Python Write Excel File

Syntax

import pandas as pd

# create a DataFrame
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'David'],
                    'Age': [25, 32, 18, 47],
                    'Country': ['USA', 'Canada', 'UK', 'Australia']})

# write DataFrame to Excel file
df.to_excel('file_name.xlsx', index=False)

Example

import pandas as pd

data = {'Name':['Tom', 'Jim', 'George', 'Anne'],
        'Age':[23, 17, 19, 21],
        'Gender':['M', 'M', 'M', 'F']}

df = pd.DataFrame(data)

# write DataFrame to Excel file
df.to_excel('students.xlsx', sheet_name='Sheet1', index=False)

Output

This code will create a file named students.xlsx in the current working directory with the following data:

Name	Age	Gender
Tom	23	M
Jim	17	M
George	19	M
Anne	21	F

Explanation

Using the pandas library, you can create a DataFrame containing the data you want to write to an Excel file. Next, you can use the to_excel() function to write the DataFrame to an Excel file. The first argument to the to_excel() function is the file name you want to write to, and the index=False parameter tells pandas not to include the index in the output file.

The second example uses a dictionary to create a DataFrame for a list of students and then writes it to an Excel file named students.xlsx with the sheet_name parameter as Sheet1 and the index parameter as False.

Use

This code can be used to write data to an Excel file for archival or sharing purposes. It can be used for various data-related work, including data analysis, accounting, budgeting, and financial management.

Important Points

• The pandas library is required to execute the code.
• The file extension should be .xlsx.
• The index parameter is optional and defaults to True. Set it to False to exclude the index column.
• If the file already exists, the to_excel() function will overwrite it by default. To avoid this, use the mode parameter to select a different write mode.

Summary

In this code snippet, we learned how to write data to an Excel file using the pandas library. We also explored the various parameters and the importance of the pandas library in the code. This knowledge is useful in various contexts, including data analysis, accounting, budgeting, and financial management.