updated column and filter names for clarity

streamlit-data-aggviz.py

@@ -1,14 +1,15 @@
-#TO RUN, AFTER SAVING FILE WITH NAME 'streamlit-data-aggviz.py' GO TO TERMINAL OR POWERSHELL AND RUN COMMAND: streamlit run streamlit-data-aggviz.py
+#GO TO POWERSHELL AND RUN COMMAND:  streamlit run streamlit-data-aggviz.py
 
 import streamlit as st
 import os
 import pandas as pd
 
+
 st.set_page_config(layout="wide")
 
-st.title("Data Viewer")
+st.title("Sacramento Campaign Finance 2014 - 2023")
 
-folder_path = 'data/campaign_finance'
+folder_path = 'data/'
 
 # Function to read all JSON files from a folder into a single DataFrame
 def read_jsons_from_folder(folder_path):
@@ -30,27 +31,53 @@ def read_jsons_from_folder(folder_path):
 combined_df['Campaigns-allYears'] = combined_df['filerName'].str[:-5] \
  .where(combined_df['filerName'].str[-4:].str.isnumeric(), combined_df['filerName'])
 
-# Replace 'City Council' values
+# Replace 'City Council' label for 2023 data with general "Sacramento City" label used for all previous years
 combined_df['agencyName'] = combined_df['agencyName'].replace("City Council", "Sacramento City")
 
+# Replace 'Board of Supervisors' label for 2023 data with general "Sacramento County" label used for all previous years
+combined_df['agencyName'] = combined_df['agencyName'].replace("Board of Supervisors", "Sacramento County")
+
 # convert 'year' column to string
 combined_df['year'] = combined_df['year'].astype(str)
 
 original_df = combined_df[['agencyName', 'filerName', 'Campaigns-allYears', 'amount', 'year', 'date', 'contributorLastName', 'contributorFirstName', 'contributorCity', 'contributorZip', 'contributorOccupation', 'contributorEmployer','committeeType','transactionId']]
 
-
-# Initial filter options with 'All'
-filters = {
- 'year': 'All',
- 'agencyName': 'All',
- 'Campaigns-allYears':'All',
- 'filerName': 'All',
- 'contributorLastName': 'All',
- 'contributorCity': 'All',
- 'contributorZip': 'All',
- 'contributorOccupation': 'All',
- 'contributorEmployer': 'All'
-}
+# Create a dictionary to map old column names to new column names
+column_mapping = {'agencyName':'Entity',
+ 'filerName':'Campaign/PAC',
+ 'Campaigns-allYears':'Campaigns/PACs-all years',
+ 'amount':'Contribution',
+ 'year':'Year',
+ 'date':'Date',
+ 'contributorLastName':'Contributor Last Name',
+ 'contributorFirstName':'Contributor First Name',
+ 'contributorCity':'Contributor City',
+ 'contributorZip':'Contributor Zip',
+ 'contributorOccupation':'Contributor Occupation',
+ 'contributorEmployer':'Contributor Employer',
+ 'committeeType':'Committee Type',
+ 'transactionId':'Transaction ID #',
+ }
+
+# Rename the columns using the rename method
+original_df.rename(columns=column_mapping, inplace=True)
+
+
+filters = {'Entity': 'All',
+ 'Campaign/PAC': 'All',
+ 'Campaigns/PACs-all years': 'All',
+ 'Contribution': 'All',
+ 'Year': 'All',
+ 'Date': 'All',
+ "Contributor Last Name": 'All',
+ "Contributor First Name": 'All',
+ 'Contributor City': 'All',
+ 'Contributor Zip': 'All',
+ 'Contributor Occupation': 'All',
+ 'Contributor Employer': 'All',
+ 'Committee Type': 'All',
+ 'Transaction ID #': 'All',
+ }
 
 # Create a function to get the unique values for a column based on active filters
 def get_filtered_values(column, active_filters):
@@ -71,16 +98,29 @@ def get_filtered_values(column, active_filters):
  filtered_df = filtered_df[filtered_df[key] == (int(value) if key == 'year' else value)]
 
 # Display aggregate total of 'amount'
-aggregate_total = filtered_df['amount'].sum()
+aggregate_total = filtered_df['Contribution'].sum()
 st.write(f"Aggregate Total Amount: ${aggregate_total:,.2f}")
 st.write(filtered_df)
 
 # Bar Chart: Amount by user-selected category
-categories = ['agencyName', 'Campaigns-allYears','filerName', 'contributorLastName', 'contributorFirstName', 'contributorCity', 'contributorZip', 'contributorOccupation', 'contributorEmployer']
+categories = ['Entity',
+ 'Campaign/PAC',
+ 'Campaigns/PACs-all years',
+ 'Contribution',
+ 'Year',
+ 'Date',
+ "Contributor Last Name",
+ "Contributor First Name",
+ 'Contributor City',
+ 'Contributor Zip',
+ 'Contributor Occupation',
+ 'Contributor Employer',
+ 'Committee Type',
+ 'Transaction ID #']
 selected_category = st.selectbox("Select a category for the bar chart", categories)
-amount_by_category = filtered_df.groupby(selected_category)['amount'].sum()
+amount_by_category = filtered_df.groupby(selected_category)['Contribution'].sum()
 st.bar_chart(amount_by_category)
 
 # Line Chart: Aggregated amount by year
-amount_by_year = filtered_df.groupby('year')['amount'].sum()
+amount_by_year = filtered_df.groupby('Year')['Contribution'].sum()
 st.line_chart(amount_by_year)