问如何在python中将xml文件转换为数据框或csv输出
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Stack Overflow用户

提问于 2018-08-08 23:43:55

回答 3查看 1.9K关注 0票数 5

我有这个xml文件，它想要将内容转换为python中csv文件的数据帧：

<?xml version="1.0" encoding="utf-8"?>
<dashboardreport name="jvm_report" version="7.0.21.1017" reportdate="2018-08-08T10:37:01.510-04:00" description="">
  <source name="CORP_GTM">
    <filters summary="from Jul-30 23:40 to Jul-31 02:40">
      <filter>tf:CustomTimeframe?1533008450802:1533019250802</filter>
    </filters>
  </source>
  <reportheader>
    <reportdetails>
      <user>test1</user>
    </reportdetails>
  </reportheader>
  <data>
    <chartdashlet name="jvm_mem_percent" description="" showabsolutevalues="false">
      <measures structuretype="tree">
        <measure measure="Memory Utilization - Memory Utilization (split by Agent)" color="#800080" aggregation="Maximum" unit="%" thresholds="false" drawingorder="1">
          <measure measure="Memory Utilization - test@server1" color="#7aebd0" aggregation="Maximum" unit="%" thresholds="false">
            <measurement timestamp="1533008460000" avg="11.116939544677734" min="11.007165908813477" max="11.143875122070312" sum="66.7016372680664" count="6"></measurement>
            <measurement timestamp="1533008520000" avg="11.204706827799479" min="11.144883155822754" max="11.268420219421387" sum="67.22824096679688" count="6"></measurement>
          </measure>
          <measure measure="Memory Utilization - test@server2" color="#a6f2e0" aggregation="Maximum" unit="%" thresholds="false">
            <measurement timestamp="1533008460000" avg="11.900418599446615" min="10.386141777038574" max="13.744248390197754" sum="71.40251159667969" count="6"></measurement>
            <measurement timestamp="1533008520000" avg="11.139397939046225" min="10.617960929870605" max="11.427289009094238" sum="66.83638763427734" count="6"></measurement>
          </measure>
          <measure measure="Memory Utilization - test@server3" color="#dd2271" aggregation="Maximum" unit="%" thresholds="false">
            <measurement timestamp="1533008460000" avg="8.395787556966146" min="8.340044021606445" max="8.429450035095215" sum="50.374725341796875" count="6"></measurement>
            <measurement timestamp="1533008520000" avg="8.490419387817383" min="8.456218719482422" max="8.5205659866333" sum="50.9425163269043" count="6"></measurement>
           </measure>
            </measure>
      </measures>
    </chartdashlet>
    <chartdashlet name="jvm_trans_errors" description="" showabsolutevalues="false">
      <measures structuretype="tree"></measures>
    </chartdashlet>
    <chartdashlet name="jvm_trans" description="" showabsolutevalues="false">
      <measures structuretype="tree">
        <measure measure="Count Backend - Count Backend (split by Agent)" color="#8080c0" aggregation="Sum" unit="num" thresholds="false" drawingorder="1">
          <measure measure="Count Backend - test@server1" color="#e44e8d" aggregation="Sum" unit="num" thresholds="false">
            <measurement timestamp="1533010380000" avg="1.0" min="1.0" max="1.0" sum="1.0" count="1"></measurement>
            <measurement timestamp="1533011340000" avg="1.0" min="1.0" max="1.0" sum="10.0" count="10"></measurement>
            <measurement timestamp="1533013080000" avg="1.0" min="1.0" max="1.0" sum="1.0" count="1"></measurement>
            <measurement timestamp="1533013200000" avg="1.0" min="1.0" max="1.0" sum="1.0" count="1"></measurement>
            <measurement timestamp="1533014940000" avg="1.0" min="1.0" max="1.0" sum="2.0" count="2"></measurement>
            <measurement timestamp="1533015780000" avg="1.0" min="1.0" max="1.0" sum="1.0" count="1"></measurement>
            <measurement timestamp="1533018480000" avg="1.0" min="1.0" max="1.0" sum="1.0" count="1"></measurement>
            <measurement timestamp="1533018540000" avg="1.0" min="1.0" max="1.0" sum="2.0" count="2"></measurement>
          </measure>
          <measure measure="Count Backend - test@server2" color="#e5cf4d" aggregation="Sum" unit="num" thresholds="false">
            <measurement timestamp="1533009060000" avg="1.0" min="1.0" max="1.0" sum="10.0" count="10"></measurement>
            <measurement timestamp="1533009120000" avg="1.0" min="1.0" max="1.0" sum="1.0" count="1"></measurement>
            <measurement timestamp="1533009420000" avg="1.0" min="1.0" max="1.0" sum="3.0" count="3"></measurement>
            <measurement timestamp="1533009480000" avg="1.0" min="1.0" max="1.0" sum="5.0" count="5"></measurement>
            <measurement timestamp="1533010020000" avg="1.0" min="1.0" max="1.0" sum="4.0" count="4"></measurement>
            <measurement timestamp="1533010320000" avg="1.0" min="1.0" max="1.0" sum="1200.0" count="1200"></measurement>
          </measure>
          <measure measure="Count Backend - test@server3" color="#dec321" aggregation="Sum" unit="num" thresholds="false">
            <measurement timestamp="1533008460000" avg="1.0" min="1.0" max="1.0" sum="4.0" count="4"></measurement>
            <measurement timestamp="1533008520000" avg="1.0" min="1.0" max="1.0" sum="5.0" count="5"></measurement>
            <measurement timestamp="1533008580000" avg="1.0" min="1.0" max="1.0" sum="9.0" count="9"></measurement>
            <measurement timestamp="1533008640000" avg="1.0" min="1.0" max="1.0" sum="5.0" count="5"></measurement>
          </measure>       
          </measure>
        </measures>
    </chartdashlet>
  </data>
</dashboardreport>

输出需要如下所示：

timestamp    max           count    node
1.53301E+12 11.14387512 6   Memory Utilization - test@server1
1.53301E+12 11.26842022 6   Memory Utilization - test@server1
1.53301E+12 13.74424839 6   Memory Utilization - test@server2
1.53301E+12 11.42728901 6   Memory Utilization - test@server2
1.53301E+12 8.429450035 6   Memory Utilization - test@server3
1.53301E+12 8.520565987 6   Memory Utilization - test@server3
1.53301E+12 1   1   Count Backend - test@server1
1.53301E+12 1   10  Count Backend - test@server1
1.53301E+12 1   1   Count Backend - test@server1
1.53301E+12 1   1   Count Backend - test@server1

我可以在R中这样做：

doc <- read_xml("C:/test1/test.xml")
  dat<-xml_find_all(doc, ".//measure/measure") %>%
    map_df(function(x) {
      xml_find_all(x, ".//measurement") %>%
        map_df(~as.list(xml_attrs(.))) %>%
        select(-min, -avg, -sum) %>%
        mutate(node=xml_attr(x, "measure"))
    })

我需要用python来做这件事，有什么想法吗？

python

回答 3

Stack Overflow用户

发布于 2018-08-11 02:14:37

您应该在Python语言中使用内置库xml。

现在，您的标记和属性不是标准的，所以我必须创建一个函数，该函数可能是针对您的问题而硬编码的，但其他人可以将其用作指南。

考虑到这种标记是您拥有的唯一数据源，并从父标记获取其node属性：

<measurement timestamp="1533008520000" avg="8.490419387817383" min="8.456218719482422" max="8.5205659866333" sum="50.9425163269043" count="6"></measurement>

以下函数应该有效，使用Pandas创建数据帧并将其导出到.csv文件：

from xml.dom import minidom
import pandas as pd

def convert():
    filename = 'teststack.xml'
    document = minidom.parse(filename)
    items = document.getElementsByTagName('measurement')

    df = pd.DataFrame(columns=["timestamp", "max", "count", "node"])
    for i, item in enumerate(items):
        # Creating new line for every item
        df.loc[i] = [
            item.getAttribute('timestamp'),
            item.getAttribute('max'),
            item.getAttribute('count'),
            item.parentNode.getAttribute('measure')
        ]

    # Exporting file
    df.to_csv("export.csv")
    return df

只需将文件名更改为您的.xml文件，它应该可以工作。一旦你有了数据框架，你就可以随心所欲地修改数据的精度、近似值和其他特征。

票数 0

Stack Overflow用户

发布于 2018-08-11 02:19:21

这是一个仅使用包含库和Python 3.6的解决方案-不需要pandas

CSV：

import csv
import xml.etree.ElementTree

e = xml.etree.ElementTree.parse('data.xml').getroot()

with open('out.csv', 'w', newline='') as csv_file:
    csv_writer = csv.writer(csv_file)
    for data in e.iter('measures'):
        measures = data.findall('measure/measure')
        for measure in measures:
            for row in measure:
                csv_writer.writerow([row.get('timestamp'), row.get('max'), row.get('count'), measure.get('measure')])

列：

import xml.etree.ElementTree

e = xml.etree.ElementTree.parse('data.xml').getroot()

row_data = [['timestamp', 'max', 'count', 'node']]
widths = [len(i) for i in row_data[0]]

for data in e.iter('measures'):
    measures = data.findall('measure/measure')
    for measure in measures:
        for row in measure:
            row_list = [row.get('timestamp'), row.get('max'), row.get('count'), measure.get('measure')]
            row_data.append(row_list)

            for i, val in enumerate(row_list):
                if len(val) > widths[i]:
                    widths[i] = len(val)

with open('out.txt', 'w') as txt_writer:
    for row in row_data:
        txt_writer.write(' '.join([f"{row[i]: <{widths[i]}}" for i in range(4)]) + '\n')

票数 0

Stack Overflow用户

发布于 2018-08-17 20:40:27

import pandas as pd
import xml.etree.ElementTree as ET

def getMetrics(file_name):
    tree = ET.parse(file_name)
    root = tree.getroot()
    result = []
    for measure in root.iter('measure'):                         #Get all 'measure' tag
        node = measure.attrib["measure"].split("-")[0].strip()    #Get Node
        for measurement in measure:                              #Get Metrics Information
            if "timestamp" in measurement.attrib:
                result.append(dict(node=node, timestamp=measurement.attrib.get("timestamp"), max=measurement.attrib["max"], count=measurement.attrib["count"]))
    return result

df = pd.DataFrame(getMetrics(filename), columns=["timestamp", "max", "count", "node"])          #Form Dataframe
print(df)

df.to_csv("Your_Output.csv")     #Write to CSV.

票数 0

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原文链接：

https://stackoverflow.com/questions/51750726

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