Update create.py

Author: bniajfi

VitalSigns/create.py

@@ -11,6 +11,15 @@
 #Cell
 #@title Run This Cell: Misc Function Declarations
 
+#Need these imports or the scripts WILL NOT work.
+import geopandas as gpd
+import numpy
+import pandas as pd
+from .acsDownload import retrieve_acs_data
+from dataplay.merge import mergeDatasets
+from dataplay.intaker import Intake
+
+
 # These functions right here are used in the calculations below.
 # Finds a column matchings a substring
 def getColName  (df, col): return df.columns[df.columns.str.contains(pat = col)][0]
@@ -33,21 +42,49 @@ def nullIfEqual(df, c1, c2):
 def sumInts(df): return df.sum(numeric_only=True)
 
 
-#Need these imports or the scripts WILL NOT work.
-import geopandas as gpd
-import numpy
-import pandas as pd
-from .acsDownload import retrieve_acs_data
-from dataplay.merge import mergeDatasets
-from dataplay.intaker import Intake
+#Move Baltimore City row to the bottom, and delete "Unassigned--Jail" row
+def baltCity(df):
+    fi = df
+
+    #Delete "Unassigned--Jail" row -> tract 100300
+    fi = fi[fi.tract != 100300] 
+  
+    #Move Baltimore City row to the bottom of the list.
+    bc = fi.loc['Baltimore City'] #save Baltimore City row
+    fi = fi.drop(fi.index[1]) #Remove baltimore City row from fi based on index location - its index location is 1 for both 2010 and 2020 indicators.
+    fi.loc['Baltimore City'] = bc
+    
+    return fi
+
+#Add 2010 CSA Column
+def add_CSA2010(df):
+  fi = df
+  CSA_Crosswalk = pd.read_csv("https://raw.githubusercontent.com/BNIA/VitalSigns/main/CSA2010_2020.csv")
+
+  fi.reset_index(inplace=True)
+  fi = CSA_Crosswalk.merge(fi, on="CSA2020", how="outer")
+  
+  return fi
+
+def add_CSA2020(df):
+  fi = df
+  CSA_Crosswalk = pd.read_csv("https://raw.githubusercontent.com/BNIA/VitalSigns/main/CSA2010_2020.csv")
+
+  fi.reset_index(inplace=True)
+  fi = CSA_Crosswalk.merge(fi, on="CSA2010", how="outer")
+  
+  return fi
+
+
+
 
 
 #cell
 #@title Run This Cell: Create createIndicator()
 
 def createAcsIndicator(state, county, tract, year, tableId,
                     mergeUrl, merge_left_col, merge_right_col, merge_how, groupBy,
-                    aggMethod, method, columnsToInclude, finalFileName=False):
+                    aggMethod, method, columnsToInclude):
 
   # Pull the data
   df = retrieve_acs_data(state, county, tract, tableId, year)
@@ -78,14 +115,18 @@ def createAcsIndicator(state, county, tract, year, tableId,
   # Create the indicator
   print('Creating Indicator')
   resp = method( df, columnsToInclude)
-  print('Indicator Created')
-  if finalFileName:
-    resp.to_csv(finalFileName, quoting=csv.QUOTE_ALL)
-    print('Indicator Saved')
+  
+  #Add Missing CSA Column
+  data_year = year
+  print("Adding Missing CSA Column")
+  if int(data_year) <= 19:
+    resp = add_CSA2020(resp)
+  else:
+    resp = add_CSA2010(resp)
 
+  print('Indicator Created')
   return resp
 
-
 # Cell
 #@title Run This Cell: Create age5
 
@@ -276,8 +317,7 @@ def racdiv(df, columnsToInclude):
                       groupBy = 'CSA2010',
                       aggMethod= 'sum', 
                       method = hisp,
-                      columnsToInclude = [],
-                      finalFileName=False)
+                      columnsToInclude = [])
   else:
     fi_hisp = createAcsIndicator(state = '24', county = '510', tract = '*' , year = chosen_year, tableId = 'B03002',
                       mergeUrl = 'https://raw.githubusercontent.com/BNIA/VitalSigns/main/CSA2020.csv', 
@@ -287,8 +327,7 @@ def racdiv(df, columnsToInclude):
                       groupBy = 'CSA2020',
                       aggMethod= 'sum', 
                       method = hisp,
-                      columnsToInclude = [],
-                      finalFileName=False)
+                      columnsToInclude = [])
 
   #Column 012E from the Hisp table has a different name on the years prior to 2019. 
   #This  code changes the name of that column automatically for every year prior to 2019.