pandas to_csv Examples

Sample DataFrame context

A cleaned dataset ready to share or load into another tool.

Syntax or pattern

df.to_csv("clean_sales.csv", index=False)

✍️

5 practical examples

Export without the index

Save a clean CSV for another tool.

report.to_csv("monthly_report.csv", index=False)

The file does not include the pandas index as an extra column.

Export selected columns

Save only the fields needed by the user.

cols = ["month", "region", "total_sales"]
report[cols].to_csv("summary.csv", index=False)

The export is focused and easier to read.

Write multiple Excel sheets

Create one workbook with several tabs.

with pd.ExcelWriter("report.xlsx") as writer:
    sales_summary.to_excel(writer, sheet_name="Sales", index=False)
    customer_summary.to_excel(writer, sheet_name="Customers", index=False)

The workbook contains separate sheets for each report table.

Round values before export

Make numeric outputs more readable.

report["margin"] = report["margin"].round(2)

The exported report has cleaner numeric values.

Export records to JSON

Create JSON records for an app or API.

report.to_json("report.json", orient="records")

The JSON file stores each row as an object.

Common mistakes to avoid

Changing the original DataFrame before checking the result.
Forgetting to inspect row counts before and after the operation.
Using a pattern without confirming column names and data types.

FAQ

What is the main use of to_csv Examples?

Export cleaned DataFrames to CSV with selected columns and formatting choices.

Should I use this in a notebook or a script?

Both work. Use a notebook while exploring the data, then move the final workflow into a repeatable script when it is stable.

How do I avoid breaking my source data?

Create a copy of the DataFrame before transforming it and check row counts, missing values and sample rows after each important step.

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