提高将具有元素和名称空间的XML解析为Pandas的速度

问题描述

所以我有一个 52M xml文件，该文件由 115139 元素组成.

So I have a 52M xml file, which consists of 115139 elements.

from lxml import etree
tree  = etree.parse(file)
root  = tree.getroot()
In [76]: len(root)
Out[76]: 115139

我具有此功能，可以遍历 root 中的元素，并将每个已解析的元素插入Pandas DataFrame中.

I have this function that iterates over the elements within root and inserts each parsed element inside a Pandas DataFrame.

def fnc_parse_xml(file, columns):
    start = datetime.datetime.now()
    df    = pd.DataFrame(columns=columns)
    tree  = etree.parse(file)
    root  = tree.getroot()
    xmlns = './/{' + root.nsmap[None] + '}'

    for loc,e in enumerate(root):
        tot = []
        for column in columns:
            tot.append(e.find(xmlns + column).text)
        df.at[loc,columns] = tot

    end = datetime.datetime.now()
    diff = end-start
    return df,diff

此过程有效，但是需要很多时间.我有一个配备16GB RAM的i7.

This process works but it takes a lot of time. I have an i7 with 16GB of RAM.

In [75]: diff.total_seconds()/60
Out[75]: 36.41769186666667
In [77]: len(df)
Out[77]: 115139

我敢肯定，有一种更好的方法可以将 52M xml文件解析为Pandas DataFrame.

I'm pretty sure there is a better way of parsing a 52M xml file into a Pandas DataFrame.

这是xml文件的摘录...

This is an extract of the xml file ...

<findToFileResponse xmlns="xmlapi_1.0">
    <equipment.MediaIndependentStats>
        <rxOctets>0</rxOctets>
        <txOctets>0</txOctets>
        <inSpeed>10000000</inSpeed>
        <outSpeed>10000000</outSpeed>
        <time>1587080746395</time>
        <seconds>931265</seconds>
        <port>Port 3/1/6</port>
        <ip>192.168.157.204</ip>
        <name>RouterA</name>
    </equipment.MediaIndependentStats>
    <equipment.MediaIndependentStats>
        <rxOctets>0</rxOctets>
        <txOctets>0</txOctets>
        <inSpeed>100000</inSpeed>
        <outSpeed>100000</outSpeed>
        <time>1587080739924</time>
        <seconds>928831</seconds>
        <port>Port 1/1/1</port>
        <ip>192.168.154.63</ip>
        <name>RouterB</name>
    </equipment.MediaIndependentStats>
</findToFileResponse>

关于如何提高速度的任何想法?

any ideas on how to improve speed?

对于上述xml的摘录，函数 fnc_parse_xml(file，columns)返回此DF....

For the extract of the above xml, the function fnc_parse_xml(file, columns) returns this DF....

In [83]: df
Out[83]:
  rxOctets txOctets   inSpeed  outSpeed           time seconds        port               ip     name
0        0        0  10000000  10000000  1587080746395  931265  Port 3/1/6  192.168.157.204  RouterA
1        0        0    100000    100000  1587080739924  928831  Port 1/1/1   192.168.154.63  RouterB

推荐答案

另一种方法是使用 iterparse ...

Another option instead of building a tree by parsing the entire XML file is to use iterparse...

import datetime
import pandas as pd
from lxml import etree


def fnc_parse_xml(file, columns):
    start = datetime.datetime.now()
    # Capture all rows in array.
    rows = []
    # Process all "equipment.MediaIndependentStats" elements.
    for event, elem in etree.iterparse(file, tag="{xmlapi_1.0}equipment.MediaIndependentStats"):
        # Each row is a new dict.
        row = {}
        # Process all chidren of "equipment.MediaIndependentStats".
        for child in elem.iterchildren():
            # Create an entry in the row dict using the local name (without namespace) of the element for
            # the key and the text content as the value.
            row[etree.QName(child.tag).localname] = child.text
        # Append the row dict to the rows array.
        rows.append(row)
    # Create the DateFrame. This would probably be better in a try/catch to handle errors.
    df = pd.DataFrame(rows, columns=columns)
    # Calculate time difference.
    end = datetime.datetime.now()
    diff = end - start
    return df, diff


print(fnc_parse_xml("input.xml",
                    ["rxOctets", "txOctets", "inSpeed", "outSpeed", "time", "seconds", "port", "ip", "name"]))

在我的计算机上，此文件将在4秒钟内处理92.5MB的文件.

On my machine this processes a 92.5MB file in about 4 seconds.

这篇关于提高将具有元素和名称空间的XML解析为Pandas的速度的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！