How to write a CSV file with pandas

Writing a CSV file with pandas exports a DataFrame to delimited text that spreadsheet, database, and automation tools can import. The export choices decide whether row indexes appear as a first column, which headers are written, how missing totals are represented, and whether dates and numbers keep the format expected by the next system.

DataFrame.to_csv() writes to a path, path-like object, file-like object, or CSV string. A path-based export is the usual handoff shape because it leaves a real file that can be inspected and read back before it is sent onward.

CSV is plain text, so pandas writes the cell values it receives instead of enforcing a spreadsheet safety policy. When exported values come from users and will be opened in spreadsheet software, review formula-looking prefixes such as =, +, -, and @ before sharing the file.

1. Create a Python script named write_orders_csv.py.

   write_orders_csv.py

   from pathlib import Path
    
   import pandas as pd
    
   output = Path("exports/orders.csv")
   output.parent.mkdir(parents=True, exist_ok=True)
    
   orders = pd.DataFrame(
       {
           "order_id": pd.Series(["A100", "A101", "A102"], dtype="string"),
           "customer": ["Ada Lovelace", "Lin Chen", "Maya Patel"],
           "region": ["EMEA", "APAC", "AMER"],
           "total_usd": [149.5, None, 212.0],
           "ordered_at": pd.to_datetime(
               ["2026-06-01", "2026-06-02", "2026-06-03"]
           ),
       }
   )
    
   orders.to_csv(
       output,
       sep=",",
       index=False,
       columns=["order_id", "customer", "region", "total_usd", "ordered_at"],
       na_rep="",
       float_format="%.2f",
       date_format="%Y-%m-%d",
       encoding="utf-8",
       lineterminator="\n",
   )
    
   print(f"Wrote {output}")

   index=False keeps pandas row labels out of the file. columns locks the export order, and sep=“,” keeps the delimiter explicit for the receiving system.

2. Run the script to write the CSV file.

   $ python3 write_orders_csv.py
   Wrote exports/orders.csv

3. Inspect the saved CSV text.

   $ cat exports/orders.csv
   order_id,customer,region,total_usd,ordered_at
   A100,Ada Lovelace,EMEA,149.50,2026-06-01
   A101,Lin Chen,APAC,,2026-06-02
   A102,Maya Patel,AMER,212.00,2026-06-03

   The header row, row count, blank total, date strings, and two-decimal totals should match the handoff target.
   Tool: Comma-Separated Values (CSV) Converter

4. Read the exported file back with pandas.

   $ python3 - <<'PY'
   import pandas as pd
   
   round_trip = pd.read_csv(
       "exports/orders.csv",
       dtype={"order_id": "string"},
       parse_dates=["ordered_at"],
   )
   
   print(round_trip.to_string(index=False))
   print()
   print(round_trip.dtypes.reindex(["order_id", "customer", "region", "total_usd", "ordered_at"]))
   print()
   print(f"Rows: {len(round_trip)}")
   print(f"Columns: {', '.join(round_trip.columns)}")
   print(f"Missing totals: {round_trip['total_usd'].isna().sum()}")
   PY
   order_id     customer region  total_usd ordered_at
       A100 Ada Lovelace   EMEA      149.5 2026-06-01
       A101     Lin Chen   APAC        NaN 2026-06-02
       A102   Maya Patel   AMER      212.0 2026-06-03
   
   order_id              string
   customer                 str
   region                   str
   total_usd            float64
   ordered_at    datetime64[us]
   dtype: object
   
   Rows: 3
   Columns: order_id, customer, region, total_usd, ordered_at
   Missing totals: 1

   The read-back output should show three rows, five columns, one missing total, a string order ID, and a parsed ordered_at datetime column.

5. Remove the sample script and export directory after confirming the CSV output.

   $ rm -r write_orders_csv.py exports