Update app.py

app.py

@@ -82,7 +82,8 @@ def load_and_concat_data():
     
     # Drop duplicates and rows with NaT in date_posted
     filtered_df = filtered_df.drop_duplicates().dropna(subset=['date_posted'])
-    
+    #filtering based on data in 2024
+    filtered_df = filtered_df[filtered_df['date_posted'].dt.year=='2024']
     # Convert titles to lowercase
     filtered_df['title'] = filtered_df['title'].str.lower()
     
@@ -117,8 +118,15 @@ 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 create_time_series(df):
-    df_by_date = df.groupby('date_posted').size().reset_index(name='count')
+def create_time_series(df, time_unit='day'):
+    if time_unit == 'month':
+        # Group by month and year
+        df_by_date = df.groupby(df['date_posted'].dt.to_period('M')).size().reset_index(name='count')
+        df_by_date['date_posted'] = df_by_date['date_posted'].dt.to_timestamp()
+    else:
+        # Keep daily grouping as before
+        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)', 
@@ -127,6 +135,14 @@ def create_time_series(df):
         xaxis_title="Date",
         yaxis_title="Number of Job Postings"
     )
+    
+    # Adjust x-axis ticks for monthly view
+    if time_unit == 'month':
+        fig.update_xaxes(
+            dtick="M1",
+            tickformat="%b %Y"
+        )
+    
     return fig
 
 @st.cache_data
@@ -160,7 +176,7 @@ def display_dashboard(df):
         st.plotly_chart(fig, use_container_width=True)
 
     # Job Postings Over Time Chart
-    fig_time_series = create_time_series(df)
+    fig_time_series = create_time_series(df,time_unit='month')
     st.plotly_chart(fig_time_series, use_container_width=True)
 
     col3, col4 = st.columns(2)
@@ -239,16 +255,12 @@ def display_data_explorer(df):
 def display_about_page():
     st.markdown("""
     ## What is this application?
-
     The Job Listings Dashboard is a powerful tool designed to provide insights into the job market. It offers a comprehensive view of job postings, allowing users to explore trends, top companies, locations, and job titles.
-
     ### Key Features:
     - **Interactive Dashboard**: Visualize job market trends with dynamic charts and graphs.
     - **Data Explorer**: Dive deep into individual job listings with advanced filtering options.
     - **Real-time Data**: Fetch the latest job data from our Hugging Face dataset.
-
     ## How to use this application
-
     ### Dashboard
     1. Navigate to the Dashboard using the sidebar.
     2. View overall statistics such as total job postings, unique companies, and today's postings.
@@ -257,7 +269,6 @@ def display_about_page():
        - Job postings over time
        - Top locations for job opportunities
        - Most common job titles
-
     ### Data Explorer
     1. Switch to the Data Explorer using the sidebar.
     2. Choose between viewing all data or applying filters.
@@ -267,13 +278,10 @@ def display_about_page():
        - Job Types
     4. Browse the filtered job listings table.
     5. Click on job or company links to view more details on the original posting site.
-
     ## Data Source
     This application fetches data from my Private dataset which scrapes data from varoious job hosting portal and the data gets updated daily.
-
     ## Contact
     For questions, feedback, or collaboration opportunities, feel free to reach out:
-
     - LinkedIn: [Nihar Palem](https://www.linkedin.com/in/nihar-palem-1b955a183/)
     """)