<p>我认为您可以将<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_values.html" rel="nofollow noreferrer">^{<cd1>}</a>与{a2}一起用于断开对齐行:</p>
<pre><code>print (df52.apply(lambda row: row.sort_values().values, axis=1))
one two
0 1.0 4.0
1 2.0 3.0
2 2.0 3.0
3 1.0 4.0
</code></pre>
<p>或者:</p>
^{pr2}$
<hr/>
<p>如果使用<code>print</code>,则得到排序输出-如果在前面添加新列,则需要更改<code>Series</code>中所选行的位置<code>DataFrame</code>中的列是什么:</p>
<pre><code>top_1_should = df52.apply(lambda row: row.sort_values()[0], 1)
top_2_should = df52.apply(lambda row: row.sort_values()[1], 1)
df52['top_1_is'] = df52.apply(lambda row: row.sort_values()[0], 1)
df52['top_1_should'] = top_1_should
df52['top_2_is'] = df52.apply(lambda row: row.sort_values()[1], 1)
df52['top_2_is'] = df52.apply(lambda row: print(row.sort_values()), 1)
one 1.0
top_1_is 1.0
top_1_should 1.0
top_2_is 1.0
two 4.0
Name: 0, dtype: float64
one 2.0
top_1_is 2.0
top_1_should 2.0
top_2_is 2.0
two 3.0
Name: 1, dtype: float64
two 2.0
top_1_is 2.0
top_1_should 2.0
top_2_is 2.0
one 3.0
Name: 2, dtype: float64
two 1.0
top_1_is 1.0
top_1_should 1.0
top_2_is 1.0
one 4.0
Name: 3, dtype: float64
</code></pre>