title: How to perfom stemming and drop columns in pandas dataframe in python?
body: <p>Below is the subset of my dataset. I am trying to clean my dataset using <code>Porter stemmer</code> that is available in <code>nltk</code> package. I would like to drop columns that are similar in their stems for example "abandon','abondoned','abondening' should be just abondoned in my dataset. Below is the code I am trying, where I can see words/columns being stemmed. But I am not sure about how to drop those columns? I have already tokeninze and removed punctuation from the corpus.</p>
  
  <p><em>Note:</em> I am new to <code>Python</code> and <code>Textmining</code>.</p>
  
  <p><strong>Dataset Subset</strong></p>
  
  <pre><code>{
     'aaaahhhs':{
        0:0,
        1:0,
        2:0,
        3:0,
        4:0,
        5:0
     },
     'aahs':{
        0:0,
        1:0,
        2:0,
        3:0,
        4:0,
        5:0
     },
     'aamir':{
        0:0,
        1:0,
        2:0,
        3:0,
        4:0,
        5:0
     },
     'aardman':{
        0:0,
        1:0,
        2:0,
        3:0,
        4:0,
        5:0
     },
     'aaron':{
        0:0,
        1:0,
        2:0,
        3:0,
        4:0,
        5:0
     },
     'abandon':{
        0:0,
        1:0,
        2:0,
        3:0,
        4:0,
        5:0
     },
     'abandoned':{
        0:0,
        1:0,
        2:0,
        3:0,
        4:0,
        5:0
     },
     'abandoning':{
        0:0,
        1:0,
        2:0,
        3:0,
        4:0,
        5:0
     },
     'abandonment':{
        0:0,
        1:0,
        2:0,
        3:0,
        4:0,
        5:0
     },
     'abandons':{
        0:0,
        1:0,
        2:0,
        3:0,
        4:0,
        5:0
     }
  }
  </code></pre>
  
  <p><strong>code so far..</strong></p>
  
  <pre><code>from nltk.stem import PorterStemmer 
  from nltk.tokenize import word_tokenize   
  ps = PorterStemmer() 
  for w in clean_df.columns:
      print(ps.stem(w))
  </code></pre>