Python

Featurecsv (standard lib)pandas.to_csv()
LibraryBuilt-in (import csv)External (pandas, requires install)
Input/OutputLists, dictsDataFrame
Header rowManual (writer.writerow([...]))Automatic from column names
Data formattingManualHandles types like datetime, NaNs, etc.
Performance (large data)Very fast for simple use casesOptimized for large tables
Compression (gzip, bz2…)Not supported nativelyBuilt-in support via compression=
Index exportN/AOptional (index=False)
Multi-index exportNot supportedSupported
Handling of nullsManualAutomatic (e.g., NaN becomes empty string)
Type preservationManualPreserves types (to the extent CSV allows)
Append modeEasy (open(..., 'a'))Possible but requires care

Example Comparison

1. Using csv

import csv
 
data = [
    ['name', 'age'],
    ['Alice', 30],
    ['Bob', 25]
]
 
with open('people.csv', 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerows(data)

You are responsible for formatting rows as lists (or dicts with DictWriter).

2. Using pandas.to_csv()

import pandas as pd
 
df = pd.DataFrame({
    'name': ['Alice', 'Bob'],
    'age': [30, 25]
})
 
df.to_csv('people.csv', index=False)

No manual row handling — pandas handles headers, data alignment, and types.

When to Use Each

Use csv if:

  • You’re not using pandas.
  • You need lightweight scripts with zero dependencies.
  • You’re working with custom file formats (e.g., pipe-delimited, legacy systems).
  • You’re writing line-by-line or in memory-constrained environments.

Use pandas.to_csv() if:

  • You’re already working with DataFrames.
  • You want fast, bulk export of structured tabular data.
  • You need features like compression, encoding control, or null handling.