title: Pandas merging dataframes and overwriting the data in the original df
body: <p>I'm trying to merge two pandas dataframes but I can't figure out how to get the result I need. These are the example versions of dataframes I'm looking at:</p>
  
  <pre><code>df1 = pd.DataFrame([["09/10/2019",None],["10/10/2019",None], ["11/10/2019",6],
                      ["12/10/2019",5], ["13/10/2019",3], ["14/10/2019",3],
                      ["15/10/2019",5],
                      ["16/10/2019",None]], columns = ['Date', 'A'])
  
  df2 = pd.DataFrame([["10/10/2019",3], ["11/10/2019",5], ["12/10/2019",6],
                      ["13/10/2019",1], ["14/10/2019",2], ["15/10/2019",4]],
                      columns = ['Date', 'A'])
  </code></pre>
  
  <p>I have checked the <a href="https://stackoverflow.com/questions/53645882/pandas-merging-101">Pandas merging 101</a> but still can't find the way to do it correctly. Essentially what I need using the same graphics as in the guide is this:</p>
  
  <p><a href="https://i.stack.imgur.com/dViKZ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/dViKZ.png" alt="enter image description here"></a></p>
  
  <p>i.e. I want to keep the data from df1 that falls outside the shared keys section, but within shared area I want df2 data from column 'A' to overwrite data from df1. I'm not even sure that <code>merge</code> is the right tool to use.</p>
  
  <p>I've tried using <code>df1 = pd.merge(df1, df2, how='right', on='Date')</code> with different options, but in most cases it creates two separate columns - <code>A_x</code> and <code>A_y</code> in the output. </p>
  
  <p>This is what I want to get as the end result:</p>
  
  <pre><code>         Date    A
  0  09/10/2019  NaN
  1  10/10/2019  3.0
  2  11/10/2019  5.0
  3  12/10/2019  6.0
  4  13/10/2019  1.0
  5  14/10/2019  2.0
  6  15/10/2019  4.0
  7  16/10/2019  NaN
  </code></pre>
  
  <p>Thanks in advance!</p>