Common Formats Supported by Pandas

Format Type	Description	Reader Method	Writer Method
CSV	Comma-Separated Values	read_csv	to_csv
JSON	JavaScript Object Notation	read_json	to_json
HTML	Hypertext Markup Language	read_html	to_html
Excel	Microsoft Excel (XLS, XLSX)	read_excel	to_excel
SQL	SQL Databases	read_sql, read_sql_query	to_sql
Parquet	Columnar Storage	read_parquet	to_parquet
Pickle	Python Object Serialization	read_pickle	to_pickle

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File Handling with Examples

1. CSV Input/Output

• Reading a CSV:

  df = pd.read_csv('example.csv')  # Reads CSV into DataFrame

• Writing a CSV:

  df.to_csv('new_file.csv', index=False)  # Saves DataFrame to CSV

2. HTML Input/Output

• Reading HTML Tables:

  tables = pd.read_html('https://en.wikipedia.org/wiki/World_population')

  • This reads all <table> elements from a webpage into a list of DataFrames.

• Writing to HTML:

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

3. Excel Input/Output

• Reading Excel Sheets:

  df = pd.read_excel('file.xlsx', sheet_name='Sheet1')

• Writing to Excel:

  df.to_excel('output.xlsx', sheet_name='Sheet1', index=False)

4. SQL Connections

• Creating a temporary SQLite database:

  from sqlalchemy import create_engine
  engine = create_engine('sqlite:///:memory:')
  df.to_sql('table_name', con=engine)

• Querying the SQL database:

  result = pd.read_sql("SELECT * FROM table_name", con=engine)

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Additional Notes

• For Excel, install necessary libraries: pip install openpyxl xlrd.
• For HTML: Ensure you have libraries like lxml, html5lib, and BeautifulSoup4.
• For SQL: Use appropriate libraries for your database (e.g., sqlalchemy for SQLite).

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Practical Tips

1. Always validate the file paths and libraries needed for reading/writing specific formats.
2. If working with large data, consider columnar formats like Parquet for better performance.
3. For troubleshooting permissions or library-specific errors, refer to documentation or community resources.