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.

Завдання

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.

Завдання

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

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

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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.

Завдання

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

Секція 1. Розділ 10

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