🌐 Pandas Work with HTML Data – Read Tables from Webpages and Export HTML

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🧲 Introduction – Why Work with HTML in Pandas?

Web pages often contain structured tabular data—from financial reports to sports stats. With Pandas, you can easily read tables directly from URLs or HTML files using read_html() and export DataFrames to HTML using to_html(). It’s a quick way to integrate web scraping or embed tables in websites.

🎯 In this guide, you’ll learn:

• How to extract tables from HTML using read_html()
• Handle multiple tables, custom parsing, and errors
• Export DataFrames as HTML tables using to_html()
• Use cases like reports, dashboards, and documentation

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📥 1. Reading HTML Tables from a URL

import pandas as pd

tables = pd.read_html("https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)")
print(len(tables))          # Number of tables found
print(tables[0].head())     # First table

✅ Returns a list of DataFrames—one for each table found on the page.

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🧾 2. Reading HTML from a File

tables = pd.read_html('local_file.html')
df = tables[0]

✅ Works with local .html files as well.

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🧪 3. Common Parameters in read_html()

Parameter	Description
io	URL, file, or string containing HTML
match	Regex or string to match in table text
flavor	'bs4' (default), 'lxml'
header	Row to use as column names
index_col	Column(s) to set as index
attrs	Match only tables with specific HTML attributes
skiprows	Skip rows at the top of table
parse_dates	Attempt date parsing in columns

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🔍 4. Select Specific Tables by Keyword Match

tables = pd.read_html("page.html", match="Population")

✅ Returns only tables that contain the keyword in the header/body.

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🏷️ 5. Extract a Table by HTML Attributes

tables = pd.read_html("page.html", attrs={'class': 'wikitable'})

✅ Filters tables by HTML attributes like id, class, or style.

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⚠️ 6. Dependency Requirements

To use read_html(), you need:

pip install lxml html5lib beautifulsoup4

✅ Pandas uses BeautifulSoup or lxml to parse HTML behind the scenes.

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📤 7. Export DataFrame to HTML

df.to_html('output.html', index=False)

👉 Example Output:

<table border="1" class="dataframe">
  <thead>...</thead>
  <tbody>...</tbody>
</table>

✅ Creates a full HTML table from a DataFrame.

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🎨 8. Customize HTML Output

html = df.to_html(classes='my-table', border=0, justify='center')

✅ Customize table appearance using classes, border, and justify.

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📌 Summary – Recap & Next Steps

Pandas makes working with HTML data seamless. Whether scraping tables from websites or embedding results into your own reports, read_html() and to_html() provide a quick and effective solution.

🔍 Key Takeaways:

• read_html() scrapes all or matched tables into DataFrames
• to_html() exports tables for websites or documentation
• Use attrs, match, and header for table filtering and structure
• Requires dependencies like lxml, html5lib, or bs4

⚙️ Real-world relevance: Used in web scraping, financial dashboards, open data analysis, and automated reporting systems.

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❓ FAQs – Working with HTML in Pandas

❓ Can Pandas extract multiple tables from a single page?
✅ Yes. read_html() returns a list of all tables found.

❓ What if no tables are found?
✅ A ValueError is raised. Make sure the HTML is well-formed and use flavor='lxml'.

❓ How can I save the DataFrame as an embeddable HTML snippet?
Use:

html_snippet = df.to_html(index=False, classes='styled-table')

❓ Can I filter tables using class or ID?
✅ Yes. Use:

pd.read_html('page.html', attrs={'class': 'target-class'})

❓ Is HTML reading fast?
⚠️ Slower than CSV/Excel due to HTML parsing overhead.

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