如何只计算目录中的文件?这将目录本身计算为一个文件:

len(glob.glob('*'))

当前回答

import os

_, _, files = next(os.walk("/usr/lib"))
file_count = len(files)

其他回答

我想扩展@Mr_and_Mrs_D的回复:

import os
folder = 'C:/Dropbox'
file_count = sum(len(files) for _, _, files in os.walk(folder))
print(file_count)

这将计算文件夹及其子文件夹中的所有文件。然而,如果你想做一些过滤——比如只计算以.svg结尾的文件,你可以这样做:

import os
file_count = sum(len([f for f in files if f.endswith('.svg')]) for _, _, files in os.walk(folder))
print(file_count)

你基本上可以替换:

len(档案)

:

Len ([f for f in files if f.endswith('.svg')])

Os.listdir()将比使用glob.glob更有效。要测试文件名是否为普通文件(而不是目录或其他实体),请使用os.path.isfile():

import os, os.path

# simple version for working with CWD
print len([name for name in os.listdir('.') if os.path.isfile(name)])

# path joining version for other paths
DIR = '/tmp'
print len([name for name in os.listdir(DIR) if os.path.isfile(os.path.join(DIR, name))])

我发现有时我不知道是否会收到文件名或文件的路径。所以我打印了os walk解决方案的输出:

def count_number_of_raw_data_point_files(path: Union[str, Path], with_file_prefix: str) -> int:
    import os
    path: Path = force_expanduser(path)

    _, _, files = next(os.walk(path))
    # file_count = len(files)
    filename: str
    count: int = 0
    for filename in files:
        print(f'-->{filename=}')  # e.g. print -->filename='data_point_99.json'
        if with_file_prefix in filename:
            count += 1
    return count

out:

-->filename='data_point_780.json'
-->filename='data_point_781.json'
-->filename='data_point_782.json'
-->filename='data_point_783.json'
-->filename='data_point_784.json'
-->filename='data_point_785.json'
-->filename='data_point_786.json'
-->filename='data_point_787.json'
-->filename='data_point_788.json'
-->filename='data_point_789.json'
-->filename='data_point_79.json'
-->filename='data_point_790.json'
-->filename='data_point_791.json'
-->filename='data_point_792.json'
-->filename='data_point_793.json'
-->filename='data_point_794.json'
-->filename='data_point_795.json'
-->filename='data_point_796.json'
-->filename='data_point_797.json'
-->filename='data_point_798.json'
-->filename='data_point_799.json'
-->filename='data_point_8.json'
-->filename='data_point_80.json'
-->filename='data_point_800.json'
-->filename='data_point_801.json'
-->filename='data_point_802.json'
-->filename='data_point_803.json'
-->filename='data_point_804.json'
-->filename='data_point_805.json'
-->filename='data_point_806.json'
-->filename='data_point_807.json'
-->filename='data_point_808.json'
-->filename='data_point_809.json'
-->filename='data_point_81.json'
-->filename='data_point_810.json'
-->filename='data_point_811.json'
-->filename='data_point_812.json'
-->filename='data_point_813.json'
-->filename='data_point_814.json'
-->filename='data_point_815.json'
-->filename='data_point_816.json'
-->filename='data_point_817.json'
-->filename='data_point_818.json'
-->filename='data_point_819.json'
-->filename='data_point_82.json'
-->filename='data_point_820.json'
-->filename='data_point_821.json'
-->filename='data_point_822.json'
-->filename='data_point_823.json'
-->filename='data_point_824.json'
-->filename='data_point_825.json'
-->filename='data_point_826.json'
-->filename='data_point_827.json'
-->filename='data_point_828.json'
-->filename='data_point_829.json'
-->filename='data_point_83.json'
-->filename='data_point_830.json'
-->filename='data_point_831.json'
-->filename='data_point_832.json'
-->filename='data_point_833.json'
-->filename='data_point_834.json'
-->filename='data_point_835.json'
-->filename='data_point_836.json'
-->filename='data_point_837.json'
-->filename='data_point_838.json'
-->filename='data_point_839.json'
-->filename='data_point_84.json'
-->filename='data_point_840.json'
-->filename='data_point_841.json'
-->filename='data_point_842.json'
-->filename='data_point_843.json'
-->filename='data_point_844.json'
-->filename='data_point_845.json'
-->filename='data_point_846.json'
-->filename='data_point_847.json'
-->filename='data_point_848.json'
-->filename='data_point_849.json'
-->filename='data_point_85.json'
-->filename='data_point_850.json'
-->filename='data_point_851.json'
-->filename='data_point_852.json'
-->filename='data_point_853.json'
-->filename='data_point_86.json'
-->filename='data_point_87.json'
-->filename='data_point_88.json'
-->filename='data_point_89.json'
-->filename='data_point_9.json'
-->filename='data_point_90.json'
-->filename='data_point_91.json'
-->filename='data_point_92.json'
-->filename='data_point_93.json'
-->filename='data_point_94.json'
-->filename='data_point_95.json'
-->filename='data_point_96.json'
-->filename='data_point_97.json'
-->filename='data_point_98.json'
-->filename='data_point_99.json'
854

注意,你可能需要排序。

import os

_, _, files = next(os.walk("/usr/lib"))
file_count = len(files)

我解决了这个问题,同时通过谷歌Colab计算谷歌驱动器目录中的文件数量,通过将自己定向到目录文件夹by

import os                                                                                                
%cd /content/drive/My Drive/  
print(len([x for x in os.listdir('folder_name/']))  

普通用户可以尝试

 import os                                                                                                     
 cd Desktop/Maheep/                                                     
 print(len([x for x in os.listdir('folder_name/']))