Python 解析和读取XML文件到PANDA数据帧

我有一个文件夹，里面还有其他几个文件夹.这些文件夹都包含TalkBank Childes XML文件

我已经编写了将数据提取到Pandas 数据框中的代码.这段代码的工作方式是要求用户输入包含XML文件的文件夹的目录，但我希望它的工作方式是，用户在任何时候想要提取XML文件时都不必一直输入目录.我可以这样编写吗？我可以在代码中指定所有文件目录，并在运行.py文件后从所有文件夹中提取XML.

import nltk
import os
import pandas as pd
from lxml import etree
from nltk.corpus.reader import CHILDESCorpusReader

# Function to get user input for the directory

def get_input_directory():
dir_childes_corpus = input("Enter the directory containing CHILDES XML files: ")
return dir_childes_corpus

# Path containing CHILDES XML files

dir_childes_corpus = get_input_directory()

# Empty lists to store speaker and utterance data

speakers_data = [ ]
utterance_data = [ ]

# Define namespaces

namespaces = {
'tb': 'http://www.talkbank.org/ns/talkbank',
'xsi': 'http://www.w3.org/2001/XMLSchema-instance'
}

for filename in os.listdir(dir_childes_corpus):
if filename.endswith('.xml'):
file_path = os.path.join(dir_childes_corpus, filename)

        # Parse XML data
        tree = etree.parse(file_path)
        
        # Extract dialogue_id from folder name and filename
        folder_name = os.path.basename(os.path.dirname(file_path))
        dialogue_id = f"{folder_name}/{os.path.splitext(filename)[0]}.xml"
    
    
        # Extract participant information
        speakers_info = []
        for participant in tree.xpath("//tb:Participants/tb:participant", namespaces=namespaces):
            speaker_info = {
                'dialogue_id': dialogue_id,
                'speaker_id': participant.get('id'),
                'speaker_name': participant.get('name'),
                'role': participant.get('role'),
                'age': participant.get('age'),
                'sex': participant.get('sex')
            }
            speakers_info.append(speaker_info)
        
        df_speakers = pd.DataFrame(speakers_info)
    
        # Extract utterance information including morphemes count
        utts_info = []
        for utt in tree.xpath("//tb:u", namespaces=namespaces):
            speaker = utt.get('who')
            uID = utt.get('uID')  # Extract uID
            utterance_text = ' '.join(utt.xpath(".//tb:w/text()", namespaces=namespaces))
            
           # Count morphemes
            utterance_length = len(utt.xpath(".//tb:w/tb:mor", namespaces=namespaces))
            
            utt_info = {
                'dialogue_id': dialogue_id,
                'uID': uID,
                'speaker': speaker,
                'utterance': utterance_text,
                'utterance_length': utterance_length
            }
            utts_info.append(utt_info)
        df_utts = pd.DataFrame(utts_info)
    
        # Append data to lists
        speakers_data.append(df_speakers)
        utterance_data.append(df_utts)

# Concatenate dataframes

speakers_data = pd.concat(speakers_data, ignore_index=True)
utterance_data = pd.concat(utterance_data, ignore_index=True)