我有大量的 xml 數據，看起來像這樣（只顯示了一小部分數據）：<weatherdata xmlns:xsi="http://www.website.com" xsi:noNamespaceSchemaLocation="www.website.com" created="2020-07-06T14:53:48Z"> <meta> <model name="xxxxxx" termin="2020-07-06T06:00:00Z" runended="2020-07-06T09:48:31Z" nextrun="2020-07-06T16:00:00Z" from="2020-07-06T15:00:00Z" to="2020-07-08T12:00:00Z"/> <model name="xxxxxx" termin="2020-07-06T00:00:00Z" runended="2020-07-06T09:48:31Z" nextrun="2020-07-06T18:00:00Z" from="2020-07-08T13:00:00Z" to="2020-07-09T18:00:00Z"/> <model name="xxxxxx" termin="2020-07-06T00:00:00Z" runended="2020-07-06T09:48:31Z" nextrun="2020-07-06T18:00:00Z" from="2020-07-09T21:00:00Z" to="2020-07-12T00:00:00Z"/> <model name="xxxxxx" termin="2020-07-06T00:00:00Z" runended="2020-07-06T09:48:31Z" nextrun="2020-07-06T18:00:00Z" from="2020-07-12T06:00:00Z" to="2020-07-16T00:00:00Z"/> </meta> <product class="pointData"> <time datatype="forecast" from="2020-07-06T15:00:00Z" to="2020-07-06T15:00:00Z"> <location altitude="10" latitude="123" longitude="123"> <temperature id="TTT" unit="celsius" value="18.8"/> <windDirection id="dd" deg="296.5" name="NW"/> <windSpeed id="ff" mps="5.8" beaufort="4" name="Laber bris"/> <globalRadiation value="524.2" unit="W/m^2"/> <humidity value="59.0" unit="percent"/> <pressure id="pr" unit="hPa" value="1022.9"/> <cloudiness id="NN" percent="22.7"/> <lowClouds id="LOW" percent="22.7"/> <mediumClouds id="MEDIUM" percent="0.0"/> <highClouds id="HIGH" percent="0.0"/> <dewpointTemperature id="TD" unit="celsius" value="10.6"/> </location> </time> <time datatype="forecast" from="2020-07-06T14:00:00Z" to="2020-07-06T15:00:00Z"> <location altitude="10" latitude="123" longitude="123"> <precipitation unit="mm" value="0.0" minvalue="0.0" maxvalue="0.0" probability="2.0"/> <symbol id="LightCloud" number="2"/> </location>
1 回答
蠱毒傳說
TA貢獻1895條經驗 獲得超3個贊
考慮分別構建temperature數據框和precipitation數據框，concat然后通過節點merge中的公共值將版本連接在一起。并考慮使用列表/字典理解將所有屬性值綁定在一起。timelocation
import xml.etree.ElementTree as et
import pandas as pd
tree = et.parse('Input.xml') # load in the data
root = tree.getroot() # get the element tree root
temp_list = []; precip_list = []
for n, x in enumerate(root.iter('time')):
# GET LIST OF DICTIONARIES OF ALL ATTRIBUTES
x_list = [{i.tag+'_'+k:v for k,v in i.attrib.items()} for i in x.iter('*')]
# COMBINE INTO SINGLE DICTIONARY
x_dict = {k:v for d in x_list for k,v in d.items()}
# BUILD DATA FRAME
df = pd.DataFrame(x_dict, index=[0])
# SEPARATELY SAVE TO LIST OF DATA FRAMES
if 'temperature_unit' in df.columns: temp_list.append(df)
if 'precipitation_unit' in df.columns: precip_list.append(df)
# MERGE CONCATENATED SETS BY COMMON VARS
df = pd.merge(pd.concat(temp_list),
pd.concat(precip_list),
on=['time_to', 'time_datatype',
'location_altitude', 'location_latitude',
'location_longitude'],
suffixes=['_t','_p'])
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