title: AnalysisException: u"cannot resolve 'name' given input columns: [ list] in sqlContext in spark
body: <p>I tried a simple example like:</p>
  
  <pre><code>data = sqlContext.read.format("csv").option("header", "true").option("inferSchema", "true").load("/databricks-datasets/samples/population-vs-price/data_geo.csv")
  
  data.cache() # Cache data for faster reuse
  data = data.dropna() # drop rows with missing values
  data = data.select("2014 Population estimate", "2015 median sales price").map(lambda r: LabeledPoint(r[1], [r[0]])).toDF()
  </code></pre>
  
  <p>It works well, But when i try something very similar like:</p>
  
  <pre><code>data = sqlContext.read.format("csv").option("header", "true").option("inferSchema", "true").load('/mnt/%s/OnlineNewsTrainingAndValidation.csv' % MOUNT_NAME)
  
  data.cache() # Cache data for faster reuse
  data = data.dropna() # drop rows with missing values
  data = data.select("timedelta", "shares").map(lambda r: LabeledPoint(r[1], [r[0]])).toDF()
  display(data)
  </code></pre>
  
  <p>It raise error: 
  AnalysisException: u"cannot resolve 'timedelta' given input columns: [ data_channel_is_tech,...</p>
  
  <p>off-course I imported LabeledPoint and LinearRegression </p>
  
  <p>What could be wrong?</p>
  
  <p>Even the simpler case </p>
  
  <pre><code>df_cleaned = df_cleaned.select("shares")
  </code></pre>
  
  <p>raises same AnalysisException (error).</p>
  
  <p>*please note:  df_cleaned.printSchema() works well.</p>