title: How to fill Non-Null values from some columns in Pandas Dataframe into a new column? How to use np.where() for multiple conditions?
body: <p>I have a question regards about np.where()</p>
  <p>Currently, I have 2 columns, each column contains Null values and categorical values. Values from each column are distinct and will not overlap.</p>
  <p>For now, I want to apply all the Non-Null values from these 2 columns into the new column and fill the NaN value in the new column as a categorical value.</p>
  <p>My idea is using np.where()</p>
  <pre><code>df['C']=np.where(df['A']=='user1', 'user1',(df['B']=='user2','user2','user3'))
  </code></pre>
  <p>Basic idea is if df['A']=='A', fill the value A into new column fist,
  elif df['B']=='B', fill the value B into new column as well,
  Else fill the value 'C' for all the NaN values.</p>
  <p>However, a syntax error returned.</p>
  <pre><code>ValueError: operands could not be broadcast together with shapes (544,) () (3,) 
  </code></pre>
  <p>Thanks for the help always!</p>
  <p>Sample data:</p>
  <pre><code>A   B   C   Desired col C
  user1   Null    Null    user1
  user1   Null    Null    user1
  user1   Null    Null    user1
  user1   Null    Null    user1
  Null    user2   Null    user2
  Null    user2   Null    user2
  Null    user2   Null    user2
  Null    user2   Null    user2
  Null    user2   Null    user2
  Null    user2   Null    user2
  Null    Null    Null    user3
  Null    Null    Null    user3
  Null    Null    Null    user3
  Null    Null    Null    user3
  </code></pre>