我正在處理一個 XML 數(shù)據(jù)文件，其中包含足球比賽期間球員的跟蹤數(shù)據(jù)。查看 XML 數(shù)據(jù)文件頂部的片段：<?xml version="1.0" encoding="utf-8"?><Tracking update="2017-01-23T14:41:26"> <Match id="2019285" dateMatch="2016-09-13T18:45:00" matchNumber="13"> <Competition id="20159" name="UEFA Champions League 2016/2017" /> <Stadium id="85265" name="Estádio do SL Benfica" pitchLength="10500" pitchWidth="6800" /> <Phases> <Phase start="2016-09-13T18:45:35.245" end="2016-09-13T19:31:49.09" leftTeamID="50157" /> <Phase start="2016-09-13T19:47:39.336" end="2016-09-13T20:37:10.591" leftTeamID="50147" /> </Phases> <Frames> <Frame utc="2016-09-13T18:45:35.272" isBallInPlay="0"> <Objs> <Obj type="7" id="0" x="-46" y="-2562" z="0" sampling="0" /> <Obj type="0" id="105823" x="939" y="113" sampling="0" /> <Obj type="0" id="250086090" x="1194" y="1425" sampling="0" /> <Obj type="0" id="250080473" x="37" y="2875" sampling="0" /> <Obj type="0" id="250054760" x="329" y="833" sampling="0" /> <Obj type="1" id="98593" x="-978" y="654" sampling="0" /> <Obj type="0" id="250075765" x="1724" y="392" sampling="0" /> <Obj type="1" id="53733" x="-4702" y="45" sampling="0" /> <Obj type="0" id="250101112" x="54" y="1436" sampling="0" /> <Obj type="1" id="250017920" x="-46" y="-2562" sampling="0" /> <Obj type="1" id="105588" x="-1449" y="209" sampling="0" /> <Obj type="1" id="250003757" x="-2395" y="-308" sampling="0" /> <Obj type="1" id="101473" x="-690" y="-644" sampling="0" /> <Obj type="0" id="250075775" x="2069" y="-895" sampling="0" /> <Obj type="1" id="103695" x="-1654" y="-2022" sampling="0" /> </Objs> </Frame> </Frames> </Match></Tracking>
1 回答
呼喚遠(yuǎn)方
TA貢獻(xiàn)1856條經(jīng)驗 獲得超11個贊
我使用xml etree模塊來遍歷 xml 并提取相關(guān)數(shù)據(jù)。注釋在下面的代碼中以解釋該過程：看看它，然后玩代碼。希望它適合您的用例
import xml.etree.ElementTree as ET
from collections import defaultdict
d = defaultdict(list)
#since u r reading from a file,
# root should be root = ET.parse('filename.xml').getroot()
#mine is wrapped in a string hence :
root = ET.fromstring(data)
#required data is in the Frame section
for ent in root.findall('./Match//Frame'):
#this gets us the timestamp
Frame = ent.attrib['utc']
for entry in ent.findall('Objs/Obj'):
#append the objects to the relevant timestamp
d[Frame].append(entry.attrib)
df = (pd.concat((pd.DataFrame(value) #create dataframe of the values
.assign(Frame=key) #assign keys to the dataframe
.filter(['id','Frame','x','y','z']) #keep only required columns
for key, value in d.items()),
axis=1) #concatenate on the columns axis
)
df.head()
id Frame x y z id Frame x y z
0 0 2016-09-13T18:45:35.272 -46 -2562 0 0 2016-09-13T18:45:35.319 -46 -2558 0
1 105823 2016-09-13T18:45:35.272 939 113 NaN 105823 2016-09-13T18:45:35.319 938 113 NaN
2 250086090 2016-09-13T18:45:35.272 1194 1425 NaN 250086090 2016-09-13T18:45:35.319 1198 1426 NaN
3 250080473 2016-09-13T18:45:35.272 37 2875 NaN 250080473 2016-09-13T18:45:35.319 36 2874 NaN
4 250054760 2016-09-13T18:45:35.272 329 833 NaN 250054760 2016-09-13T18:45:35.319 330 833 NaN
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