如何自动过滤 python 中的文件？

Question

我有一个文件夹 Measurement，其中包含许多文件（file1data.csv，file2data.csv......，file10data.csv） .csv 格式。我现在使用的不同功能：

1.readfiles(Path) ---read the files from the folder
2.initialization(Data) -- some initailization
3.Data_to_analyze() ---some specific data for the filtering
4.create_subset()  - making subset of the data to analyse
5.filter_file1()  - function which contain filtering conditions for file 1
6.filter_file2()  - function which contain filtering conditions for file 2 
7.filter_file3()  - function which contain filtering conditions for file 3

函数5,6,7想在函数内部调用4.Currenlty我在注释函数5的时候需要过滤文件2
函数3,4对所有文件通用

我想自动执行此过程：

函数1中，读取文件1时需要执行函数2-->3-->4-->5.
同file2data，需要执行2-->3-->4-->6 .

如何实现自动化？

目前节目编排如下：

def readfiles(Path):
    Filenames = glob.glob(Path + '/*.csv')
    for Filename in Filenames:
       #read file
       initialization(Data)

def initialization(Data):
    Data_to_analyze()

def Data_to_analyze():
    .
    .
    create_subset()

def create_subset():
    .
    .
    . 
    filter_file1()
    #filter_file2()

def filter_file1()
    .
    #filtering conditions
    .
    .
     
   

def filter_file2()
    .
    #filtering conditions
    .
    .

我想改进上面的代码。

因为我是初学者，这会很有帮助

Answer 1

我猜你对 10 个文件有 10 个过滤函数。如果是，这个答案对你有用。

def readfiles(Path):
    Filenames = glob.glob(Path + '/*.csv')
    for count, Filename in enumerate(Filenames, 1):
       #read file
       function_no = count
       initialization(Data, function_no)

def initialization(Data, function_no):
    Data_to_analyze()
    create_subset()
    function_map.get(function_no)()

def Data_to_analyze():
    .
    .
    

def create_subset():
    .
    .
    . 
    
    #filter_file2()

def filter_file1()
    .
    #filtering conditions
    .
    .
       
    final_file2df.to_excel('new_file_path', ignore_index=True)

function_map = {
    1: filter_file1
    2: filter_file2
    3: filter_file3
    # please continue the same for remain...
}

如何自动过滤 python 中的文件？

How to automate filtering files in python?

python

data-analysis

pandas

python-multiprocessing