Python: fastest way of checking if there are more than x files in a folder

To get the fastest it’s probably something hacky.

My guess was:

def iterdir_approach(path):
    iter_of_files = (x for x in Path(path).iterdir() if x.isfile())
    try:
        next(iter_of_files)
        next(iter_of_files)
        next(iter_of_files)
        return True
    except:
        return False

We create a generator and try to exhaust it, catching the thrown exception if necessary.

To profile the approaches we create a bunch of directories with a bunch of files in them :

import shutil
import tempfile
import timeit
import matplotlib.pyplot as plt
from pathlib import Path


def create_temp_directory(num_directories):
    temp_dir = tempfile.mkdtemp()
    for i in range(num_directories):
        dir_path = os.path.join(temp_dir, f"subdir_{i}")
        os.makedirs(dir_path)
        for j in range(random.randint(0,i)):
            file_path = os.path.join(dir_path, f"file_{j}.txt")
            with open(file_path, 'w') as file:
                file.write("Sample content")
    return temp_dir

We define the various approaches (Copied the other two from the answers to the question:

def iterdir_approach(path):
    #@swozny
    iter_of_files = (x for x in Path(path).iterdir() if x.isfile())
    try:
        next(iter_of_files)
        next(iter_of_files)
        next(iter_of_files)
        return True
    except:
        return False

def len_os_dir_approach(path):
    #@bluppfisk
    return len(os.listdir(path)) > 2


def check_files_os_scandir_approach(path):
    #@PoneyUHC
    MINIMUM_SIZE = 3
    file_count = 0
    for entry in os.scandir(path):
        if entry.is_file():
            file_count += 1
        if file_count == MINIMUM_SIZE:
            return True
    return False


def path_resolve_approach(path):
    #@matleg
    directory_path = Path(path).resolve()
    nb_files = 0
    enough_files = False
    for file_path in directory_path.glob("*"):
        if file_path.is_file():
            nb_files += 1
        if nb_files > 2:
            return True
    return False

def dilettant_approach(path):
    #@dilettant
    gen = os.scandir(path)  # OP states only files in folder /path/
    enough = 3  # At least 2 files

    has_enough = len(list(itertools.islice(gen, enough))) >= enough

    return has_enough
def adrian_ang_approach(path):
    #@adrian_ang
    count = 0
    with os.scandir(path) as entries:
        for entry in entries:
            if entry.is_file():
                count += 1
                if count > 2:
                    return True
    return False

Then we profile the code using timeit.timeit and plot the execution times for various amounts of directories:

num_directories_list = [10, 50, 100, 200, 500,1000]
approach1_times = []
approach2_times = []
approach3_times = []
approach4_times = []
approach5_times = []
approach6_times = []


for num_directories in num_directories_list:
    temp_dir = create_temp_directory(num_directories)
    subdir_paths = [str(p) for p in Path(create_temp_directory(num_directories)).iterdir()]
    approach1_time = timeit.timeit(lambda: [iterdir_approach(path)for path in subdir_paths], number=5)
    approach2_time = timeit.timeit(lambda: [check_files_os_scandir_approach(path)for path in subdir_paths], number=5)
    approach3_time = timeit.timeit(lambda: [path_resolve_approach(path)for path in subdir_paths], number=5)
    approach4_time = timeit.timeit(lambda: [len_os_dir_approach(path)for path in subdir_paths], number=5)
    approach5_time = timeit.timeit(lambda: [dilettant_approach(path)for path in subdir_paths], number=5)
    approach6_time = timeit.timeit(lambda: [adrian_ang_approach(path)for path in subdir_paths], number=5)


    approach1_times.append(approach1_time)
    approach2_times.append(approach2_time)
    approach3_times.append(approach3_time)
    approach4_times.append(approach4_time)
    approach5_times.append(approach5_time)
    approach6_times.append(approach6_time)




    shutil.rmtree(temp_dir)

Visualization of the results

plt.plot(num_directories_list, approach1_times, label='iterdir_approach')
plt.plot(num_directories_list, approach2_times, label='check_files_os_scandir_approach')
plt.plot(num_directories_list, approach3_times, label='path_resolve_approach')
plt.plot(num_directories_list, approach4_times, label='os_dir_approach')
plt.plot(num_directories_list, approach5_times, label='dilettant_approach')
plt.plot(num_directories_list, approach6_times, label='adrian_ang_approach')


plt.xlabel('Number of Directories')
plt.ylabel('Execution Time (seconds)')
plt.title('Performance Comparison')
plt.legend()
plt.show()

Closeup of best 3 solutions: