对.txt文件Python的XML解析
我需要对此进行分析，并将日期和时间移动到对.txt文件Python的XML解析,python,xml,parsing,xml-parsing,Python,Xml,Parsing,Xml Parsing,我需要对此进行分析，并将日期和时间移动到%Y-%m-%d%H:%m:%S格式，以及变量每小时qpf和降水概率到以制表符分隔的.txt文件中的列中 我所能做的就是使用以下代码读取XML文件： page = urllib2.urlopen('http://forecast.weather.gov/MapClick.php?lat=47.6062&lon=-122.3321&FcstType=digitalDWML') page_content = page.read() with o
%Y-%m-%d%H:%m:%S每小时qpf降水概率page = urllib2.urlopen('http://forecast.weather.gov/MapClick.php?lat=47.6062&lon=-122.3321&FcstType=digitalDWML')
page_content = page.read()
with open('KBFI.xml', 'w') as fid:
fid.write(page_content)
data = []
import xml.etree.ElementTree as ET
tree = ET.parse('KBFI.xml')
root = tree.getroot()
for data in root.findall('data'):
for time-layout in root.findall('time-layout'):
start-valid-time = time-layout.find('start-valid-time')
time = datetime.datetime.strptime(start-valid-time, '%Y-%m-%dT%H:%M:%S')
for parameters in root.findall('parameters'):
for probability-of-precipitation in root.findall('probability-of-precipitation'):
value = probability-of-precipitation.find('value')
for hourly-qpf in root.findall('hourly-qpf'):
value2 = hourly-qpf.find('value')
data = data.append([time,
value,
value2])
with open('KBFI.txt','w') as file:
file.writelines('\t'.join(map(str,i)) + '\n' for i in data)
但是，有一个问题，因为变量是连字符的，我不知道如何将它们更改为下划线或删除它们。而且，正因为如此，我不知道我的代码是否好 您可以使用python xml库：
您可以使用python xml库：
您可以使用python xml库：
您可以使用python xml库：
我建议使用xmltodict来解析和提取XML中的数据，因为它简单易用，因为它使用与XML源相同的嵌套方式将XML转换为Python DICT。对于那些熟悉Python语法的人来说，使用它是很自然的，Python dict是完全通用的，这意味着它们能够表达异构和嵌套的数据结构。例如，依赖Python DITCS用于Python、C++和java数据互操作性，并提供将XML转换为DIX的工具。XMLtoDIT的优点在于它的小型、快速和独立的模块，只需将XML转换为DI.T/P> 作为xmltodict使用的示例，以下脚本下载并提取其创建日期以及降水概率和每小时qpf值列表：
import requests
url='http://forecast.weather.gov/MapClick.php?lat=47.6062&lon=-122.3321&FcstType=digitalDWML'
r = requests.get(url)
import xmltodict
result = xmltodict.parse(r.text)
cd = result['dwml']['head']['product']['creation-date']['#text']
print("creation-date =",cd)
pop = result['dwml']['data']['parameters']['probability-of-precipitation']['value']
print("\nprobability-of-precipitation =", pop)
hqpf = result['dwml']['data']['parameters']['hourly-qpf']['value']
print("\nhourly-qpf =", hqpf)
{'dwml': {'@version': '1.0',
'@xmlns:xsd': 'http://www.w3.org/2001/XMLSchema',
'@xmlns:xsi': 'http://www.w3.org/2001/XMLSchema-instance',
'@xsi:noNamespaceSchemaLocation': 'http://graphical.weather.gov/xml/DWMLgen/schema/DWML.xsd',
'head': {'product': {'@concise-name': 'tabular-digital',
'@operational-mode': 'developmental',
'@srsName': 'WGS 1984',
'creation-date': {'@refresh-frequency': 'PT1H',
'#text': '2015-07-31T14:20:30-07:00'}},
'source': {'production-center': 'Seattle, WA',
'credit': 'http://www.wrh.noaa.gov/sew',
'more-information': 'http://www.nws.noaa.gov/forecasts/xml/'}},
'data': {'location': {'location-key': 'point1',
'description': 'Downtown Seattle WA, WA',
'point': {'@latitude': '47.61',
'@longitude': '-122.32'},
'city': {'@state': 'WA',
'#text': 'Downtown Seattle WA'},
'height': {'@datum': 'mean sea level',
'#text': '240'}},
'moreWeatherInformation': {'@applicable-location': 'point1',
'#text': 'http://forecast.weather.gov/MapClick.php?lat=47.61&lon=-122.32&FcstType=digital'},
'time-layout': {'@time-coordinate': 'local',
'@summarization': 'none',
'layout-key': 'k-p1h-n1-0',
'start-valid-time': ['2015-07-31T16:00:00-07:00',
'2015-07-31T17:00:00-07:00',
'2015-07-31T18:00:00-07:00',
...
import xmltodict
result = xmltodict.parse(r.text)
cd = result['dwml']['head']['product']['creation-date']['#text']
print("creation-date =",cd)
svt = result['dwml']['data']['time-layout']['start-valid-time']
print("\nstart-valid-time =", svt)
print("number of start-valid-time entries =", len(svt))
evt = result['dwml']['data']['time-layout']['end-valid-time']
print("\nend-valid-time =", evt)
print("number of end-valid-time entries =", len(evt))
pop = result['dwml']['data']['parameters']['probability-of-precipitation']['value']
print("\nprobability-of-precipitation =", pop)
print("number of probability-of-precipitation entries =", len(pop))
hqpf = result['dwml']['data']['parameters']['hourly-qpf']['value']
print("\nhourly-qpf =", hqpf)
print("number of hourly-qpf entries =", len(hqpf))
我建议使用xmltodict来解析和提取XML中的数据，因为它简单易用，因为它使用与XML源相同的嵌套方式将XML转换为Python DICT。对于那些熟悉Python语法的人来说，使用它是很自然的，Python dict是完全通用的，这意味着它们能够表达异构和嵌套的数据结构。例如，依赖Python DITCS用于Python、C++和java数据互操作性，并提供将XML转换为DIX的工具。XMLtoDIT的优点在于它的小型、快速和独立的模块，只需将XML转换为DI.T/P> 作为xmltodict使用的示例，以下脚本下载并提取其创建日期以及降水概率和每小时qpf值列表：
import requests
url='http://forecast.weather.gov/MapClick.php?lat=47.6062&lon=-122.3321&FcstType=digitalDWML'
r = requests.get(url)
import xmltodict
result = xmltodict.parse(r.text)
cd = result['dwml']['head']['product']['creation-date']['#text']
print("creation-date =",cd)
pop = result['dwml']['data']['parameters']['probability-of-precipitation']['value']
print("\nprobability-of-precipitation =", pop)
hqpf = result['dwml']['data']['parameters']['hourly-qpf']['value']
print("\nhourly-qpf =", hqpf)
{'dwml': {'@version': '1.0',
'@xmlns:xsd': 'http://www.w3.org/2001/XMLSchema',
'@xmlns:xsi': 'http://www.w3.org/2001/XMLSchema-instance',
'@xsi:noNamespaceSchemaLocation': 'http://graphical.weather.gov/xml/DWMLgen/schema/DWML.xsd',
'head': {'product': {'@concise-name': 'tabular-digital',
'@operational-mode': 'developmental',
'@srsName': 'WGS 1984',
'creation-date': {'@refresh-frequency': 'PT1H',
'#text': '2015-07-31T14:20:30-07:00'}},
'source': {'production-center': 'Seattle, WA',
'credit': 'http://www.wrh.noaa.gov/sew',
'more-information': 'http://www.nws.noaa.gov/forecasts/xml/'}},
'data': {'location': {'location-key': 'point1',
'description': 'Downtown Seattle WA, WA',
'point': {'@latitude': '47.61',
'@longitude': '-122.32'},
'city': {'@state': 'WA',
'#text': 'Downtown Seattle WA'},
'height': {'@datum': 'mean sea level',
'#text': '240'}},
'moreWeatherInformation': {'@applicable-location': 'point1',
'#text': 'http://forecast.weather.gov/MapClick.php?lat=47.61&lon=-122.32&FcstType=digital'},
'time-layout': {'@time-coordinate': 'local',
'@summarization': 'none',
'layout-key': 'k-p1h-n1-0',
'start-valid-time': ['2015-07-31T16:00:00-07:00',
'2015-07-31T17:00:00-07:00',
'2015-07-31T18:00:00-07:00',
...
import xmltodict
result = xmltodict.parse(r.text)
cd = result['dwml']['head']['product']['creation-date']['#text']
print("creation-date =",cd)
svt = result['dwml']['data']['time-layout']['start-valid-time']
print("\nstart-valid-time =", svt)
print("number of start-valid-time entries =", len(svt))
evt = result['dwml']['data']['time-layout']['end-valid-time']
print("\nend-valid-time =", evt)
print("number of end-valid-time entries =", len(evt))
pop = result['dwml']['data']['parameters']['probability-of-precipitation']['value']
print("\nprobability-of-precipitation =", pop)
print("number of probability-of-precipitation entries =", len(pop))
hqpf = result['dwml']['data']['parameters']['hourly-qpf']['value']
print("\nhourly-qpf =", hqpf)
print("number of hourly-qpf entries =", len(hqpf))
我建议使用xmltodict来解析和提取XML中的数据，因为它简单易用，因为它使用与XML源相同的嵌套方式将XML转换为Python DICT。对于那些熟悉Python语法的人来说，使用它是很自然的，Python dict是完全通用的，这意味着它们能够表达异构和嵌套的数据结构。例如，依赖Python DITCS用于Python、C++和java数据互操作性，并提供将XML转换为DIX的工具。XMLtoDIT的优点在于它的小型、快速和独立的模块，只需将XML转换为DI.T/P> 作为xmltodict使用的一个示例，以下脚本下载并提取其创建内容