我很受伤，没有人建议熊猫。Pandas有一个read_xml()函数，它非常适合这种扁平的xml结构。

import pandas as pd

xml = """<foo>
   <bar>
      <type foobar="1"/>
      <type foobar="2"/>
   </bar>
</foo>"""

df = pd.read_xml(xml, xpath=".//type")
print(df)

输出:

   foobar
0       1
1       2

#If the xml is in the form of a string as shown below then
from lxml  import etree, objectify
'''sample xml as a string with a name space {http://xmlns.abc.com}'''
message =b'<?xml version="1.0" encoding="UTF-8"?>\r\n<pa:Process xmlns:pa="http://xmlns.abc.com">\r\n\t<pa:firsttag>SAMPLE</pa:firsttag></pa:Process>\r\n'  # this is a sample xml which is a string


print('************message coversion and parsing starts*************')

message=message.decode('utf-8') 
message=message.replace('<?xml version="1.0" encoding="UTF-8"?>\r\n','') #replace is used to remove unwanted strings from the 'message'
message=message.replace('pa:Process>\r\n','pa:Process>')
print (message)

print ('******Parsing starts*************')
parser = etree.XMLParser(remove_blank_text=True) #the name space is removed here
root = etree.fromstring(message, parser) #parsing of xml happens here
print ('******Parsing completed************')


dict={}
for child in root: # parsed xml is iterated using a for loop and values are stored in a dictionary
    print(child.tag,child.text)
    print('****Derving from xml tree*****')
    if child.tag =="{http://xmlns.abc.com}firsttag":
        dict["FIRST_TAG"]=child.text
        print(dict)


### output
'''************message coversion and parsing starts*************
<pa:Process xmlns:pa="http://xmlns.abc.com">

    <pa:firsttag>SAMPLE</pa:firsttag></pa:Process>
******Parsing starts*************
******Parsing completed************
{http://xmlns.abc.com}firsttag SAMPLE
****Derving from xml tree*****
{'FIRST_TAG': 'SAMPLE'}'''

如果你使用python-benedict，就不需要使用lib特定的API。只需从XML初始化一个新实例并轻松管理它，因为它是dict子类。

安装很简单:pip install python-benedict

from benedict import benedict as bdict

# data-source can be an url, a filepath or data-string (as in this example)
data_source = """
<foo>
   <bar>
      <type foobar="1"/>
      <type foobar="2"/>
   </bar>
</foo>"""

data = bdict.from_xml(data_source)
t_list = data['foo.bar'] # yes, keypath supported
for t in t_list:
   print(t['@foobar'])

它支持并规范化多种格式的I/O操作:Base64, CSV, JSON, TOML, XML, YAML和查询字符串。

它在GitHub上经过了很好的测试和开源。披露:我是作者。

你可以使用BeautifulSoup:

from bs4 import BeautifulSoup

x="""<foo>
   <bar>
      <type foobar="1"/>
      <type foobar="2"/>
   </bar>
</foo>"""

y=BeautifulSoup(x)
>>> y.foo.bar.type["foobar"]
u'1'

>>> y.foo.bar.findAll("type")
[<type foobar="1"></type>, <type foobar="2"></type>]

>>> y.foo.bar.findAll("type")[0]["foobar"]
u'1'
>>> y.foo.bar.findAll("type")[1]["foobar"]
u'2'

Minidom是最快速且非常直接的方法。

XML:

<data>
    <items>
        <item name="item1"></item>
        <item name="item2"></item>
        <item name="item3"></item>
        <item name="item4"></item>
    </items>
</data>

Python:

from xml.dom import minidom

dom = minidom.parse('items.xml')
elements = dom.getElementsByTagName('item')

print(f"There are {len(elements)} items:")

for element in elements:
    print(element.attributes['name'].value)

输出:

There are 4 items:
item1
item2
item3
item4

我很受伤，没有人建议熊猫。Pandas有一个read_xml()函数，它非常适合这种扁平的xml结构。

import pandas as pd

xml = """<foo>
   <bar>
      <type foobar="1"/>
      <type foobar="2"/>
   </bar>
</foo>"""

df = pd.read_xml(xml, xpath=".//type")
print(df)

输出:

   foobar
0       1
1       2