title: inner join/merge in pandas dataframe give more rows than left dataframe
body: <p>Here are how the dataframes columns look like. </p>
  
  <p>df1='device number', 'date', ....&lt;&lt;10 other columns>> 3500 records</p>
  
  <p>df2='device number', 'date', ....&lt;&lt;9 other columns>> 14,000 records</p>
  
  <p>In each data frame, neither 'device number', nor 'date' are unique. However, their combination is unique to identify a row. </p>
  
  <p>I am trying to form a new data frame which matches the rows from df1 and df2 where both device number and date are equal, and have all the columns from these df1 and df2. The pandas command I am trying is </p>
  
  <pre><code>df3=pd.merge(df1, df2, how='inner', on=['device number', 'date'])
  </code></pre>
  
  <p>However, df3 gives me a dataframe of shape (14,000, 21). The column number makes sense, but how can the inner join has more rows than any of the left dataframes? Does it mean I have a flaw in my understanding of inner join? Also, how can I achieve the result I described? </p>