Added function to read and parse the CityScope categories and their r…

…elation to NAICS

indicator_tools.py

@@ -6,6 +6,7 @@
 @author: doorleyr
 """
 import math
+import pandas as pd
 
 def shannon_equitability_score(species_counts):
     diversity=0
@@ -18,4 +19,23 @@ def shannon_equitability_score(species_counts):
         equitability=diversity/math.log(len(species_counts))
         return equitability
     else:
-        return None
+        return None
+
+def parse_CityScopeCategories(fpath,CS_column='CS Amenities ',NAICS_column='Unnamed: 5'):
+    '''
+    Useful function to parse the cityscope categories excel located at:
+    fpath = tables/200405_CityScope.categories.xlsx
+    
+    '''
+    CS_cats = pd.read_excel(fpath).iloc[1:]
+    CS_cats = CS_cats[[CS_column,NAICS_column]]
+    CS_cats['shifted'] = CS_cats[CS_column]
+    while any(CS_cats[CS_column].isna()):
+        CS_cats['shifted'] = CS_cats['shifted'].shift(1)
+        CS_cats.loc[CS_cats[CS_column].isna(),CS_column] = CS_cats[CS_cats[CS_column].isna()]['shifted']
+    CS_cats = CS_cats.drop('shifted',1)
+    CS_cats = CS_cats.dropna().drop_duplicates()
+    CS_cats['NAICS'] = CS_cats[NAICS_column].str.strip().str.split(' ').apply(lambda x:x[0])
+    CS_cats['NAICS_name'] = [n.replace(c,'').replace('-','').strip() for n,c in CS_cats[[NAICS_column,'NAICS']].values]
+    CS_cats = CS_cats.drop(NAICS_column,1)
+    return CS_cats