dot和str.split的单向：

import numpy as np

df2 = df1.dot(df1.columns+",")
         .str.split(",", expand=True)
         .replace(r'^\s*$', np.nan, regex=True)
         .rename_axis(None)
df2.columns = [f'column{num}' for num in range(1, len(df2.columns)+1)]

>>> df2

    column1 column2 column3 column4
1         w       y       z     NaN
2         x       y     NaN     NaN
3         y       z     NaN     NaN

请尝试：

out = np.full(df1.shape, np.nan, dtype='object')

# mask valid columns for each row
mask = np.arange(df1.shape[1]) < df1.sum(1).values[:,None]

out[mask] = np.where(df1, df1.columns, np.nan)[df1]

out = pd.DataFrame(out)

您可以先使用特殊乘法（True -〉1，1 *'w' -〉'w' / False -〉0，0 *'w' -〉''）将列名Map到单元格
然后使用自定义键对行进行独立排序（isinstance(w, float)将在末尾推送NaN/float）

cols = [f'column{num}' for num in range(1, len(df1.columns) + 1)]
(df1*df1.columns).where(df1).apply(lambda r: pd.Series(sorted(r, key=lambda x: isinstance(x, float)),
                                                       index=cols), axis=1)

输出：

column1 column2 column3  column4
index                                 
1           w       y       z      NaN
2           x       y     NaN      NaN
3           y       z     NaN      NaN

在您的情况下，执行dot，然后执行split

out = df.dot(df.columns+',').str[:-1].str.split(',',expand=True).reindex(columns= np.arange(df.shape[1]))
Out[34]: 
       0  1     2   3
index                
1      w  y     z NaN
2      x  y  None NaN
3      y  z  None NaN