如何使用列名而不是值创建秩矩阵？

Question

I am quite new to Python. I have a data frame like the following

       book 1      book 2     book 3  
user 1  0.05       0.47       0.09
user 2  0.3        0.01       0.35

I want it to be ranked based on the values(descending order) and the output to be the following:

        Rank 1      Rank 2      Rank 3 
user1   book 2      book 3      book 1
user2   book 3      book 1      book 2  

this is how the matrix looks in real task

I would appreciate your help.

Thank you.

Answer 1

Here's one approach.

pandas.DataFrame([df.columns[x] for x in np.argsort(-df.values)], 
                 index=df.index, 
                 columns=['rank' + str(i + 1) for i in range(df.shape[1])])

       rank1  rank2
user1  book2  book1
user2  book1  book2

Answer 2

new_cols = {'book %i' % i: 'Rank %i' % i for i in range(1, df.shape[1]+1)}
df.apply(lambda s: s.index[s.argsort()][::-1], axis=1).rename(new_cols, axis=1)

returns

        Rank 1  Rank 2  Rank 3
user 1  book 2  book 3  book 1
user 2  book 3  book 1  book 2

Answer 3

You can use apply:

(
     df.apply(lambda x: x.sort_values().index[::-1], axis=1)
     .rename(columns=lambda x: x.replace('book','Rank'))
)
Out[9]: 
        Rank 1  Rank 2
user 1  book 2  book 1
user 2  book 1  book 2

Answer 4

Use numpy.argsort :

Notice: as @ely mentioned in comment, axis=1 is not necessary

df = pd.DataFrame((-df.values).argsort() + 1, index=df.index)
#same as
#df = pd.DataFrame((-df.values).argsort(axis=1) + 1, index=df.index)
print (df)
        0  1  2
user 1  2  3  1
user 2  3  1  2

Change columns names and values in DataFrame :

df.columns = ['Rank {}'.format(x+1) for x in df.columns]
df = 'book ' + df.astype(str)
print (df)
        Rank 1  Rank 2  Rank 3
user 1  book 2  book 3  book 1
user 2  book 3  book 1  book 2