問(wèn)題描述

我有一個(gè)大小為 4 GB 的 XML 文件.我想解析它并將其轉(zhuǎn)換為數(shù)據(jù)框來(lái)處理它.但由于文件太大，以下代碼無(wú)法將文件轉(zhuǎn)換為 Pandas 數(shù)據(jù)框.代碼只是不斷加載，不提供任何輸出.但是當(dāng)我將它用于較小尺寸的類似文件時(shí)，我得到了正確的輸出.

I have an XML file of size 4 GB. I want to parse it and convert it to a Data Frame to work on it. But because the file size is too large the following code is unable to convert the file to a Pandas Data Frame. The code just keeps loading and does not provide any output. But when I use it for a similar file of smaller size I obtain the correct output.

任何人都可以提出任何解決方案.也許是一個(gè)代碼可以加快從 XML 到 Data Frame 的轉(zhuǎn)換過(guò)程或?qū)?XML 文件拆分為更小的子集.

Can anyone suggest any solution to this. Maybe a code that speeds up the process of conversion from XML to Data Frame or splitting of the XML file into smaller sub sets.

任何建議我是否應(yīng)該在我的個(gè)人系統(tǒng)(2 GB RAM)上處理如此大的 XML 文件，或者我應(yīng)該使用 Google Colab.如果是 Google Colab，那么有什么方法可以更快地將如此大的文件上傳到驅(qū)動(dòng)器，從而更快地上傳到 Colab?

Any suggestion whether I should work with such large XML files on my personal system (2 GB RAM) or I should use Google Colab. If Google Colab, then is there any way to upload such large files quicker to drive and thus to Colab?

以下是我使用的代碼:

import xml.etree.ElementTree as ET
tree = ET.parse("Badges.xml")
root = tree.getroot()

#Column names for DataFrame
columns = ['row Id',"UserId",'Name','Date','Class','TagBased']

#Creating DataFrame
df = pd.DataFrame(columns = columns)

#Converting XML Tree to a Pandas DataFrame

for node in root: 
    
    row_Id = node.attrib.get("Id")
    UserId = node.attrib.get("UserId")
    Name = node.attrib.get("Name")
    Date = node.attrib.get("Date")
    Class = node.attrib.get("Class")
    TagBased = node.attrib.get("TagBased")
    
    df = df.append(pd.Series([row_Id,UserId,Name,Date,Class,TagBased], index = columns), ignore_index = True)

以下是我的 XML 文件:

Following is my XML File:

<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" />

推薦答案

考慮 iterparse 用于以增量方式構(gòu)建樹的快速流處理.在每次迭代中構(gòu)建一個(gè)字典列表，然后您可以將其傳遞到 pandas.DataFrame 構(gòu)造函數(shù) once 外部循環(huán).下面調(diào)整根子節(jié)點(diǎn)的重復(fù)節(jié)點(diǎn)名稱:

Consider iterparse for fast streaming processing that builds tree incrementally. In each iteration build a list of dictionaries that you can then pass into pandas.DataFrame constructor once outside loop. Adjust below to name of repeating nodes of root's children:

from xml.etree.ElementTree import iterparse
#from cElementTree import iterparse
import pandas as pd

file_path = r"/path/to/Input.xml"
dict_list = []

for _, elem in iterparse(file_path, events=("end",)):
    if elem.tag == "row":
        dict_list.append({'rowId': elem.attrib['Id'],
                          'UserId': elem.attrib['UserId'],
                          'Name': elem.attrib['Name'],
                          'Date': elem.attrib['Date'],
                          'Class': elem.attrib['Class'],
                          'TagBased': elem.attrib['TagBased']})

        # dict_list.append(elem.attrib)      # ALTERNATIVELY, PARSE ALL ATTRIBUTES

        elem.clear()

df = pd.DataFrame(dict_list)

這篇關(guān)于Python 中的大型 XML 文件解析的文章就介紹到這了，希望我們推薦的答案對(duì)大家有所幫助，也希望大家多多支持html5模板網(wǎng)！