Take n rows from a spark dataframe and pass to toPandas()

I have this code:

l = [('Alice', 1),('Jim',2),('Sandra',3)]
df = sqlContext.createDataFrame(l, ['name', 'age'])
df.withColumn('age2', df.age + 2).toPandas()

Works fine, does what it needs to. Suppose though I only want to display the first n rows, and then call toPandas() to return a pandas dataframe. How do I do it? I can't call take(n) because that doesn't return a dataframe and thus I can't pass it to toPandas().

So to put it another way, how can I take the top n rows from a dataframe and call toPandas() on the resulting dataframe? Can't think this is difficult but I can't figure it out.

I'm using Spark 1.6.0.

Solution 1:

You can use the limit(n) function:

l = [('Alice', 1),('Jim',2),('Sandra',3)]
df = sqlContext.createDataFrame(l, ['name', 'age'])
df.limit(2).withColumn('age2', df.age + 2).toPandas()

Or:

l = [('Alice', 1),('Jim',2),('Sandra',3)]
df = sqlContext.createDataFrame(l, ['name', 'age'])
df.withColumn('age2', df.age + 2).limit(2).toPandas()

Solution 2:

You could get first rows of Spark DataFrame with head and then create Pandas DataFrame:

l = [('Alice', 1),('Jim',2),('Sandra',3)]
df = sqlContext.createDataFrame(l, ['name', 'age'])

df_pandas = pd.DataFrame(df.head(3), columns=df.columns)

In [4]: df_pandas
Out[4]: 
     name  age
0   Alice    1
1     Jim    2
2  Sandra    3