title: np.where on multiple variables
body: <p>I have a data frame with:</p>
  
  <pre><code>customer_id [1,2,3,4,5,6,7,8,9,10]
  feature1 [0,0,1,1,0,0,1,1,0,0]
  feature2 [1,0,1,0,1,0,1,0,1,0]
  feature3 [0,0,1,0,0,0,1,0,0,0]
  </code></pre>
  
  <p>Using this I want to create a new variable (say new_var) to say when feature 1 is 1 then the new_var=1, if feature_2=1 then new_var=2, feature3=1 then new_var=3 else 4. I was trying np.where but though it doesn't give me an error, it doesn't do the right thing - so I guess a nested np.where works on a single variable only. In which case, what's the best way to perform a nested if/case when in pandas?</p>
  
  <p>My np.where code was something like this:</p>
  
  <pre><code>df[new_var]=np.where(df['feature1']==1,'1', np.where(df['feature2']==1,'2', np.where(df[feature3']==1,'3','4')))
  </code></pre>