title: Test equality of a dataframe value with Pandas
body: <p>When doing this:</p>
  <pre><code>import pandas as pd
  df = pd.DataFrame({'A': ['1.0', '1.1', '1.2', '1.3'], 'B': ['a', 'b', 'c', 'd']})
  
  row1 = df[df['A'] == '1.1']
  
  if row1['B'] == 'b':  
      print('hello')
  </code></pre>
  <p>we get:</p>
  <blockquote>
  <p>ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().</p>
  </blockquote>
  <p>Why is it ambiguous?</p>
  <p>It is solved indeed if I use <code>any()</code> or <code>all()</code> but here I'm reluctant about adding <code>any</code> or <code>all</code> without fully understanding why it is necessary. Usually <code>any</code> or <code>all</code> is necessary when we have a list of boolean values, but here we have only one such boolean.</p>
  <p>TL;DR: how to test the equality of one dataframe value with Pandas?</p>