Add first interactive chart

app.py

@@ -9,65 +9,61 @@
 import streamlit as st
 import altair as alt
 from vega_datasets import data
+import pandas as pd
+import ipywidgets as widgets
  
 st.title('Final Project')
 
-st.text("The URL for this app is: https://huggingface.co/spaces/jnaiman/is445_demo")
-
-source = data.seattle_weather()
-
-scale = alt.Scale(
-    domain=["sun", "fog", "drizzle", "rain", "snow"],
-    range=["#e7ba52", "#a7a7a7", "#aec7e8", "#1f77b4", "#9467bd"],
-)
-color = alt.Color("weather:N", scale=scale)
-
-# We create two selections:
-# - a brush that is active on the top panel
-# - a multi-click that is active on the bottom panel
-brush = alt.selection_interval(encodings=["x"])
-click = alt.selection_point(encodings=["color"])
-
-# Top panel is scatter plot of temperature vs time
-points = (
-    alt.Chart()
-    .mark_point()
-    .encode(
-        alt.X("monthdate(date):T", title="Date"),
-        alt.Y(
-            "temp_max:Q",
-            title="Maximum Daily Temperature (C)",
-            scale=alt.Scale(domain=[-5, 40]),
-        ),
-        color=alt.condition(brush, color, alt.value("lightgray")),
-        size=alt.Size("precipitation:Q", scale=alt.Scale(range=[5, 200])),
-    )
-    .properties(width=550, height=300)
-    .add_params(brush)
-    .transform_filter(click)
-)
-
-# Bottom panel is a bar chart of weather type
-bars = (
-    alt.Chart()
-    .mark_bar()
-    .encode(
-        x="count()",
-        y="weather:N",
-        color=alt.condition(click, color, alt.value("lightgray")),
-    )
-    .transform_filter(brush)
-    .properties(
-        width=550,
-    )
-    .add_params(click)
-)
-
-chart = alt.vconcat(points, bars, data=source, title="Seattle Weather: 2012-2015")
-
-tab1, tab2 = st.tabs(["Streamlit theme (default)", "Altair native theme"])
-
-with tab1:
-    st.altair_chart(chart, theme="streamlit", use_container_width=True)
-with tab2:
-    st.altair_chart(chart, theme=None, use_container_width=True)
+st.text("The URL for this app is: https://huggingface.co/spaces/445final/final2.moved")
+
+df = pd.read_csv('State_Employee_Pay.csv')
+
+df_copy = df.copy
+df['Period Pay Rate'] = df['Period Pay Rate'].str.replace(",", "")
+df['YTD Gross'] = df['YTD Gross'].str.replace(",", "")
+df['Period Pay Rate'] = df['Period Pay Rate'].astype(int)
+df['YTD Gross'] = df['YTD Gross'].astype(int)
+df = df.dropna()
+df = df[df['YTD Gross']!= 0]
+df['Agency'] = df['Agency'].str.lower()
+df['Agency Division'] = df['Agency Division'].str.lower()
+df['Employee Name'] = df['Employee Name'].str.lower()
+df['Position Title'] = df['Position Title'].str.lower()
+
+df_c = df[df['Agency'] == 'corrections']
+df_t = df[df['Agency'] == 'transportation']
+
+df1 = df_c
+
+#agency_division_list = df1['Agency Division'].value_counts().tolist()
+agency_division_list = df1['Agency Division'].unique().tolist()
+
+selected_division = st.selectbox('Agency Division:',
+                                 options=agency_division_list,
+                                 index=0)
+
+# horizontal bar chart
+
+def division_positions(selected_division):
+    df_db = df1[df1['Agency Division'] == selected_division]
+    
+    bar_chart_data = df_db.groupby('Position Title').agg(
+        employeeCount=('Employee Name', 'count'),
+        meanYTDGross=('YTD Gross', 'mean')).reset_index()
+
+    bar_chart_data = bar_chart_data.sort_values('meanYTDGross', ascending=False)
+
+    chart_bar = alt.Chart(bar_chart_data).mark_bar().encode(
+        x=alt.X('employeeCount', title = '# of Employees'),
+        y=alt.Y('Position Title:O'),
+        tooltip=['Position Title', 'employeeCount', alt.Tooltip(
+            'meanYTDGross:Q', format='$,.0f', title='Avg YTD Gross')]
+               ).properties(title=f'Employee Count by Position in: {selected_division} (Sorted by Avg YTD Gross)', 
+                 height=alt.Step(20)).interactive()
+    return chart_bar
+
+
+if selected_division:
+    final_chart = division_positions(selected_division)
+    st.altair_chart(final_chart, use_container_width=True)
+