as_pandas() -> as_pandas_dataframe(); add __str__ representation

Change-Id: I357f9544572fd93bd4a50a9b11f05aaa38121964
diff --git a/src/trace_processor/python/example.py b/src/trace_processor/python/example.py
index 5788aa3..b3925a4 100644
--- a/src/trace_processor/python/example.py
+++ b/src/trace_processor/python/example.py
@@ -46,14 +46,14 @@
         addr=args.address, file_path=args.file, bin_path=args.binary)
 
   # Iterate through QueryResultIterator
-  res_it = tp.query('select name from slice')
+  res_it = tp.query('select * from slice limit 10')
   for row in res_it:
     print(row.name)
 
   # Convert QueryResultIterator into a pandas dataframe + iterate. This yields
   # the same results as the function above.
   try:
-    res_df = tp.query('select name from slice').as_pandas()
+    res_df = tp.query('select * from slice limit 10').as_pandas_dataframe()
     for index, row in res_df.iterrows():
       print(row['name'])
   except Exception:
diff --git a/src/trace_processor/python/perfetto/trace_processor/api.py b/src/trace_processor/python/perfetto/trace_processor/api.py
index 6f5eb8f..9cabf07 100644
--- a/src/trace_processor/python/perfetto/trace_processor/api.py
+++ b/src/trace_processor/python/perfetto/trace_processor/api.py
@@ -44,8 +44,13 @@
   # resultant query. Each column name is stored as an attribute of this
   # class, with the value corresponding to the column name and row in
   # the query results table.
-  class Row:
-    pass
+  class Row(object):
+
+    def __str__(self):
+      return str(self.__dict__)
+
+    def __repr__(self):
+      return self.__dict__
 
   class QueryResultIterator:
 
@@ -76,7 +81,7 @@
     # To use the query result as a populated Pandas dataframe, this
     # function must be called directly after calling query inside
     # TraceProcesor.
-    def as_pandas(self):
+    def as_pandas_dataframe(self):
       try:
         import numpy as np
         import pandas as pd
@@ -187,8 +192,8 @@
 
     Returns:
       A class which can iterate through each row of the results table. This
-      can also be converted to a pandas dataframe by calling the as_pandas()
-      function after calling query.
+      can also be converted to a pandas dataframe by calling the
+      as_pandas_dataframe() function after calling query.
     """
     response = self.http.execute_query(sql)
     if response.error: