added parties for counties/cities pre-2000.

app.py

@@ -38,30 +38,6 @@ llm = OpenAI(api_token=st.secrets["OPENAI_API_KEY"])
 df1 = pd.read_csv("data.csv")
 agent = Agent([df1], config={"verbose": True, "response_parser": StreamlitResponse, "llm": llm})
 
-with st.sidebar:
-
-
-    '''
-    ## Data Assistant (experimental)
-
-    Ask questions about the landvote data, like:
-
-    - What are the top states for approved conservation funds?
-    - Plot the total funds spent in conservation each year.
-    - What city has approved the most funds in a single measure? What was the description of that vote?
-    - Which state has had largest number measures fail? What is that as a fraction of it's total measures?
-    '''
-    
-    prompt = st.chat_input("Ask about the data")
-    if prompt:
-        with st.spinner():
-            resp = agent.chat(prompt)
-            if os.path.isfile('exports/charts/temp_chart.png'):
-                im = plt.imread('exports/charts/temp_chart.png')
-                st.image(im)
-                os.remove('exports/charts/temp_chart.png')
-            st.write(resp)
-
 year = st.slider("Select a year", 1988, 2024, 2022, 1)
 
  
@@ -96,7 +72,7 @@ def create_chart(df, y_column, ylab, title, color):
 # percentage of measures passing, per party
 def get_passes(votes):
     return (votes
-        .filter(_.year >= 2000)
+        # .filter(_.year >= 2000)
         .group_by("year", "party")
         .aggregate(total=_.count(), passes=_.Status.isin(["Pass", "Pass*"]).sum())
         .mutate(percent_passed=(_.passes / _.total).round(2),
@@ -107,7 +83,7 @@ def get_passes(votes):
 # cumulative funding over time 
 def funding_chart(votes):
    return (votes
-           .filter(_.year >= 2000)
+           # .filter(_.year >= 2000)
           .mutate(amount=_.amount.replace('$', '')
                   .replace(',', '')
                   .cast('float64'))
@@ -243,7 +219,7 @@ sv_style =  {
         ]
 }
 
-party_pmtiles = "https://huggingface.co/datasets/boettiger-lab/landvote/resolve/main/party_polygons.pmtiles"
+party_pmtiles = "https://huggingface.co/datasets/boettiger-lab/landvote/resolve/main/party_polygons_all.pmtiles"
 
 recent_election_year = year - year%4 
 party_style =  {
@@ -271,12 +247,38 @@ party_style =  {
     }
 
 
+with st.sidebar:
+    color_choice = st.radio("Color by:", ["Measure Status", "Political Party"])
+    st.divider()
+
+    social_toggle = st.toggle("Social Vulnerability Index")
+    party_toggle = st.toggle("Political Parties")
+    st.divider()
+
+    '''
+    ## Data Assistant (experimental)
+
+    Ask questions about the landvote data, like:
+
+    - What are the top states for approved conservation funds?
+    - Plot the total funds spent in conservation each year.
+    - What city has approved the most funds in a single measure? What was the description of that vote?
+    - Which state has had largest number measures fail? What is that as a fraction of it's total measures?
+    '''
+    
+    prompt = st.chat_input("Ask about the data")
+    if prompt:
+        with st.spinner():
+            resp = agent.chat(prompt)
+            if os.path.isfile('exports/charts/temp_chart.png'):
+                im = plt.imread('exports/charts/temp_chart.png')
+                st.image(im)
+                os.remove('exports/charts/temp_chart.png')
+            st.write(resp)
+
               
 m = leafmap.Map(style="positron", center=(-100, 40), zoom=3)
 
-color_choice = st.radio("Color by:", ["Measure Status", "Political Party"])
-social_toggle = st.toggle("Social Vulnerability Index")
-party_toggle = st.toggle("Political Parties")
 
 if social_toggle:
     m.add_pmtiles(sv_pmtiles, style = sv_style ,visible=True, opacity=0.3, tooltip=True)

preprocess.ipynb

@@ -24,7 +24,8 @@
     "conn = ibis.duckdb.connect(extensions=[\"spatial\"])\n",
     "\n",
     "landvote_url = \"https://huggingface.co/datasets/boettiger-lab/landvote/resolve/main/landvote_polygons.parquet\"\n",
-    "party_url = \"https://huggingface.co/datasets/boettiger-lab/landvote/resolve/main/party_polygons.parquet\"\n",
+    "# party_url = \"https://huggingface.co/datasets/boettiger-lab/landvote/resolve/main/party_polygons.parquet\"\n",
+    "party_url = \"https://huggingface.co/datasets/boettiger-lab/landvote/resolve/main/party_polygons_all.parquet\"\n",
     "\n"
    ]
   },
@@ -58,7 +59,7 @@
     "\n",
     "party = (conn\n",
     "        .read_parquet(party_url)\n",
-    "        .cast({\"geometry\": \"geometry\"})\n",
+    "        .cast({\"geometry\": \"geometry\",\"municipal\":\"string\"})\n",
     "        .mutate(municipal=ibis.case()\n",
     "                .when(_.jurisdiction.isin(['State','County']), ibis.literal(\"-\")) \n",
     "                .else_(_.municipal) \n",
@@ -201,7 +202,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,