title: PySpark: How to fillna values in dataframe for specific columns?
body: <p>I have the following sample DataFrame:</p>
  
  <pre><code>a    | b    | c   | 
  
  1    | 2    | 4   |
  0    | null | null| 
  null | 3    | 4   |
  </code></pre>
  
  <p>And I want to replace null values only in the first 2 columns - Column "a" and "b":</p>
  
  <pre><code>a    | b    | c   | 
  
  1    | 2    | 4   |
  0    | 0    | null| 
  0    | 3    | 4   |
  </code></pre>
  
  <p>Here is the code to create sample dataframe: </p>
  
  <pre><code>rdd = sc.parallelize([(1,2,4), (0,None,None), (None,3,4)])
  df2 = sqlContext.createDataFrame(rdd, ["a", "b", "c"])
  </code></pre>
  
  <p>I know how to replace all null values using: </p>
  
  <pre><code>df2 = df2.fillna(0)
  </code></pre>
  
  <p>And when I try this, I lose the third column:</p>
  
  <pre><code>df2 = df2.select(df2.columns[0:1]).fillna(0)
  </code></pre>