title: Read Excel with varying number of rows per column to dataframe without NAN
body: <p>I have an excel file that looks like:</p>
  <p><a href="https://i.stack.imgur.com/55DOz.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/55DOz.jpg" alt="sample input" /></a></p>
  <p>I would like to read this into pandas dataframe and avoid getting NAN values. The reason I am trying to avoid getting NAN is that later on, I want to create a simple contingency table similar to this</p>
  <p><a href="https://i.stack.imgur.com/OMia3.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/OMia3.jpg" alt="sample output" /></a></p>
  <p>and NAN values create issues in this contingency table. Is there any elegant way of doing this without reading columns separately, using value_counts(), and concatenating the series, as below?</p>
  <pre><code>df_1=pd.read_excel('Book1.xlsx', usecols='A')
  df_2=pd.read_excel('Book2.xlsx', usecols='B')
  value_c_1 = df_1.value_counts()
  value_c_2 = df_2.value_counts()
  pd.concat([value_c_1, value_c_2], axis=1)
  </code></pre>
  <p>There must be an elegant way of doing this; my apologies if the answer is so obvious; I searched for it before posting this question.</p>