Full interactive Streamlit data analytics dashboard

• Upload the books.csv file
• View tables and summary statistics
• See price analysis using NumPy
• Visualize ratings, categories, and prices using graphs

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📊 Project: Book Data Explorer with Streamlit

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🎯 Features

✅ Upload books.csv
✅ Show book data in table format
✅ Display price stats (NumPy)
✅ Category & rating analysis
✅ Interactive charts: bar plots, histograms

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📦 Install Requirements

pip install streamlit pandas numpy matplotlib seaborn

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📁 Folder Structure

book_streamlit_dashboard/
├── app.py
└── books.csv  ← (optional default file to test)

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📝 app.py

import streamlit as st
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

st.set_page_config(page_title="📚 Book Data Explorer", layout="wide")
st.title("📚 Book Data Explorer with Streamlit")

# ---- File Upload ----
uploaded_file = st.file_uploader("Upload your books.csv file", type=["csv"])

@st.cache_data
def load_data(file):
    df = pd.read_csv(file)
    # Clean price column (remove £ and convert to float)
    df['price'] = df['price'].str.replace('£', '', regex=False).astype(float)
    return df

if uploaded_file:
    df = load_data(uploaded_file)
    st.success("Data loaded successfully!")

    # ---- Data Preview ----
    st.subheader("🔍 Preview of Book Data")
    st.dataframe(df.head(20), use_container_width=True)

    # ---- NumPy Price Stats ----
    st.subheader("📈 Price Statistics (NumPy)")
    prices = df['price'].values
    col1, col2, col3, col4 = st.columns(4)
    col1.metric("Min Price", f"£{np.min(prices):.2f}")
    col2.metric("Max Price", f"£{np.max(prices):.2f}")
    col3.metric("Mean Price", f"£{np.mean(prices):.2f}")
    col4.metric("Std Dev", f"£{np.std(prices):.2f}")

    # ---- Ratings Distribution ----
    st.subheader("⭐ Rating Distribution")
    rating_counts = df['rating'].value_counts().sort_index()
    st.bar_chart(rating_counts)

    # ---- Price Distribution ----
    st.subheader("📉 Price Distribution")
    fig1, ax1 = plt.subplots()
    sns.histplot(df['price'], kde=True, color='orange', ax=ax1)
    ax1.set_xlabel("Price (£)")
    ax1.set_title("Histogram of Book Prices")
    st.pyplot(fig1)

    # ---- Top N Expensive Books ----
    st.subheader("💰 Top Expensive Books")
    top_n = st.slider("Select number of top books", min_value=5, max_value=30, value=10)
    top_books = df.sort_values(by='price', ascending=False).head(top_n)
    st.dataframe(top_books[['title', 'price', 'rating']], use_container_width=True)

    # ---- Search Books ----
    st.subheader("🔎 Search Books by Title")
    search_term = st.text_input("Enter title keyword:")
    if search_term:
        result = df[df['title'].str.contains(search_term, case=False, na=False)]
        st.write(f"Found {len(result)} book(s):")
        st.dataframe(result, use_container_width=True)

else:
    st.info("Please upload a `books.csv` file to begin.")

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🧪 To Run the App

streamlit run app.py

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🖼️ What Students Will See

• Upload section
• Live data table
• Metric cards for prices
• Interactive bar charts (ratings, categories)
• Histogram of prices
• Search box for books

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💡 Extension Ideas

• Filter by rating/category/price range
• Download filtered results
• Add pie chart of stock availability
• Add author analysis if included in dataset