File size: 1,050 Bytes
e557ce4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | import streamlit as st
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
# Load dataset
df = sns.load_dataset("tips")
df["tip_pct"] = df["tip"] / df["total_bill"] * 100
# --- App layout ---
st.title("🍽️ Tips Data Explorer")
st.write("This app helps you explore tipping behavior.")
# Sidebar controls
st.sidebar.header("Filters")
day = st.sidebar.selectbox("Select a day:", df["day"].unique())
time = st.sidebar.radio("Select time:", df["time"].unique())
# Filter data
filtered = df[(df["day"] == day) & (df["time"] == time)]
# KPI (average tip %)
avg_tip = filtered["tip_pct"].mean()
st.metric(label=f"Average Tip % on {day} ({time})", value=f"{avg_tip:.2f}%")
# Visualization
fig, ax = plt.subplots()
ax.hist(filtered["tip_pct"], bins=10, color="skyblue", edgecolor="black")
ax.set_title(f"Tip % Distribution on {day} ({time})")
ax.set_xlabel("Tip Percentage")
ax.set_ylabel("Count")
st.pyplot(fig)
# Dynamic insight
st.write(f"💡 On **{day} {time}**, the average tip percentage is around **{avg_tip:.2f}%**.")
|