title: removing redundant columns when using get_dummies
body: <p>Hi have a pandas dataframe <code>df</code> containing categorical variables.</p>
  
  <pre><code>df=pandas.DataFrame(data=[['male','blue'],['female','brown'],
  ['male','black']],columns=['gender','eyes'])
  
  df
  Out[16]: 
     gender   eyes
  0    male   blue
  1  female  brown
  2    male  black
  </code></pre>
  
  <p>using the function get_dummies I get the following dataframe</p>
  
  <pre><code>df_dummies = pandas.get_dummies(df)
  
  df_dummies
  Out[18]: 
     gender_female  gender_male  eyes_black  eyes_blue  eyes_brown
  0              0            1           0          1           0
  1              1            0           0          0           1
  2              0            1           1          0           0
  </code></pre>
  
  <p>Owever the columns <code>gender_female</code> and <code>gender_male</code> contain the same information because the original column could assume a binary value. Is there a (smart) way to keep only one of the 2 columns?</p>
  
  <p><strong>UPDATED</strong></p>
  
  <p>The use of </p>
  
  <pre><code>df_dummies = pandas.get_dummies(df,drop_first=True)
  </code></pre>
  
  <p>Would give me </p>
  
  <pre><code>df_dummies
  Out[21]: 
     gender_male  eyes_blue  eyes_brown
  0            1          1           0
  1            0          0           1
  2            1          0           0
  </code></pre>
  
  <p>but I would like to remove the columns for which originally I had only 2 possibilities</p>
  
  <p>The desired result should be</p>
  
  <pre><code>df_dummies
  Out[18]: 
     gender_male  eyes_black  eyes_blue  eyes_brown
  0  1           0          1           0
  1  0           0          0           1
  2  1           1          0           0
  </code></pre>