How to avoid mutiple columns on Pandas.Merge
<p>Imagine I have the following DataFrames on Pandas:</p>

<pre><code>In [7]: A= pd.DataFrame([['foo'],['bar'],['quz'],['baz']],columns=['key'])

In [8]: A['value'] = 'None'

In [9]: A
Out[9]:
   key value
0  foo  None
1  bar  None
2  quz  None
3  baz  None

In [10]: B = pd.DataFrame([['foo',5],['bar',6],['quz',7]],columns= ['key','value'])

In [11]: B
Out[11]:
   key  value
0  foo      5
1  bar      6
2  quz      7

In [12]: pd.merge(A,B, on='key', how='outer')
Out[12]:
   key value_x  value_y
0  foo    None        5
1  bar    None        6
2  quz    None        7
3  baz    None      NaN
</code></pre>

<p>But what I want is (avoiding the repeat column basically):</p>

<pre><code>   key  value
0  foo  5
1  bar  6
2  quz  7
3  baz  NaN
</code></pre>

<p>I suppose I can take the output and drop the <code>_x</code> value and rename the <code>_y</code> but that seems like an overkill. On SQL this would be trivial.</p>

<p>EDIT:</p>

<p>John as recomended to use:</p>

<pre><code>In [1]: A.set_index('key', inplace=True)
        A.update(B.set_index('key'), join='left', overwrite=True)
        A.reset_index(inplace=True)
</code></pre>

<p>This works and does what I asked for.</p>