title: Deleting multiple columns based on column names in Pandas
body: <p>I have some data and when I import it, I get the following unneeded columns. I'm looking for an easy way to delete all of these.</p>
  <pre><code>'Unnamed: 24', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27',
  'Unnamed: 28', 'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31',
  'Unnamed: 32', 'Unnamed: 33', 'Unnamed: 34', 'Unnamed: 35',
  'Unnamed: 36', 'Unnamed: 37', 'Unnamed: 38', 'Unnamed: 39',
  'Unnamed: 40', 'Unnamed: 41', 'Unnamed: 42', 'Unnamed: 43',
  'Unnamed: 44', 'Unnamed: 45', 'Unnamed: 46', 'Unnamed: 47',
  'Unnamed: 48', 'Unnamed: 49', 'Unnamed: 50', 'Unnamed: 51',
  'Unnamed: 52', 'Unnamed: 53', 'Unnamed: 54', 'Unnamed: 55',
  'Unnamed: 56', 'Unnamed: 57', 'Unnamed: 58', 'Unnamed: 59',
  'Unnamed: 60'
  </code></pre>
  <p>They are indexed by 0-indexing so I tried something like</p>
  <pre><code>df.drop(df.columns[[22, 23, 24, 25, 
  26, 27, 28, 29, 30, 31, 32 ,55]], axis=1, inplace=True)
  </code></pre>
  <p>But this isn't very efficient. I tried writing some for loops but this struck me as bad Pandas behaviour. Hence i ask the question here.</p>
  <p>I've seen some examples which are similar (<a href="https://stackoverflow.com/questions/26347412/drop-multiple-columns-pandas">Drop multiple columns in pandas</a>) but this doesn't answer my question.</p>