csv module

Python

Feature	csv (standard lib)	pandas.to_csv()
Library	Built-in (import csv)	External (pandas, requires install)
Input/Output	Lists, dicts	DataFrame
Header row	Manual (writer.writerow([...]))	Automatic from column names
Data formatting	Manual	Handles types like datetime, NaNs, etc.
Performance (large data)	Very fast for simple use cases	Optimized for large tables
Compression (gzip, bz2…)	Not supported natively	Built-in support via compression=
Index export	N/A	Optional (index=False)
Multi-index export	Not supported	Supported
Handling of nulls	Manual	Automatic (e.g., NaN becomes empty string)
Type preservation	Manual	Preserves types (to the extent CSV allows)
Append mode	Easy (open(..., 'a'))	Possible but requires care

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🔹 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).

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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.

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🔸 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.