Reading HTML tables into pandas DataFrames

The blog explains how to use the pandas.read_html() function to read HTML tables into Pandas DataFrames. This function is incredibly useful for web scraping and data analysis.

Introduction

The Pandas library in python provides a very handy function for copying tables from websites: pandas.read_html(). It helps us in extracting tables from an HTML page and converting them to Pandas DataFrames, ready for analysis.

Key Features

1. io

• It can be URL, file path, or HTML content.
• Specifies where the HTML table is located.

pd.read_html(io=url)

2. Match case

• Filters tables by matching text or a regular expression in the table’s HTML content.
• If there are multiple tables on a webpage, this helps find the one containing specific text.

pd.read_html(url, match=' ')

3. Flavor

• Specifies the HTML parser to use.
• Used if the default parser fails, or if the webpage’s HTML is poorly structured

pd.read_html(url, flavor='lxml')

4. Header

• Specifies the row(s) to use as column headers.

pd.read_html(url, header=0)

5. Index_col

• Specifies the column(s) to use as the DataFrame index.

pd.read_html(url, index_col=0)

6. Skiprows

• Skips the specified number of rows before parsing headers.
• Useful for messy tables

pd.read_html(url, skiprows=1)

7. Attrs

• Filters tables by their HTML attributes (like id, class).
• Handy when multiple tables exist.

pd.read_html(url, attrs={'class': ' '})

8. Parse_dates

• Converts columns to datetime format
• Useful for tables with date columns.

pd.read_html(url, parse_dates=True)

9. Converters

• It is a dictonary of column converters.
• Applies custom functions to columns.

converters = {2: lambda x: x.strip('%')}
pd.read_html(url, converters=converters)

10. na_values

• Specifies strings to recognize as NaN.
• Customizes missing value representations.

pd.read_html(url, na_values=['N/A', '-']))

For further reading refer to https://pandas.pydata.org/docs/reference/api/pandas.read_html.html#pandas.read_html

Conclusion

With above mentioned ways we can extract tables from websites using pandas.read_html(). By using its features, it becomes a quite useful tool for tasks such as research, data analysis and financial reporting. Its flexibility and ease make it a useful tool for anyone dealing with web data.