How to Export a Pandas DataFrame to Excel: Basic Export
Pandas is a popular data analysis library in Python. It offers a variety of functions to work with data, including the ability to export data from a DataFrame to an Excel file.
In this section, we’ll walk through the basic steps to export a pandas DataFrame to Excel.
1. Import Pandas
import pandas as pd
2. Create a DataFrame
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'City': ['New York', 'San Francisco', 'London']})
3. Export the DataFrame to Excel
df.to_excel('output.xlsx', index=False)
Here, we’re using the to_excel()
function to export the DataFrame to an Excel file called output.xlsx
. The index=False
argument ensures that the DataFrame’s index column is not included in the exported file.
To view the output file, open output.xlsx
in your preferred spreadsheet program. You’ll see that the DataFrame has been successfully exported to Excel.
Export without Index
By default, when exporting a pandas DataFrame to Excel, the index column is included in the exported file. However, sometimes we may want to exclude it.
Here’s how to do that:
- Create the DataFrame as before.
- Use the
to_excel()
function with theindex
argument set toFalse
:
df.to_excel('output.xlsx', index=False)
This will export the DataFrame to Excel without the index column.
Export without Index and Header
In some cases, we may want to export a DataFrame without both the index column and the header row. Here’s how to do that:
- Create the DataFrame as before.
- Set the
header
argument in theto_excel()
function toNone
, and theindex
argument toFalse
:
df.to_excel('output.xlsx', header=None, index=False)
This will export the DataFrame to Excel without both the index and header rows.
Export and Name the Sheet
When exporting a DataFrame to Excel, the exported file typically has only one sheet, named <sheet1>
. However, we can change the name of the sheet to something more descriptive.
Here’s how to do that:
- Create the DataFrame as before.
- Use the
ExcelWriter()
function to create an Excel file object, and use theto_excel()
function to export the DataFrame to the file object. Set thesheet_name
argument to the desired sheet name:
with pd.ExcelWriter('output.xlsx') as writer:
df.to_excel(writer, sheet_name='Sheet1', index=False)
By default, ExcelWriter()
creates a new Excel file. We use the with
statement to ensure that the file is properly closed after we’re done writing to it.
When we call df.to_excel()
, we pass the writer
object to the function instead of a filename. This allows us to write data to an existing file.
We also use the sheet_name
argument to name the sheet.
Installing openpyxl
If you don’t have openpyxl installed on your system, you’ll need to do that before you can use it in pandas. Here’s how to install openpyxl:
- Open a command prompt or terminal window.
- Type the following command, then press Enter:
pip install openpyxl
This will install openpyxl using pip, the package installer for Python.
Conclusion
Exporting a pandas DataFrame to Excel is a useful feature that enables us to work with data more easily. By using these techniques, you can export data from a DataFrame to an Excel file with or without the column and header rows, or name the sheet to make it more easily identifiable.
And if you’re interested in using these features, it’s important to first install openpyxl. By following these steps, you’ll be able to easily export your pandas DataFrame to Excel.
In conclusion, exporting pandas DataFrame to Excel offers huge benefits to analysts and data scientists as it enables them to work with data more easily. The article has provided a step-by-step guide on exporting a pandas DataFrame to Excel, including the export process, without an index, without an index and header, and labeling the sheet.
Additionally, the article outlined the installation process for openpyxl. It’s important to note that exporting data to Excel provides an essential tool for data analysis across industries.
It’s a skill that all data analysts should master.