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import streamlit as st
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
# Load data
def load_data():
df = pd.read_csv("processed_data.csv") # replace with your dataset
return df
# Create Streamlit app
def app():
# Title for the app
st.title("Pizza Sales Data Analysis Dashboard")
df = load_data()
df = pd.DataFrame(df)
# Calculate key metrics
total_orders = df['order_id'].nunique() #Write the appropriate function which can calculate the number of unique values
total_revenue = df['total_price'].sum() #Write a appropriate function which can sum the column
most_popular_size = df['pizza_size'].value_counts().idxmax() #Write a appropriate function which can get the maximum value
most_frequent_category = df['pizza_category'].value_counts().idxmax() #Write a appropriate function which can count of value of each product
total_pizzas_sold = df['quantity'].sum()
# Sidebar with key metrics
st.sidebar.header("Key Metrics")
st.sidebar.metric("Total Orders", total_orders)
st.sidebar.metric("Total Revenue", f"${total_revenue:,.2f}")
st.sidebar.metric("Most Popular Size", most_popular_size)
st.sidebar.metric("Most Popular Category", most_frequent_category)
st.sidebar.metric("Total Pizzas Sold", total_pizzas_sold)
plots = [
{"title": "Top Selling Pizzas (by Quantity)", "x": "pizza_category", "y": "quantity", "top": 5}, #Write the appropriiate column as per the title given
{"title": "Quantity of Pizzas Sold by Category and Time of the Day", "x": "time_of_day", "hue": "pizza_category"}, #Write the appropriiate column as per the title given
{"title": "Quantity of Pizzas Sold by Size and Time of the Day", "x": "time_of_day", "hue": "pizza_size"}, #Write the appropriiate column as per the title given
{"title": "Monthly Revenue Trends by Pizza Category", "x": "order_month", "y": "total_price", "hue": "pizza_category", "estimator": "sum", "marker": "o"}, #Write the appropriiate column as per the title given
]
for plot in plots:
st.header(plot["title"])
fig, ax = plt.subplots()
if "Top Selling Pizzas" in plot["title"]:
data_aux = df.groupby(plot["x"])[plot["y"]].sum().reset_index().sort_values(by=plot["y"], ascending=False).head(plot["top"])
ax.bar(data_aux[plot["x"]].values.tolist(), data_aux[plot["y"]].values.tolist())
if "Quantity of Pizzas" in plot["title"]:
sns.countplot(data=df, x=plot["x"], hue=plot["hue"], ax=ax)
if "Monthly Revenue" in plot["title"]:
sns.lineplot(data=df, x=plot["x"], y=plot["y"], hue=plot["hue"], estimator=plot["estimator"], errorbar=None, marker=plot["marker"], ax=ax)
ax.set_xlabel(" ".join(plot["x"].split("_")).capitalize())
if "y" in plot.keys():
ax.set_ylabel(" ".join(plot["y"].split("_")).capitalize())
else:
ax.set_ylabel("Quantity")
ax.legend(bbox_to_anchor=(1,1))
st.pyplot(fig)
if __name__ == "__main__":
app()
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