Duplicate from Chitranshu/Dashboard-Zomato

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+RUN python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["panel", "serve", "/code/app.py", "--address", "0.0.0.0", "--port", "7860",  "--allow-websocket-origin", "*"]
+
+RUN mkdir /.cache
+RUN chmod 777 /.cache
+RUN mkdir .chroma
+RUN chmod 777 .chroma

README.md

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+---
+title: Zomato-Dashboard
+emoji: 📊
+colorFrom: red
+colorTo: red
+sdk: docker
+pinned: false
+duplicated_from: Chitranshu/Dashboard-Zomato
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import pandas as pd
+import pandas as pd
+import panel as pn
+import hvplot.pandas
+from itertools import cycle
+from bokeh.palettes import Reds9
+import folium
+raw_df = pd.read_csv('zomato_data.csv')
+zomato_df = raw_df.copy()
+rating_type_df = zomato_df['RATING_TYPE'].value_counts().reset_index()
+rating_type_df.rename(columns={'index':'RATING TYPE', 'RATING_TYPE':'COUNT OF RESTAURANTS'}, inplace=True)
+foodtruck_df = zomato_df[zomato_df['CUSINE TYPE'] == 'Food Truck']
+foodtruck_df.sort_values(by='RATING',ascending=False)
+
+
+# Read the CSV file into a DataFrame
+zomato_df = pd.read_csv('zomato_data.csv')
+
+# Count the occurrences of each cuisine type
+cuisine_counts = zomato_df['CUSINE TYPE'].value_counts()
+
+# Create the bar plot using hvplot
+bar_plot_cuisine = cuisine_counts.hvplot.bar(
+    color='#E10F14',
+    title='No. of Restaurants by Cuisine Type',
+    xlabel='Cuisine Type',
+    ylabel='Count',
+    width=900,
+    height=500
+).opts(xrotation=90)
+
+# Wrap the bar plot in a Panel object
+panel_cuisine = pn.panel(bar_plot_cuisine)
+
+# Create a DataFrame with the given data
+rating_type_df = pd.DataFrame({
+    'RATING TYPE': ['Average', 'Good', 'Very Good', 'Excellent', 'Poor', 'Very Poor'],
+    'COUNT OF RESTAURANTS': [4983, 4263, 1145, 96, 56, 4]
+})
+
+# Define the hvplot chart
+bar_plot_rating = rating_type_df.hvplot.bar(
+    x='RATING TYPE',
+    y='COUNT OF RESTAURANTS',
+    color='#E10F14',
+    title='Count of Restaurants by Rating Type',
+    xlabel='Rating Type',
+    ylabel='Count',
+    width=900,
+    height=500
+)
+
+# Wrap the bar plot in a Panel object
+panel_rating = pn.panel(bar_plot_rating)
+
+# Filter food trucks in Mumbai
+foodtruck_df = zomato_df[zomato_df['CUSINE TYPE'] == 'Food Truck']
+
+# Sort by rating in descending order and select the top result
+best_food_truck = foodtruck_df.sort_values(by='RATING', ascending=False).head()
+
+# Create the bar plot using hvplot
+bar_plot_best_food_truck = best_food_truck.hvplot.bar(
+    x='NAME',
+    y='PRICE',
+    color='#E10F14',
+    title='Best Food Truck in Mumbai: Price vs. Name',
+    xlabel='Food Truck Name',
+    ylabel='Price',
+    hover_cols=['RATING', 'REGION', 'CUSINE_CATEGORY'],
+    rot=90,
+    width=900,
+    height=500
+)
+
+# Wrap the bar plot in a Panel object
+panel_best_food_truck = pn.panel(bar_plot_best_food_truck)
+
+# Filter seafood restaurants in Mumbai
+seafood_df = zomato_df[zomato_df['CUSINE_CATEGORY'].notna() & zomato_df['CUSINE_CATEGORY'].str.contains('Seafood')]
+
+# Get top 10 seafood restaurants in Mumbai, sorted by rating
+top_seafood_df = seafood_df.sort_values(by='RATING', ascending=False).head(10)
+
+# Create the bar plot using hvplot
+bar_plot_top_seafood = top_seafood_df.hvplot.bar(
+    x='NAME',
+    y='PRICE',
+    color='#E10F14',
+    title='Top 10 Seafood Restaurants in Mumbai: Price vs. Name',
+    xlabel='Restaurant Name',
+    ylabel='Price',
+    hover_cols=['RATING', 'REGION', 'CUSINE_CATEGORY'],
+    rot=90,
+    width=900,
+    height=500
+)
+
+# Wrap the bar plot in a Panel object
+panel_top_seafood = pn.panel(bar_plot_top_seafood)
+
+# Define Panel widgets
+yaxis_radio = pn.widgets.RadioButtonGroup(
+    name='Y axis',
+    options=['Cuisine Type', 'Rating Type', 'Best Food Truck', 'Top 10 Seafood', 'Highest Rated', 'Top Avg Price', 'Chinese Resto', 'Price vs Rating', 'Region vs Price', 'Map'],
+    button_type='danger',
+    inline=True,
+    value='Cuisine Type'
+)
+
+# Define the Panel layout
+panel_layout = pn.Column(
+    pn.Row(yaxis_radio)
+)
+
+# Create the map centered at Mumbai with dark mode
+mumbai_map = folium.Map(location=[19.0760, 72.8777], zoom_start=12, tiles="StamenTonerBackground")
+
+# Add a marker for Mumbai
+folium.Marker(
+    location=[19.0760, 72.8777],
+    popup='<b>Mumbai</b>',
+    icon=folium.Icon(color='red', icon_color='white', icon='heart', prefix='fa')
+).add_to(mumbai_map)
+
+# Add markers for the specified locations
+locations = [
+    {'name': 'Hitchki', 'region': 'Bandra', 'rating': '4.8', 'latitude': 19.0590, 'longitude': 72.8292, 'cuisine': 'Indian'},
+    {'name': 'Downtown China', 'region': 'Andheri', 'rating': '4.9', 'latitude': 19.1136, 'longitude': 72.8697, 'cuisine': 'Chinese'},
+    {'name': 'The Northern Vibe', 'region': 'Powai', 'rating': '4.7', 'latitude': 19.1187, 'longitude': 72.9073, 'cuisine': 'Continental'},
+    {'name': 'Rajdhani', 'region': 'Ghatkopar', 'rating': '4.8', 'latitude': 19.0866, 'longitude': 72.9081, 'cuisine': 'Indian'},
+    {'name': 'Trumpet Sky Lounge', 'region': 'Andheri', 'rating': '4.9', 'latitude': 19.1189, 'longitude': 72.8537, 'cuisine': 'International'},
+    {'name': 'Dessertino', 'region': 'Kandivali', 'rating': '4.7', 'latitude': 19.2128, 'longitude': 72.8376, 'cuisine': 'Desserts'}
+]
+
+for location in locations:
+    popup_content = f"<b>Name:</b> {location['name']}<br><b>Region:</b> {location['region']}<br><b>Rating:</b> {location['rating']}<br><b>Cuisine:</b> {location['cuisine']}"
+    if location['name'] == 'Dessertino':
+        icon = folium.Icon(color='red', icon_color='white', icon='coffee', prefix='fa')
+    else:
+        icon = folium.Icon(color='red', icon_color='white', icon='cutlery', prefix='fa')
+    folium.Marker(
+        location=[location['latitude'], location['longitude']],
+        popup=popup_content,
+        icon=icon
+    ).add_to(mumbai_map)
+    
+title_html = """
+<div style="font-size: 17px; font-weight: bold; text-align: left;">The best Restaurant to order food with best price and Quality</div>
+"""
+# Wrap the map in a Panel object
+panel_map = pn.pane.HTML(title_html + mumbai_map._repr_html_(), width=800, height=600)
+
+# Define the callback function for the radio button
+def update_chart(event):
+    if event.new == 'Cuisine Type':
+        panel_layout[1:] = [panel_cuisine]
+    elif event.new == 'Rating Type':
+        panel_layout[1:]= [panel_rating]
+    elif event.new == 'Best Food Truck':
+        panel_layout[1:] = [panel_best_food_truck]
+    elif event.new == 'Top 10 Seafood':
+        panel_layout[1:] = [panel_top_seafood]
+    elif event.new == 'Highest Rated':
+        # Filter the DataFrame for highest rated restaurants
+        highest_rated = zomato_df[zomato_df['RATING'] >= 4.7]
+
+        # Create the bar plot using hvplot
+        bar_plot_highest_rated = highest_rated.hvplot.bar(
+            x='NAME',
+            y='PRICE',
+            color='#E10F14',
+            title='Highest Rated Restaurants in Mumbai: Price vs. Name',
+            xlabel='Restaurant Name',
+            ylabel='Price',
+            hover_cols=['RATING', 'REGION', 'CUSINE_CATEGORY'],
+            rot=90,
+            width=900,
+            height=500
+        )
+
+        # Wrap the bar plot in a Panel object
+        panel_highest_rated = pn.panel(bar_plot_highest_rated)
+        panel_layout[1:] = [panel_highest_rated]
+    elif event.new == 'Top Avg Price':
+        # Filter the DataFrame for ratings greater than or equal to 4.5
+        filtered_df = zomato_df[zomato_df['RATING'] >= 4.5]
+
+        # Calculate the mean price for each combination of 'REGION' and 'CUSINE TYPE'
+        highest_rated_price_df = filtered_df.groupby(['REGION', 'CUSINE TYPE'])['PRICE'].mean().reset_index()
+
+        # Sort the DataFrame by 'REGION' in alphabetical order
+        highest_rated_price_df = highest_rated_price_df.sort_values('REGION')
+
+        # Create a scatter plot with rotated labels and star marker
+        scatter_plot_top_avg_price = highest_rated_price_df.hvplot.scatter(
+            x='REGION',
+            y='PRICE',
+            c='CUSINE TYPE',
+            cmap='Category10',
+            title='Avg Price Distribution of High-rated restaurants for each Cuisine Type',
+            size=100,  # Increase the marker size
+            rot=90,
+            width=900,
+            height=500,
+            marker='*',
+        )
+
+        # Create a Panel object with the scatter plot
+        panel_top_avg_price = pn.panel(scatter_plot_top_avg_price)
+        panel_layout[1:] = [panel_top_avg_price]
+    elif event.new == 'Chinese Resto':
+        zomato_df_cleaned = zomato_df.dropna(subset=['CUSINE_CATEGORY'])
+        chinese_df = zomato_df_cleaned[zomato_df_cleaned['CUSINE_CATEGORY'].str.contains('Chinese')]
+        chinese_rest_df = chinese_df.groupby(by='REGION').agg({'NAME': 'count', 'PRICE': 'mean'}).rename(columns={'NAME': 'COUNT OF RESTAURANTS'}).reset_index()
+        chinese_rest_df = chinese_rest_df.sort_values('COUNT OF RESTAURANTS', ascending=False).head(25)
+        bar_plot = chinese_rest_df.hvplot.bar(
+            x='REGION',
+            y='COUNT OF RESTAURANTS',
+            color='#E10F14',  # Set the color to red
+            title='No. of Chinese Restaurants by Places',
+            xlabel='Region',
+            ylabel='Count of Restaurants',
+            rot=90,
+            height=500,
+            width=900
+        )
+        layout = pn.Column(bar_plot)
+        panel_layout[1:] = [bar_plot]
+    elif event.new == 'Price vs Rating':
+        # Calculate the mean price and rating for each cuisine type
+        price_rating_df = zomato_df.groupby(['CUSINE TYPE', 'RATING'])['PRICE'].mean().reset_index()
+        hvplot_price_rating = price_rating_df.hvplot.line(
+            x='RATING',
+            y='PRICE',
+            by='CUSINE TYPE',
+            title='Price vs Rating by Cuisine Type',
+            xlabel='Rating',
+            ylabel='Price',
+            width=900,
+            height=500,
+            legend='bottom'  # Set the position of the legend to 'bottom'
+        )
+
+        # Set the number of legend columns
+        hvplot_price_rating.opts(legend_cols=6)  # Adjust the value to your desired maximum number of legend items per row
+
+        # Wrap the Hvplot plot in a Panel object
+        panel_price_vs_rating = pn.panel(hvplot_price_rating)
+        panel_layout[1:] = [panel_price_vs_rating]
+    elif event.new == 'Region vs Price':
+        region_price_df = zomato_df.groupby(['REGION'])['PRICE'].mean().reset_index()
+        scatter_plot = region_price_df.hvplot.scatter(
+            x='REGION',
+            y='PRICE',
+            cmap='Category10',
+            title='Relation between Region and Price',
+            size=100,  # Increase the marker size
+            rot=90,
+            width=900,
+            height=600,
+            marker='*',
+            color='red'
+        )
+        panel_region_vs_price = pn.Column(scatter_plot)
+        panel_layout[1:] = [panel_region_vs_price]
+    elif event.new == 'Map':
+        panel_layout[1:] = [panel_map]
+
+yaxis_radio.param.watch(update_chart, 'value')
+
+# Display the initial chart
+panel_layout.append(panel_cuisine)
+
+# Display the Panel layout
+panel_layout
+dashboard = panel_layout
+import panel as pn
+pn.extension()  # Add this line to load the Panel extension
+
+# Layout using Template
+template = pn.template.FastListTemplate(
+    title='Zomato Mumbai Dashboard', 
+    sidebar=[
+        pn.pane.PNG('zomato.png', sizing_mode='scale_both'),
+        pn.pane.Markdown("# Performing Exploratory Data Analysis"),  
+        pn.pane.Markdown("1. How many restaurants are in Mumbai for each type of cuisine?"),
+        pn.pane.Markdown("2. What are the percentage of restaurants by Rating Type in Mumbai?"),
+        pn.pane.Markdown("3. Which are the Top 10 highest rated Seafood Restaurant in Mumbai?"),
+        pn.pane.Markdown("4. Which is the best Food Truck in Mumbai?"),
+        pn.pane.Markdown("5. Which places have the highest rated restaurant for each Cuisine Type in Mumbai?"),
+        pn.pane.Markdown("6. What is the Avg Price Distibution of highest rated restaurant for each Cuisine Type in Mumbai?"),
+        pn.pane.Markdown("7. Which areas have a large number of Chinese Restaurant Market?"),
+        pn.pane.Markdown("8. Is there a relation between Price and Rating by each Cuisine Type?"),
+        pn.pane.Markdown("9. Is there a relation between Region and Price?"),
+        pn.pane.Markdown("10. Can we map the best restraunt with high quality food?"),
+    ],
+    main = [pn.Row(pn.Column(dashboard)),
+            pn.Row(pn.pane.Markdown("Designed and Developed with ❤️ by Chitranshu Nagdawane © 2023"))
+           ],
+    accent_base_color="#E10F14",
+    header_background="#E10F14"
+)
+
+template.servable()

requirements.txt

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+panel
+jupyter
+numpy
+pandas
+hvplot
+bokeh
+seaborn
+folium