Visualize Scraped Data: Tạo Charts Từ Dữ Liệu Thu Thập

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February 4, 2026

Visualize Scraped Data: Tạo Charts Từ Dữ Liệu Thu Thập

Data không có nghĩa gì nếu không visualize. Bài viết hướng dẫn tạo charts từ scraped data.

Tools Phổ Biến

  • Matplotlib: Basic plotting
  • Seaborn: Statistical visualization
  • Plotly: Interactive charts
  • Pandas: Quick plots

1. Matplotlib Basics

import matplotlib.pyplot as plt
import pandas as pd

# Load scraped data
df = pd.read_csv('products.csv')

# Bar chart - Top 10 products by price
top_10 = df.nlargest(10, 'price')
plt.figure(figsize=(12, 6))
plt.barh(top_10['name'], top_10['price'])
plt.xlabel('Price (VND)')
plt.title('Top 10 Most Expensive Products')
plt.tight_layout()
plt.savefig('top_products.png')
plt.show()

2. Price Distribution

import matplotlib.pyplot as plt

# Histogram
plt.figure(figsize=(10, 6))
plt.hist(df['price'], bins=50, edgecolor='black')
plt.xlabel('Price')
plt.ylabel('Count')
plt.title('Price Distribution')
plt.savefig('price_distribution.png')

3. Time Series

# Price tracking over time
prices_df = pd.read_csv('price_history.csv')
prices_df['date'] = pd.to_datetime(prices_df['date'])

plt.figure(figsize=(12, 6))
for product in prices_df['product'].unique()[:5]:
    product_data = prices_df[prices_df['product'] == product]
    plt.plot(product_data['date'], product_data['price'], label=product)

plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Price Trends Over Time')
plt.legend()
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('price_trends.png')

4. Seaborn Statistical Plots

import seaborn as sns

# Box plot by category
plt.figure(figsize=(12, 6))
sns.boxplot(x='category', y='price', data=df)
plt.xticks(rotation=45)
plt.title('Price Distribution by Category')
plt.tight_layout()
plt.savefig('price_by_category.png')

# Heatmap - Correlation
numeric_cols = ['price', 'rating', 'reviews', 'sold']
correlation = df[numeric_cols].corr()
sns.heatmap(correlation, annot=True, cmap='coolwarm')
plt.title('Feature Correlation')
plt.savefig('correlation.png')

5. Plotly Interactive

import plotly.express as px

# Interactive scatter
fig = px.scatter(
    df, 
    x='price', 
    y='rating',
    size='reviews',
    color='category',
    hover_name='name',
    title='Price vs Rating'
)
fig.write_html('interactive_scatter.html')
fig.show()

# Interactive line chart
fig = px.line(
    prices_df, 
    x='date', 
    y='price', 
    color='product',
    title='Price History'
)
fig.write_html('price_history.html')

6. Dashboard với Streamlit

# app.py
import streamlit as st
import pandas as pd
import plotly.express as px

st.title('Scraped Data Dashboard')

# Load data
df = pd.read_csv('products.csv')

# Sidebar filters
category = st.sidebar.selectbox('Category', df['category'].unique())
filtered = df[df['category'] == category]

# Metrics
col1, col2, col3 = st.columns(3)
col1.metric('Total Products', len(filtered))
col2.metric('Avg Price', f"{filtered['price'].mean():,.0f}")
col3.metric('Avg Rating', f"{filtered['rating'].mean():.1f}")

# Chart
fig = px.bar(filtered.head(20), x='name', y='price')
st.plotly_chart(fig)

# Run: streamlit run app.py

7. Export Charts

# High-quality export
plt.figure(figsize=(12, 8), dpi=300)
plt.plot(data)
plt.savefig('chart.png', dpi=300, bbox_inches='tight')

# PDF export
plt.savefig('chart.pdf', format='pdf')

# Multiple charts in one PDF
from matplotlib.backends.backend_pdf import PdfPages

with PdfPages('report.pdf') as pdf:
    for chart in charts:
        fig = create_chart(chart)
        pdf.savefig(fig)
        plt.close()

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