How Can I Efficiently Load Specific Worksheets from a Large Excel File with Pandas?

Loading Specific Worksheets from a Workbook Efficiently with Pandas

Pandas' pd.read_excel() function is a powerful tool for reading Excel workbooks. However, when working with large files that contain multiple worksheets, loading the entire workbook can be inefficient, especially if you only require data from a few specific sheets.

Understanding the Loading Process with pd.read_excel()

When using pd.read_excel() on a particular worksheet, it appears that the entire workbook is loaded into memory. This is because pandas internally uses an ExcelFile object to represent the workbook. The ExcelFile object parses the entire file during its initialization, regardless of which worksheet is specified.

Loading Specific Sheets Efficiently

To optimize the loading process, consider using the pd.ExcelFile object directly. By instantiating an ExcelFile object with the workbook path, you can access specific worksheets without reloading the entire file.

For instance:

xls = pd.ExcelFile('path_to_file.xls')
df1 = pd.read_excel(xls, 'Sheet1')
df2 = pd.read_excel(xls, 'Sheet2')

This approach loads the entire workbook only once during the creation of the ExcelFile object. Subsequent calls to pd.read_excel() will retrieve data from the specified worksheets without incurring the overhead of re-loading the file.

Loading Multiple Sheets

Additionally, you can specify a list of sheet names or indices to pd.read_excel() to load multiple sheets simultaneously. This returns a dictionary where the keys are the sheet names or indices, and the values are the corresponding data frames.

For example:

sheet_list = ['Sheet1', 'Sheet2']
df_dict = pd.read_excel(xls, sheet_list)

Loading All Sheets

If you need to load all worksheets in the workbook, set the sheet_name parameter to None:

df_dict = pd.read_excel(xls, sheet_name=None)

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