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2.py
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import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import dash
from dash import dcc
from dash import html
csv_data = pd.read_csv('zomato.csv')
excel_data = pd.read_excel('cocn.xlsx')
exchangerate = pd.read_csv('exchange rate.csv')
name=[]
for index,i in enumerate(csv_data["Country Code"]):
for index1, j in enumerate(excel_data["Country Code"]):
if(i==j):
name.append(excel_data["Country"][index1])
unique_array = list(set(name))
csv_data.insert(3,"country name",name)
unique_array1 = list(set(csv_data["Currency"]))
indrsval=[]
for ind ,s in enumerate(csv_data["Currency"]):
for ind1 ,h in enumerate(exchangerate["currency"]):
# print(s.split("(")[0])
# print(h)
if(s.split("(")[0]==h):
csv_data.at[ind,"inr"]=exchangerate["inr"][ind1]
# print(csv_data["Average Cost for two"][ind])
# print(exchangerate["inr"][ind1])
# print(csv_data["Average Cost for two"][ind]*exchangerate["inr"][ind1])
# indrsval.append(csv_data["Average Cost for two"][ind]*exchangerate["inr"][ind1])
# csv_data.insert(12,"amount in INR",indrsval)
csv_data.head()
#csv_data.to_csv("zomato_with_currency.csv", index=False)
for ins , i in enumerate(csv_data["Average Cost for two"]):
# print(i)
# print(csv_data["inr"][ins])
indrsval.append(i*csv_data["inr"][ins])
#print(indrsval)
csv_data.insert(13,"in rs",indrsval)
csv_data.to_csv("zomato_with_currency.csv", index=False)
print("done")
# fig = go.Figure(data=[go.Bar(x=csv_data['country name'], y=csv_data['inr'])])
# # Set the axis labels and title
# fig.update_layout(xaxis_title='Country', yaxis_title='Exchange Rate (relative to INR)', title='Exchange Rates Relative to the Indian Rupee')
# # Display the chart
# fig.show()
fig = go.Figure()
fig.add_trace(go.Scatter(x=csv_data['country name'], y=csv_data['inr'], mode='lines', name='line'))
# Set the axis labels and title
fig.update_layout(xaxis_title='Country', yaxis_title='Exchange Rate', title='Currency Comparison between India and Other Countries')
# Display the chart
#fig.show()
app = dash.Dash(__name__)
# Define the layout
app.layout = html.Div([
html.H1('Indian Restaurant Dashboard'),
html.Br(),
dcc.Graph(figure=fig),
dcc.Dropdown(
id='city-dd',
options=[{'label': c, 'value': c} for c in csv_data['City'].unique()],
value='New Delhi'
),
html.H3(id="fc"),
html.H3(id="cc"),
html.H3(id="rc"),
dcc.Graph(id='chart4'),
dcc.Dropdown(
id='country-dropdown',
options=[{'label': c, 'value': c} for c in csv_data['country name'].unique()],
value='India'
),
dcc.Graph(id='chart1'),
dcc.Graph(id='chart2'),
dcc.Graph(id='chart3'),
])
# Define the callbacks
@app.callback(
[dash.dependencies.Output('chart1', 'figure'),
dash.dependencies.Output('chart2', 'figure'),
dash.dependencies.Output('chart3', 'figure')
],
[dash.dependencies.Input('country-dropdown', 'value')],
#[dash.dependencies.Input('city-dd', 'value')]
)
def update_charts(country):
filtered_data = csv_data[csv_data['country name'] == country]
# print(filtered_data)
# Chart 1: Most expensive cuisines
expensive_cuisines = filtered_data.groupby('Cuisines')['in rs'].mean().sort_values(ascending=False)[:10]
fig1 = px.bar(expensive_cuisines, x=expensive_cuisines.index, y='in rs', title='Most Expensive Cuisines')
# Chart 2: Online delivery vs dine-in
delivery_type_counts = filtered_data['Has Online delivery'].value_counts()
fig2 = px.pie(delivery_type_counts, values=delivery_type_counts.values, names=delivery_type_counts.index, title='Online Delivery vs Dine-in')
# Create a bar chart that shows the comparison between the cities
counts = filtered_data.groupby("City")["Restaurant ID"].count()
# print(counts)
fig3 = px.bar(x=counts.index, y=counts.values, labels={"x": "City", "y": "Number of Restaurants"},title='City and Resturant Comparision')
return fig1,fig2,fig3
@app.callback(
[dash.dependencies.Output('fc', 'children'),dash.dependencies.Output('cc', 'children'),dash.dependencies.Output('rc', 'children'),dash.dependencies.Output('chart4', 'figure')],
[dash.dependencies.Input('city-dd', 'value')]
)
def update_charts1(city):
delhi_restaurants = csv_data[csv_data["City"] == city]
# Extract the cuisines for each restaurant in Delhi
delhi_cuisines = delhi_restaurants["Cuisines"].str.split(", ")
# Count the occurrence of each cuisine in Delhi
cuisine_counts = {}
for cuisines in delhi_cuisines:
for cuisine in cuisines:
cuisine = cuisine.strip()
if cuisine not in cuisine_counts:
cuisine_counts[cuisine] = 0
cuisine_counts[cuisine] += 1
# Sort the cuisines by the occurrence count in descending order
popular_cuisines = sorted(cuisine_counts.items(), key=lambda x: x[1], reverse=True)
cost_per_cuisine= delhi_restaurants.groupby("Cuisines")["in rs"].mean().reset_index()
# print(cost_per_cuisine)
costliest_cuisine=cost_per_cuisine.loc[cost_per_cuisine["in rs"].idxmax(),"Cuisines"]
rating_test= delhi_restaurants["Aggregate rating"].value_counts().reset_index()
rating_test.columns=['rating test','count']
df = rating_test.drop(rating_test[rating_test['rating test'] == 0.0].index)
rating=df.iloc[0]['rating test']
count=df.iloc[0]['count']
# print(df.iloc[0]['count'])
delivery_type_counts = delhi_restaurants['Has Online delivery'].value_counts()
fig3 = px.pie(delivery_type_counts, values=delivery_type_counts.values, names=delivery_type_counts.index, title='Online Delivery vs Dine-in of {}'.format(city))
# print(costliest_cuisine)
# Print the most popular cuisine in Delhi
return "The famous Cuisines in {} is {}".format(city,popular_cuisines[0][0]), "The costliest cuisine in {} is {}".format(city,costliest_cuisine), "The rating test of city {} is {} and count is {}".format(city,rating,count),fig3
if __name__ == '__main__':
app.run_server(debug=True)