title: Convert values to negative on the condition of values from another column
body: <p>I have a df where I want to convert the value in column <code>b</code> to a negative number if the corresponding value in column <code>a</code> is not <code>NaN</code>.</p>
  <p>Here is my code:</p>
  <pre><code># create a test df
  df = pd.DataFrame(np.array([[12, 34], [67, 98], [np.nan, 6], [23, 45], [np.nan, 5], [5, 82], [61, 92]]), columns=['a', 'b'])
  
  print(df)
      a   b
  0   12.0    34.0
  1   67.0    98.0
  2   NaN     6.0
  3   23.0    45.0
  4   NaN     5.0
  
  
  # the code to convert b to negative if a is not NaN
  df['b'] = df['b'].apply(lambda x: -x if df['a'] != pd.isnull else x)
  </code></pre>
  <p>This is error is receive:</p>
  <pre><code>ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()
  </code></pre>
  <p>I tried to look up the error code but I don't understand what is happening. I am using lambda but not sure if there is a better approach. The df should look like this:</p>
  <pre><code>
      a   b
  0   12.0    -34.0
  1   67.0    -98.0
  2   NaN     6.0
  3   23.0    -45.0
  4   NaN     5.0
  </code></pre>