你可以使用np.argsort http://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html找到的索引n每行最大的项目:
import numpy as np
import pandas as pd
df = pd.DataFrame({'id': [1, 2, 3, 4, 5],
'p1': [0, 0, 1, 1, 2],
'p2': [9, 2, 3, 5, 3],
'p3': [1, 3, 10, 3, 7],
'p4': [4, 4, 7, 1, 10]})
df = df.set_index('id')
nlargest = 3
order = np.argsort(-df.values, axis=1)[:, :nlargest]
result = pd.DataFrame(df.columns[order],
columns=['top{}'.format(i) for i in range(1, nlargest+1)],
index=df.index)
print(result)
yields
top1 top2 top3
id
1 p2 p4 p3
2 p4 p3 p2
3 p3 p4 p2
4 p2 p3 p1
5 p4 p3 p2