasses.items()]))
df = df.replace(d)
Out[299]: 
        col0    col1    col2    col3
row1  classA  classA  classB  classB
row2  classA  classB  classC  classD
row3  classA  classB  classD  classD

Then

df = df.mask(df.apply(pd.Series.duplicated,1))
Out[307]: 
        col0    col1    col2    col3
row1  classA     NaN  classB     NaN
row2  classA  classB  classC  classD
row3  classA  classB  classD     NaN