fell very short on this one

pa1.py

@@ -33,7 +33,7 @@ def make_new_downloads(data_set_id, client, limit, offset):
 def create_df(list_of_dicts):
     #takes the full list of crime records and makes a df out of them
     return pd.DataFrame(list_of_dicts)
-    
+'''
 #get the data
 id_17 = 'd62x-nvdr'
 id_18 = '3i3m-jwuy'
@@ -42,19 +42,30 @@ df_17 = create_df(get_full_dataset(id_17))
 df_18 = create_df(get_full_dataset(id_18))
 
 #summary statistics:
+print("Part 1: Statistical Summary")
 print('10 Most Common Chicago Crimes in 2017:\n',
       df_17.primary_type.value_counts().head(10))
 print('10 Most Common Chicago Crimes in 2018:\n',
       df_18.primary_type.value_counts().head(10))
-print('10 Neighborhoods with Highest Volume of Crime in 2017:\n', 
+print('10 Neighborhoods with Highest Volume of Crime in 2017:\n',
       df_17.community_area.value_counts().head(10))
-print('10 Neighborhoods with Highest Volume of Crime in 2018:\n', 
+print('10 Neighborhoods with Highest Volume of Crime in 2018:\n',
       df_18.community_area.value_counts().head(10))
 
 arrest_rates_17 = df_17.arrest.value_counts()
 arrest_rates_18 = df_18.arrest.value_counts()
+aps_17 = arrest_rates_17[1] / arrest_rates_17[0]
+aps_18 = arrest_rates_18[1] / arrest_rates_18[0]
 
-print('Arrests Per Stop in 2017:\n', (arrest_rates_17[1] / arrest_rates_17[0]))
-print('Arrests Per Stop in 2018:\n', (arrest_rates_18[1] / arrest_rates_18[0]))
+print('Arrests Per Stop in 2017:\n', aps_17)
+print('Arrests Per Stop in 2018:\n', aps_18)
+print('Change in Arrest Per Stop Rate from 17-18:\n', (aps_18 - aps_17))
 
+#pull in census tract data from chicago data portal:
+id_tract = '74p9-q2aq'
+df_tract_info = create_df(get_full_datset(id_tract))
+
+#update 17 and 18 dfs with tracts
+df_17.tract = df_17.location.apply()
+'''