<p>我认为两列都需要<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.duplicated.html" rel="nofollow noreferrer">^{<cd1>}</a>:</p>
<pre><code>df = df[df.duplicated(['Name','Value'], keep=False)]
print (df)
Name Value
1 B 219
2 B 219
</code></pre>
<p>但是如果需要输出按级别<code>Value</code>过滤的计数值:</p>
<pre><code>s = df.groupby("Value")["Name"].value_counts()
print (s)
df1 = s[s.index.get_level_values('Value').duplicated(keep=False)].reset_index(name='count')
print (df1)
Value Name count
0 219 B 2
1 219 D 1
</code></pre>
<p>另一种解决方案是首先通过<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.reset_index.html" rel="nofollow noreferrer">^{<cd4>}</a>创建<code>DataFrame</code>:</p>
<pre><code>df2 = df.groupby("Value")["Name"].value_counts().reset_index(name='count')
print (df2)
Value Name count
0 201 A 1
1 219 B 2
2 219 D 1
3 222 D 1
4 704 C 1
df1 = df2[df2['Value'].duplicated(keep=False)]
print (df1)
Value Name count
1 219 B 2
2 219 D 1
</code></pre>