Question

我创建了以下函数，该函数将XML文件转换为DataFrame。此功能适用于小于1 GB的文件，以及大于RAM（13GB Google Colab RAM）崩溃的文件。如果我在Jupyter Notebook（4GB Laptop RAM）上本地尝试，也会发生同样的情况。有没有一种方法可以优化代码？

代码

#Libraries
import pandas as pd
import xml.etree.cElementTree as ET

#Function to convert XML file to Pandas Dataframe    
def xml2df(file_path):

  #Parsing XML File and obtaining root
  tree = ET.parse(file_path)
  root = tree.getroot()

  dict_list = []

  for _, elem in ET.iterparse(file_path, events=("end",)):
      if elem.tag == "row":
        dict_list.append(elem.attrib)      # PARSE ALL ATTRIBUTES
        elem.clear()

  df = pd.DataFrame(dict_list)
  return df

XML文件的一部分（“ Badges.xml”）

<badges>
  <row Id="82946" UserId="3718" Name="Teacher" Date="2008-09-15T08:55:03.923" Class="3" TagBased="False" />
  <row Id="82947" UserId="994" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />
  <row Id="82949" UserId="3893" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />
  <row Id="82950" UserId="4591" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />
  <row Id="82951" UserId="5196" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />
  <row Id="82952" UserId="2635" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />
  <row Id="82953" UserId="1113" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />

如何将大型XML文件转换为Pandas DataFrame？

0 个答案: