title: AttributeError: module 'pandas' has no attribute 'to_csv'
body: <p>I took some rows from csv file like this </p>
  
  <pre><code>pd.DataFrame(CV_data.take(5), columns=CV_data.columns) 
  </code></pre>
  
  <p>and performed some functions on it. now i want to save it in csv again but it is giving error <code>module 'pandas' has no attribute 'to_csv'</code>
  I am trying to save it like this </p>
  
  <pre><code>pd.to_csv(CV_data, sep='\t', encoding='utf-8') 
  </code></pre>
  
  <p>here is my full code. how can i save my resulting data in csv or excel?</p>
  
  <pre><code>   # Disable warnings, set Matplotlib inline plotting and load Pandas package
  import warnings
  warnings.filterwarnings('ignore')
  
  %matplotlib inline
  import pandas as pd
  pd.options.display.mpl_style = 'default' 
  
  CV_data = sqlContext.read.load('Downloads/data/churn-bigml-80.csv', 
                            format='com.databricks.spark.csv', 
                            header='true', 
                            inferSchema='true')
  
  final_test_data = sqlContext.read.load('Downloads/data/churn-bigml-20.csv', 
                            format='com.databricks.spark.csv', 
                            header='true', 
                            inferSchema='true')
  CV_data.cache()
  CV_data.printSchema() 
  
  pd.DataFrame(CV_data.take(5), columns=CV_data.columns) 
  
  from pyspark.sql.types import DoubleType
  from pyspark.sql.functions import UserDefinedFunction
  
  binary_map = {'Yes':1.0, 'No':0.0, True:1.0, False:0.0} 
  toNum = UserDefinedFunction(lambda k: binary_map[k], DoubleType())
  
  CV_data = CV_data.drop('State').drop('Area code') \
      .drop('Total day charge').drop('Total eve charge') \
      .drop('Total night charge').drop('Total intl charge') \
      .withColumn('Churn', toNum(CV_data['Churn'])) \
      .withColumn('International plan', toNum(CV_data['International plan'])) \
      .withColumn('Voice mail plan', toNum(CV_data['Voice mail plan'])).cache()
  
  final_test_data = final_test_data.drop('State').drop('Area code') \
      .drop('Total day charge').drop('Total eve charge') \
      .drop('Total night charge').drop('Total intl charge') \
      .withColumn('Churn', toNum(final_test_data['Churn'])) \
      .withColumn('International plan', toNum(final_test_data['International plan'])) \
      .withColumn('Voice mail plan', toNum(final_test_data['Voice mail plan'])).cache()
  
  pd.DataFrame(CV_data.take(5), columns=CV_data.columns) 
  
  from pyspark.mllib.regression import LabeledPoint
  from pyspark.mllib.tree import DecisionTree
  
  def labelData(data):
      # label: row[end], features: row[0:end-1]
      return data.map(lambda row: LabeledPoint(row[-1], row[:-1]))
  
  training_data, testing_data = labelData(CV_data).randomSplit([0.8, 0.2])
  
  model = DecisionTree.trainClassifier(training_data, numClasses=2, maxDepth=2,
                                       categoricalFeaturesInfo={1:2, 2:2},
                                       impurity='gini', maxBins=32)
  
  print (model.toDebugString())  
  print ('Feature 12:', CV_data.columns[12])
  print ('Feature 4: ', CV_data.columns[4] ) 
  
  from pyspark.mllib.evaluation import MulticlassMetrics
  
  def getPredictionsLabels(model, test_data):
      predictions = model.predict(test_data.map(lambda r: r.features))
      return predictions.zip(test_data.map(lambda r: r.label))
  
  def printMetrics(predictions_and_labels):
      metrics = MulticlassMetrics(predictions_and_labels)
      print ('Precision of True ', metrics.precision(1))
      print ('Precision of False', metrics.precision(0))
      print ('Recall of True    ', metrics.recall(1))
      print ('Recall of False   ', metrics.recall(0))
      print ('F-1 Score         ', metrics.fMeasure())
      print ('Confusion Matrix\n', metrics.confusionMatrix().toArray()) 
  
  predictions_and_labels = getPredictionsLabels(model, testing_data)
  
  printMetrics(predictions_and_labels)  
  
  CV_data.groupby('Churn').count().toPandas() 
  
  stratified_CV_data = CV_data.sampleBy('Churn', fractions={0: 388./2278, 1: 1.0}).cache()
  
  stratified_CV_data.groupby('Churn').count().toPandas() 
  
  pd.to_csv(CV_data, sep='\t', encoding='utf-8') 
  </code></pre>