scandir vs listdir in Python: Which Is Faster? Tested

Intro — what this post promises

For a plain list of names, os.listdir is slightly faster than os.scandir (about 4,288k vs 3,280k entries/s on an 8,000-file directory in Python 3.13.5), but once you check file type os.scandir wins by about 8.6x (3,056k vs 354k entries/s at 2,000 entries) because DirEntry.is_file() reuses type data from the directory read. Use listdir for simple name dumps and scandir whenever you branch on is_file, is_dir or stat.

It is not a remake of the glob vs rglob vs walk localhost lab (pattern matching / recursive globs). Here the question is directory iterator APIs and when cached DirEntry metadata wins.

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Lab honesty (1 Oct 2026 IST): Python 3.13.5. Fixtures under lab-evidence/90-scandir-vs-listdir/results/fixture_tree/. Affiliates: 0. No Docker.

Verdict up front (2000 entries, is_file path): scandir ~3056.2 k/s vs listdir+isfile ~353.7 k/s (~8.64×) vs Path.iterdir().is_file ~242.4 k/s. For names only at 8000 files, listdir ~4288.1 k/s edged scandir ~3279.9 k/s; iterdir ~1079.8 k/s.

Arms

Lab topology

flat: 500 / 2000 / 8000 files · 7 rounds · p50
isfile check at n=2000
nested: 50 dirs × 40 files (2050 entries)
metric: entries/s

Script: lab-evidence/90-scandir-vs-listdir/results/run_lab.py.

Lead table — flat names (p50 entries/s)

When you need is_file (n=2000)

~8.64× — this is the scandir headline. Extra stat per name is the tax DirEntry avoids when the type is already known from the directory read.

Nested walk (2050 entries)

Reading it

• Names only: listdir is fine (and slightly fastest here).
• Need type/stat: prefer scandir / DirEntry.
• Path.iterdir is ergonomic; expect a measurable object-creation tax on huge directories.
• Use os.walk or a scandir stack for trees; rglob("*") was ~4.45× slower on this nest.

Names-only vs metadata

If you only print names, listdir avoids building DirEntry objects and won slightly here. The moment you call isfile/isdir/stat on each name, you pay syscalls that scandir often already satisfied. Rule of thumb from this box: branching on type → scandir; names dump → listdir is acceptable.

pathlib tax

Path.iterdir returns Path objects — great for chaining .suffix / .with_name. On an 8000-file directory that object churn showed up as ~4.0× slower than raw listdir. Use pathlib at the edges; tight loops may want scandir/listdir.

Pitfalls

• Calling os.path.isfile on every listdir name in a hot path.
• Assuming scandir is always fastest for names-only (not on this box).
• Forgetting with os.scandir(...) (resource lifetime).
• Following symlinks unintentionally in recursive walks.

Reproduce

python3 lab-evidence/90-scandir-vs-listdir/results/run_lab.py

Evidence: summary.json, fixture_tree/.

Limits

One Linux box, synthetic files on local FS. Not networked filesystems / huge directories with millions of entries. Cold-cache dentries may differ.

Takeaway

For name listing, listdir ~4288.1 k/s ≈ scandir ~3279.9 k/s at 8000 files. For is_file filtering, scandir ~8.64× beat listdir+stat. Default to scandir when you branch on file type; keep listdir for simple name dumps.

FAQ

Is os.scandir faster than os.listdir?

Only when you need file type or stat. For names only, listdir edged scandir at every size tested (500, 2,000 and 8,000 files).

Why is scandir faster with is_file?

listdir returns bare names, so os.path.isfile has to stat each path. scandir's DirEntry already knows the type from the directory read, which avoids that extra syscall.

What about pathlib Path.iterdir?

It was the slowest: about 1,080k names/s at 8,000 files (around 4x slower than listdir) and 242k entries/s with is_file. Use it where Path methods matter more than speed.

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