title: invalid literal for int() with base 10: 'EC180'
body: <p>All that i tried was:</p>
  <pre><code>df['buyer_zip']=df['buyer_zip'].replace('-', 0)
  df['buyer_zip']=df['buyer_zip'].replace('', 0)
  df['buyer_zip']=df['buyer_zip'].str[:5]
  df[&quot;buyer_zip&quot;].fillna( method ='ffill', inplace = True)
  df[&quot;buyer_zip&quot;].apply(int)
  </code></pre>
  <p>I have two columns in a pandas dataframe called Buyer_zip and Item_zip which are the zip codes of Buyer and items respectively. These zip codes have 4 formats. One is 5 digit zip code( ex: 12345), one is 5+4 digit zip code( 12345-1234), one is 9 digit zipcode (123456789) and the last one is 'EC180'. So, the last format is alphanumeric. There are 15 Million records in total. I am struck at a point where i have to convert all those alphanumeric values to numeric. When trying to do the same, i encountered the error: invalid literal for int() with base 10: 'EC180'. Could someone help me how to find all the words in my data column and replace it with 00000. Appreciate any help.But none of it gave an answer to how to find the words in that column and replace it with numbers</p>
  <p>Sample data:</p>
  <pre><code>buyer_zip 
  97219 
  11415-3528 
  EC180
  907031234
  </code></pre>
  <p><strong>Expected output</strong></p>
  <pre><code>     buyer_zip
  0      97219
  1  114153528
  2          0
  3  907031234
  </code></pre>