PyInns - Home

Iterating with .iloc in pandas DataFrame – Python 2026 with Efficient Code

Using .iloc to iterate over a pandas DataFrame is a common pattern, but in 2026 it is often a sign of suboptimal code. While .iloc is fast for positional indexing, iterating with it is usually much slower than vectorized alternatives.

This March 15, 2026 guide explains when .iloc iteration is acceptable and, more importantly, how to avoid it for better performance.

TL;DR — Key Takeaways 2026

.iloc is great for accessing specific positions, but slow for iteration
Avoid for i in range(len(df)) with df.iloc[i]
Prefer vectorized operations, .itertuples(), or .apply() (sparingly)
Vectorized code is usually 10–100x faster than .iloc loops
Use .iloc only when you truly need positional access

1. The Common Anti-Pattern


# ❌ Bad: Iterating with .iloc (very slow)
for i in range(len(df)):
    row = df.iloc[i]
    df.loc[i, 'new_col'] = row['score'] * 1.1

2. Better Alternatives in 2026


# ✅ Option 1: Vectorized (Best)
df['new_col'] = df['score'] * 1.1

# ✅ Option 2: itertuples() - Much faster than iloc
for row in df.itertuples():
    # row is a namedtuple - very fast access
    pass

# ✅ Option 3: When you need index and value
for idx, score in enumerate(df['score']):
    pass

3. When .iloc Iteration Is Acceptable


# Only acceptable in rare cases:
for i in range(len(df)):
    if some_complex_condition(df.iloc[i]):
        df.iloc[i, df.columns.get_loc('status')] = 'special'

Even then, a vectorized approach with np.where() or boolean indexing is usually better.

4. Best Practices in 2026

Avoid for i in range(len(df)) with .iloc[i]
Use vectorized operations whenever possible
Use .itertuples() when you must iterate row by row
Use boolean indexing for conditional updates
Use .apply() only when vectorization isn’t feasible

Conclusion — Iterating with .iloc in 2026

Iterating over a DataFrame with .iloc is one of the most common performance anti-patterns in pandas. In 2026, the most efficient pandas code almost never uses explicit .iloc loops. Instead, it relies on vectorized operations, boolean indexing, and occasionally .itertuples() when row-by-row logic is truly required.

Next steps: