Update app.py

app.py

@@ -87,34 +87,9 @@ def create_chart(data, _x, y, title, color_sequence):
     fig.update_layout(plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font_color='#FFFFFF')
     return fig
 
-def expand_time_series(df, date_col, start_date, end_date):
-    full_range = pd.date_range(start=start_date, end=end_date, freq='W')
-    df.set_index(date_col, inplace=True)
-    expanded_df = df.reindex(full_range).reset_index()  
-    expanded_df.columns = ['Week', 'Job Postings']
-    expanded_df['Job Postings'] = expanded_df['Job Postings'].fillna(0)
-    return expanded_df
-
-def create_weekly_dashboard(df):
-    df['Week'] = df['date_posted'].dt.to_period('W').apply(lambda r: r.start_time)
-    weekly_data = df.groupby('Week').size().reset_index(name='Job Postings')
-    
-    start_date = weekly_data['Week'].min()
-    end_date = weekly_data['Week'].max()
-    
-    expanded_weekly_data = expand_time_series(weekly_data, 'Week', start_date, end_date)
-    return expanded_weekly_data
-
-@st.cache_data
-def get_location_data(df):
-    location_counts = df['location'].value_counts().reset_index()
-    location_counts.columns = ['location', 'count']
-    return location_counts
-
-def create_heatmap(location_data):
-    fig = px.density_mapbox(location_data, lat='latitude', lon='longitude', z='count', radius=10,
-                            center=dict(lat=37.0902, lon=-95.7129), zoom=3,
-                            mapbox_style="carto-darkmatter", title="Job Postings Heatmap")
+def create_time_series(df):
+    df_by_date = df.groupby('date_posted').size().reset_index(name='count')
+    fig = px.line(df_by_date, x='date_posted', y='count', title="Job Postings Over Time", color_discrete_sequence=['#4e79a7'])
     fig.update_layout(plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font_color='#FFFFFF')
     return fig
 
@@ -136,11 +111,9 @@ def display_dashboard(df):
         fig = create_chart(top_companies, top_companies.index, top_companies.values, "Top 10 Companies", ['#4e79a7'])
         st.plotly_chart(fig, use_container_width=True)
 
-    # Weekly Job Postings Chart
-    weekly_data = create_weekly_dashboard(df)
-    fig_weekly = px.line(weekly_data, x='Week', y='Job Postings', title="Weekly Job Postings", color_discrete_sequence=['#4e79a7'])
-    fig_weekly.update_layout(plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font_color='#FFFFFF')
-    st.plotly_chart(fig_weekly, use_container_width=True)
+    # Job Postings Over Time Chart
+    fig_time_series = create_time_series(df)
+    st.plotly_chart(fig_time_series, use_container_width=True)
 
     col3, col4 = st.columns(2)
 
@@ -155,12 +128,6 @@ def display_dashboard(df):
         fig.update_layout(plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font_color='#FFFFFF')
         st.plotly_chart(fig, use_container_width=True)
 
-    # Job Postings Heatmap
-    st.subheader("Job Postings Heatmap")
-    location_data = get_location_data(df)
-    fig_heatmap = create_heatmap(location_data)
-    st.plotly_chart(fig_heatmap, use_container_width=True)
-
 @st.cache_data
 def filter_dataframe(df, companies, locations, job_types):
     filtered_df = df
@@ -216,7 +183,7 @@ def main():
 
     if page == "Dashboard":
         display_dashboard(df)
-    elif page == "Data Explorer":
+    elif page "Data Explorer":
         display_data_explorer(df)
 
 if __name__ == "__main__":