title: Pandas: find matching rows in two dataframes (without using `merge`)
body: <p>Let's suppose I have these two dataframes with the same number of columns, but possibly different number of rows:</p>
  <pre><code>tmp = np.arange(0,12).reshape((4,3))
  df = pd.DataFrame(data=tmp) 
  
  tmp2 = {'a':[3,100,101], 'b':[4,4,100], 'c':[5,100,3]}
  df2 = pd.DataFrame(data=tmp2)
  
  print(df)
     0   1   2
  0  0   1   2
  1  3   4   5
  2  6   7   8
  3  9  10  11
  
  print(df2)
       a    b    c
  0    3    4    5
  1  100    4  100
  2  101  100    3
  </code></pre>
  <p>I want to verify if the rows of <code>df2</code> are matching any rows of  <code>df</code>, that is I want to obtain a series (or an array) of boolean values that gives this result:</p>
  <pre><code>0     True
  1    False
  2    False
  dtype: bool
  </code></pre>
  <p>I think something like the <code>isin</code> method should work, but I got this result, which results in a dataframe and is wrong:</p>
  <pre><code>print(df2.isin(df))
         a      b      c
  0  False  False  False
  1  False  False  False
  2  False  False  False
  </code></pre>
  <p>As a constraint, I wish to not use the <code>merge</code> method, since what I am doing is in fact a check on the data before applying merge itself.
  Thank you for your help!</p>