Update examples to correctly insert and explode new Pandas column (#106)

Author: shifucun

datacommons/examples/core.py

@@ -64,30 +64,24 @@ def main():
 
   # Get the names for the given counties.
   utils._print_header('Get County Names')
-  pd_frame['county_name'] = dc.get_property_values(
-    pd_frame['county'], 'name')
+  pd_frame['county_name'] = pd_frame['county'].map(
+    dc.get_property_values(pd_frame['county'], 'name'))
+  pd_frame = pd_frame.explode('county_name')
   print(pd_frame)
 
   # Get the cities contained in these counties.
   utils._print_header('Get Contained Cities')
-  pd_frame['city'] = dc.get_property_values(
-    pd_frame['county'], 'containedInPlace', out=False, value_type='City')
-  print(pd_frame)
-
-  # To expand on a column with get_property_values, the data frame has to be
-  # flattened first. Clients can use flatten_frame to do this.
-  utils._print_header('Flatten the Frame')
-  pd_frame = pd_frame.explode('county')
+  pd_frame['city'] = pd_frame['county'].map(
+    dc.get_property_values(
+      pd_frame['county'], 'containedInPlace', out=False, value_type='City'))
+  pd_frame = pd_frame.explode('city')
   print(pd_frame)
 
   # Get the names for each city.
   utils._print_header('Get City Names')
-  pd_frame['city_name'] = dc.get_property_values(pd_frame['city'], 'name')
-  print(pd_frame)
-
-  # Format the final frame.
-  utils._print_header('The Final Frame')
-  pd_frame = pd_frame.explode('city')
+  pd_frame['city_name'] = pd_frame['city'].map(
+    dc.get_property_values(pd_frame['city'], 'name'))
+  pd_frame = pd_frame.explode('city_name')
   print(pd_frame)
 
 

datacommons/examples/places.py

@@ -52,7 +52,8 @@ def main():
 
   # Get all CensusTracts in these two counties.
   utils._print_header('Get Census Tracts')
-  pd_frame['tracts'] = dc.get_places_in(pd_frame['county'], 'CensusTract')
+  pd_frame['tracts'] = pd_frame['county'].map(
+    dc.get_places_in(pd_frame['county'], 'CensusTract'))
   pd_frame = pd_frame.explode('tracts')
   print(pd_frame)
 

datacommons/examples/populations.py

@@ -69,13 +69,14 @@ def main():
     constraining_properties={'employment': 'BLS_Employed'}))
 
   # Add the observation for employed individuals
-  pd_frame['employed_count'] = dc.get_observations(
-    pd_frame['employed_pop'],
-    'count',
-    'measuredValue',
-    '2018-12',
-    observation_period='P1M',
-    measurement_method='BLSSeasonallyAdjusted')
+  pd_frame['employed_count'] = pd_frame['employed_pop'].map(
+    dc.get_observations(
+      pd_frame['employed_pop'],
+      'count',
+      'measuredValue',
+      '2018-12',
+      observation_period='P1M',
+      measurement_method='BLSSeasonallyAdjusted'))
   print(pd_frame)
 
   # Final dataframe. Use the convenience function "clean_frame" to convert