Create FDIC_banks.py

Author: bniajfi

VitalSigns/FDIC_banks.py

@@ -0,0 +1,55 @@
+# -*- coding: utf-8 -*-
+"""
+This notebook uses the Banks 2021 data to create a portion of BNIA's Vital Signs report
+
+###Indicator from this csv
+*   banks
+
+###Tables used in this notebook
+*   Banks_2021
+*   CSA2010 to CSA2020 Crosswalk
+*   2020 Population
+"""
+
+#export
+#Import needed libraries
+import pandas as pd
+
+#Export
+#Read data
+banks = pd.read_excel("Banks_2021_CSA _City.xlsx")
+crosswalk = pd.read_csv("https://raw.githubusercontent.com/BNIA/VitalSigns/main/CSA2010_2020.csv")
+pop_2020 = pd.read_csv("CSA2020_CSA2010_&_2020pop.csv")
+
+#Remove rows where Baltimore City is empty
+banks = banks[banks['BaltCity'] != '']
+
+"""###banks"""
+
+#Export
+#Banks Indicator
+#Rate of Banks and Bank Branches per 1,000 Residents
+
+#Only keep needed columns
+banks = banks[['CSA']]
+
+#Add counter
+banks['count'] = 1
+
+#Add Baltimore City row
+banks.loc[len(banks.index)] = ['Baltimore City', banks['count'].sum()]
+
+#Group by CSA
+banks = banks.groupby("CSA").sum()
+
+#Append 2010 CSAs
+banks = crosswalk.merge(banks, left_on="CSA2020", right_on="CSA", how="outer")
+
+#Append 2020 Population
+banks = banks.merge(pop_2020, on=["CSA2010", "CSA2020"], how='outer')
+
+#Get banks indicator
+banks['banksXX'] = (banks['count'] / banks['tpop20']) * 1000
+
+#Sort rows alphabetically 
+banks = banks.sort_values('CSA2010')