如何将XML文件转换为漂亮的pandas数据帧？

时间：2015-02-01 03:58:54

标签： python xml python-2.7 parsing pandas

我们假设我有这样的XML：

<author type="XXX" language="EN" gender="xx" feature="xx" web="foobar.com">
    <documents count="N">
        <document KEY="e95a9a6c790ecb95e46cf15bee517651" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="bc360cfbafc39970587547215162f0db" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="19e71144c50a8b9160b3f0955e906fce" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="21d4af9021a174f61b884606c74d9e42" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="28a45eb2460899763d709ca00ddbb665" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="a0c0712a6a351f85d9f5757e9fff8946" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="626726ba8d34d15d02b6d043c55fe691" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="2cb473e0f102e2e4a40aa3006e412ae4" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...] [...]
]]>
        </document>
    </documents>
</author>

我想阅读这个XML文件并将其转换为pandas DataFrame：

key                                         type     language    feature            web                         data
e95324a9a6c790ecb95e46cf15bE232ee517651      XXX        EN          xx      www.foo_bar_exmaple.com     A large text with lots of strings and punctuations symbols [...]
e95324a9a6c790ecb95e46cf15bE232ee517651      XXX        EN          xx      www.foo_bar_exmaple.com     A large text with lots of strings and punctuations symbols [...]
19e71144c50a8b9160b3cvdf2324f0955e906fce     XXX        EN          xx      www.foo_bar_exmaple.com     A large text with lots of strings and punctuations symbols [...]
21d4af9021a174f61b8erf284606c74d9e42         XXX        EN          xx      www.foo_bar_exmaple.com     A large text with lots of strings and punctuations symbols [...]
28a45eb2460823499763d70vdf9ca00ddbb665       XXX        EN          xx      www.foo_bar_exmaple.com     A large text with lots of strings and punctuations symbols [...]

这是我已经尝试过的，但是我遇到了一些错误，可能还有更有效的方法来完成这项任务：

from lxml import objectify
import pandas as pd

path = 'file_path'
xml = objectify.parse(open(path))
root = xml.getroot()
root.getchildren()[0].getchildren()
df = pd.DataFrame(columns=('key','type', 'language', 'feature', 'web', 'data'))

for i in range(0,len(xml)):
    obj = root.getchildren()[i].getchildren()
    row = dict(zip(['key','type', 'language', 'feature', 'web', 'data'], [obj[0].text, obj[1].text]))
    row_s = pd.Series(row)
    row_s.name = i
    df = df.append(row_s)

有人能为我提供更好的解决方案吗？

6 个答案:

答案 0 :(得分：36)

您可以轻松使用xml（来自Python标准库）转换为pandas.DataFrame。这就是我要做的事情（当从文件中读取xml_data替换文件或文件对象的名称时）：

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

def iter_docs(author):
    author_attr = author.attrib
    for doc in author.iter('document'):
        doc_dict = author_attr.copy()
        doc_dict.update(doc.attrib)
        doc_dict['data'] = doc.text
        yield doc_dict

xml_data = io.StringIO(u'''\
<author type="XXX" language="EN" gender="xx" feature="xx" web="foobar.com">
    <documents count="N">
        <document KEY="e95a9a6c790ecb95e46cf15bee517651" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="bc360cfbafc39970587547215162f0db" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="19e71144c50a8b9160b3f0955e906fce" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="21d4af9021a174f61b884606c74d9e42" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="28a45eb2460899763d709ca00ddbb665" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="a0c0712a6a351f85d9f5757e9fff8946" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="626726ba8d34d15d02b6d043c55fe691" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="2cb473e0f102e2e4a40aa3006e412ae4" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...] [...]
]]>
        </document>
    </documents>
</author>
''')

etree = ET.parse(xml_data) #create an ElementTree object 
doc_df = pd.DataFrame(list(iter_docs(etree.getroot())))

如果原始文档中有多位作者或者XML的根不是author，那么我会添加以下生成器：

def iter_author(etree):
    for author in etree.iter('author'):
        for row in iter_docs(author):
            yield row

并将doc_df = pd.DataFrame(list(iter_docs(etree.getroot())))更改为doc_df = pd.DataFrame(list(iter_author(etree)))

查看ElementTree库tutorial中提供的xml documentation。

答案 1 :(得分：7)

这是将xml转换为pandas数据帧的另一种方法。例如，我从字符串解析xml，但这个逻辑也适用于读取文件。

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

xml_str = '<?xml version="1.0" encoding="utf-8"?>\n<response>\n <head>\n  <code>\n   200\n  </code>\n </head>\n <body>\n  <data id="0" name="All Categories" t="2018052600" tg="1" type="category"/>\n  <data id="13" name="RealEstate.com.au [H]" t="2018052600" tg="1" type="publication"/>\n </body>\n</response>'

etree = ET.fromstring(xml_str)
dfcols = ['id', 'name']
df = pd.DataFrame(columns=dfcols)

for i in etree.iter(tag='data'):
    df = df.append(
        pd.Series([i.get('id'), i.get('name')], index=dfcols),
        ignore_index=True)

df.head()

答案 2 :(得分：6)

从 v1.3 开始，您可以简单地使用：

pandas.read_xml(path_or_file)

您可以通过以下方式安装最新的 Pandas 开发版：

pip install git+https://github.com/pandas-dev/pandas

答案 3 :(得分：1)

请推荐使用xmltodict库。它很好地处理了您的xml文本，并且我已将其用于提取具有近百万条记录的xml文件。 xmltodict handling xml load

答案 4 :(得分：0)

您还可以通过创建元素字典来进行转换，然后直接转换为数据框：

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

# Contents of test.xml
# <?xml version="1.0" encoding="utf-8"?> <tags>   <row Id="1" TagName="bayesian" Count="4699" ExcerptPostId="20258" WikiPostId="20257" />   <row Id="2" TagName="prior" Count="598" ExcerptPostId="62158" WikiPostId="62157" />   <row Id="3" TagName="elicitation" Count="10" />   <row Id="5" TagName="open-source" Count="16" /> </tags>

root = ET.parse('test.xml').getroot()

tags = {"tags":[]}
for elem in root:
    tag = {}
    tag["Id"] = elem.attrib['Id']
    tag["TagName"] = elem.attrib['TagName']
    tag["Count"] = elem.attrib['Count']
    tags["tags"]. append(tag)

df_users = pd.DataFrame(tags["tags"])
df_users.head()

答案 5 :(得分：-14)

如果文件大小<1,00,000行，您可以将其另存为MS Excel中的Excel或Csv文件，并通过以下任意一种轻松阅读

[let express = require('express'); let app = express(); // For POST-Support let bodyParser = require('body-parser'); let multer = require('multer'); let upload = multer(); app.use(bodyParser.json()); app.use(bodyParser.urlencoded({ extended: true })); app.post('/api/sayHello', upload.array(), (request, response) => { let a = request.body.a; let b = request.body.b; let c = parseInt(a) + parseInt(b); response.send('Result : '+c); console.log('Result : '+c); }); app.listen(3000); Please see the below example in the link https://stackoverflow.com/questions/41955103/cant-get-post-data-using-nodejs-expressjs-and-postman/53514520#53514520 This will help you][1]

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