有没有更好的方法来使用glob。Glob在python中获取多个文件类型的列表,如.txt, .mdown和.markdown?现在我有这样的东西:

projectFiles1 = glob.glob( os.path.join(projectDir, '*.txt') )
projectFiles2 = glob.glob( os.path.join(projectDir, '*.mdown') )
projectFiles3 = glob.glob( os.path.join(projectDir, '*.markdown') )

当前回答

不是glob,这里是另一种使用列表理解的方式:

extensions = 'txt mdown markdown'.split()
projectFiles = [f for f in os.listdir(projectDir) 
                  if os.path.splitext(f)[1][1:] in extensions]

其他回答

这应该有用:

import glob
extensions = ('*.txt', '*.mdown', '*.markdown')
for i in extensions:
    for files in glob.glob(i):
        print (files)

例如:

import glob
lst_img = []
base_dir = '/home/xy/img/'

# get all the jpg file in base_dir 
lst_img += glob.glob(base_dir + '*.jpg')
print lst_img
# ['/home/xy/img/2.jpg', '/home/xy/img/1.jpg']

# append all the png file in base_dir to lst_img
lst_img += glob.glob(base_dir + '*.png')
print lst_img
# ['/home/xy/img/2.jpg', '/home/xy/img/1.jpg', '/home/xy/img/3.png']

一个函数:

import glob
def get_files(base_dir='/home/xy/img/', lst_extension=['*.jpg', '*.png']):
    """
    :param base_dir:base directory
    :param lst_extension:lst_extension: list like ['*.jpg', '*.png', ...]
    :return:file lists like ['/home/xy/img/2.jpg','/home/xy/img/3.png']
    """
    lst_files = []
    for ext in lst_extension:
        lst_files += glob.glob(base_dir+ext)
    return lst_files

也许有更好的办法,但是:

import glob
types = ('*.pdf', '*.cpp') # the tuple of file types
files_grabbed = []
for files in types:
    files_grabbed.extend(glob.glob(files))

# files_grabbed is the list of pdf and cpp files

也许还有其他的方法,所以等待别人提出更好的答案。

一句话:

img_ext = (".jpg", ".jpeg", ".jpeg", "。就是“,”。jfi”、“.jif”、“jpg”)

目录= './'

files = [glob.glob(目录+'/*')中的文件的文件if file.endswith(img_ext)]

import glob
import pandas as pd

df1 = pd.DataFrame(columns=['A'])
for i in glob.glob('C:\dir\path\*.txt'):
    df1 = df1.append({'A': i}, ignore_index=True)
for i in glob.glob('C:\dir\path\*.mdown'):
    df1 = df1.append({'A': i}, ignore_index=True)
for i in glob.glob('C:\dir\path\*.markdown):
    df1 = df1.append({'A': i}, ignore_index=True)