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Openpyxl and Pandas | Excel Automation with Python
Excel Automation with Python
course content

Conteúdo do Curso

Excel Automation with Python

Openpyxl and Pandas

Let's now import an excel file and transform it into a pandas DataFrame!

Using openpyxl and pandas together allowing for efficient data analysis and the creation of feature-rich Excel reports directly from Python.

Tarefa

  • Import pandas and openpyxl;
  • Extract the values of the workbook:
    • Use load_workbook() function that reads the file from the disk so that you can manipulate it;
    • workbook.active help get access to the currently active worksheet in the workbook;
  • sheet.values property generates a generator of all the rows in the worksheet, where each row is represented as a tuple of cell values.
  • Create a DataFrame (df) out of them:
    • Use pd.DataFrame() function.

Tarefa

  • Import pandas and openpyxl;
  • Extract the values of the workbook:
    • Use load_workbook() function that reads the file from the disk so that you can manipulate it;
    • workbook.active help get access to the currently active worksheet in the workbook;
  • sheet.values property generates a generator of all the rows in the worksheet, where each row is represented as a tuple of cell values.
  • Create a DataFrame (df) out of them:
    • Use pd.DataFrame() function.

Mark tasks as Completed

Tudo estava claro?

Let's now import an excel file and transform it into a pandas DataFrame!

Using openpyxl and pandas together allowing for efficient data analysis and the creation of feature-rich Excel reports directly from Python.

Tarefa

  • Import pandas and openpyxl;
  • Extract the values of the workbook:
    • Use load_workbook() function that reads the file from the disk so that you can manipulate it;
    • workbook.active help get access to the currently active worksheet in the workbook;
  • sheet.values property generates a generator of all the rows in the worksheet, where each row is represented as a tuple of cell values.
  • Create a DataFrame (df) out of them:
    • Use pd.DataFrame() function.

Mark tasks as Completed
Seção 1. Capítulo 10
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