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1b53ab9d52f6dff1d1c6c4320b5b9a12dc423141
AIclinicalresearch/docs/03-业务模块/DC-数据清洗整理/02-技术设计/工具 C：AI 辅助医疗数据清洗场景分级清单.md
HaHafeng 1b53ab9d52 feat(aia): Complete AIA V2.0 with universal streaming capabilities 
Major Changes:
- Add StreamingService with OpenAI Compatible format
- Upgrade Chat component V2 with Ant Design X integration
- Implement AIA module with 12 intelligent agents
- Update API routes to unified /api/v1 prefix
- Update system documentation

Backend (~1300 lines):
- common/streaming: OpenAI Compatible adapter
- modules/aia: 12 agents, conversation service, streaming integration
- Update route versions (RVW, PKB to v1)

Frontend (~3500 lines):
- modules/aia: AgentHub + ChatWorkspace (100% prototype restoration)
- shared/Chat: AIStreamChat, ThinkingBlock, useAIStream Hook
- Update API endpoints to v1

Documentation:
- AIA module status guide
- Universal capabilities catalog
- System overview updates
- All module documentation sync

Tested: Stream response verified, authentication working
Status: AIA V2.0 core completed (85%)
2026-01-14 19:15:01 +08:00

6.3 KiB
Raw Blame History

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餈嗘遢皜<EFBFBD><EFBFBD><EFBFBD>?<EFBFBD><EFBFBD><EFBFBD>臬<EFBFBD><EFBFBD>圈𠗕摨?<EFBFBD>?*銝𡁜𦛚<F0A1819C>餉<EFBFBD>憭齿<E686AD>摨?*隞𡒊<E99A9E><F0A1928A>訫<EFBFBD>憭齿<E686AD><E9BDBF>鍦<EFBFBD><E98DA6><EFBFBD><EFBFBD><EFBFBD>匧㦤<E58CA7>臬<EFBFBD><E887AC><EFBFBD>挽<EFBFBD>唳旿撌脣<E6928C>頧賭蛹 Pandas DataFrame (df)<29>?

Level 1: <20>箇<EFBFBD><E7AE87>怎<EFBFBD>皜<EFBFBD><E79A9C> (Data Hygiene)

*<2A>格<EFBFBD>嚗𡁏<E59A97><F0A1818F>𡏭<EFBFBD><F0A18FAD>脲㺭<E884B2>桀<EFBFBD><E6A180>鐥<EFBFBD>𡏭<EFBFBD>霂領<E99C82>萘<EFBFBD><E89098>唳旿<E594B3><E697BF>xcel 銋蠘<E98A8B><E8A098>𡄯<EFBFBD>雿?Python <20>游翰<E6B8B8>游<EFBFBD><E6B8B8>?

1.1 <20>㗛<EFBFBD><E3979B>齿<EFBFBD><E9BDBF><EFBFBD><EFBFBD> (Rename)

  • *<EFBFBD>箸艶嚗? <20>笔<EFBFBD>銵典仍<E585B8>臭葉<E887AD><E89189><EFBFBD><EFBFBD>怎鸌畾羓泵<E7BE93>瘀<EFBFBD>撟湧<E6929F>(撗?, <20>批<EFBFBD>/Gender, <20>仿堺_<E5A0BA>交<EFBFBD>嚗㚁<E59A97>SPSS <20>仿<EFBFBD><E4BBBF>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣<EFBFBD><F0A880A3><EFBFBD><EFBFBD>匧<EFBFBD><E58CA7>滩蓮銝箇滲<E7AE87>望<EFBFBD>撠誩<E692A0>嚗<EFBFBD>縧<EFBFBD>㗇𡠺<E39787>瑯<EFBFBD><E791AF><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? 雿輻鍂<E8BCBB>惩<EFBFBD>摮堒<E691AE><E5A092>𡝗迤<F0A19D97>蹱𤜯<E8B9B1>Ｗ<EFBFBD><EFBCB7>溻<EFBFBD>?

1.2 <20>啣<EFBFBD>澆<EFBFBD><E6BE86>𨀣<EFBFBD>瘥圝<E798A5>?(Clean Numeric)

  • *<EFBFBD>箸艶嚗? 璉<>撉𣬚<E69289>撖澆枂<E6BE86><E69E82>㺭<EFBFBD>殷<EFBFBD><E6AEB7>啣<EFBFBD>澆<EFBFBD>瘛瑕<E7989B>鈭<EFBFBD>泵<EFBFBD>瘀<EFBFBD>>100, <0.1, 12.5+, <20>芣䰻嚗剹<E59A97>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣<EFBFBD><F0A880A3>䁅<EFBFBD><E48185>鐥<EFBFBD>坔<EFBFBD><E59D94>𣬚<EFBFBD><F0A3AC9A>墧㺭摮㛖泵<E39B96>瑕縧<E79195>㚁<EFBFBD><E39A81>娫<0.1<EFBFBD>蹱<EFBFBD><EFBFBD>?.05<EFBFBD>坔<EFBFBD><EFBFBD><EFBFBD><EFBFBD>頧砌蛹瘚桃<EFBFBD><EFBFBD>啜<EFBFBD><EFBFBD><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? str.replace + 甇<><E79487><EFBFBD>𣂼<EFBFBD> + pd.to_numeric(errors='coerce')<29>?

1.3 蝏煺<E89D8F>蝻箏仃<E7AE8F>?(Standardize Nulls)

  • *<EFBFBD>箸艶嚗? <20>唳旿<E594B3>峕毽<E5B395><E6AFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>隞<EFBFBD>”<EFBFBD>𦦵征<F0A6A6B5>萘<EFBFBD>霂㵪<E99C82>NA, N/A, -, \, 銝滩祕<E6BBA9>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣<EFBFBD><F0A880A3><EFBFBD><EFBFBD>劐誨銵兩<E98AB5>䀹瓷<E480B9>争<EFBFBD>嗵<EFBFBD>摮㛖泵<E39B96>賜<EFBFBD>銝<EFBFBD><E98A9D>踵揢銝箸<E98A9D><E7AEB8><EFBFBD><EFBFBD>蝛箏<E89D9B>潦<EFBFBD><E6BDA6><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? df.replace(['-', '銝滩祕', 'NA'], np.nan, inplace=True)<29>?

Level 2: <20>㗛<EFBFBD><E3979B><EFBFBD><EFBFBD><EFBFBD>碶<EFBFBD><E7A2B6>滨<EFBFBD><E6BBA8>?(Recode & Standardization)

*<2A>格<EFBFBD>嚗帋蛹蝏蠘恣<E8A098><E681A3><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>掩<EFBFBD>㗛<EFBFBD><E3979B>?

2.1 <20><>𧋦頧祆㺭<E7A586>潭<EFBFBD>撠?(Map Categorical)

  • *<EFBFBD>箸艶嚗? <20>批<EFBFBD><E689B9>埈糓 Male/Female嚗<65>𢙺<EFBFBD>笔蟮<E7AC94>?Yes/No<4E>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣<EFBFBD><F0A880A3>批<EFBFBD>頧砌蛹 1(<28>?/0(憟?嚗峕<E59A97><E5B395>貊<EFBFBD><E8B28A>脰蓮銝?1/0<><30><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? df['sex'].map({'Male': 1, 'Female': 0})<29>?

2.2 餈䂿賒<E482BF>㗛<EFBFBD><E3979B><EFBFBD>拳 (Binning)

  • *<EFBFBD>箸艶嚗? <20><>閬<EFBFBD><E996AC>撟湧<E6929F><E6B9A7><EFBFBD><EFBFBD>餈𥡝<E9A488><F0A5A19D>⊥䲮璉<E4B2AE>撉䎚<E69289>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣<EFBFBD>撟湧<E6929F><E6B9A7>?0-18, 19-60, 60+ <20><>蛹<EFBFBD>䀹𧊋<E480B9>𣂼僑<F0A382BC>? <20>䀹<EFBFBD>撟氯<E6929F>? <20>䁅<EFBFBD><E48185>僑<EFBFBD>嗘<EFBFBD>蝏<EFBFBD><E89D8F><EFBFBD><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? pd.cut() <20>賣㺭<E8B3A3>?

2.3 憭齿<E686AD><E9BDBF>交<EFBFBD>霈∠<E99C88> (Date Logic)

  • *<EFBFBD>箸艶嚗? 霈∠<E99C88><E288A0>笔<EFBFBD><E7AC94>園𡢿嚗㇉S嚗剹<E59A97><E589B9>xcel 蝏誩虜蝞烾<E89D9E><E783BE>啣僑<E595A3>𡝗<EFBFBD>隞賬<E99A9E>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣覔<F0A880A3>栽<EFBFBD>条＆霂𦠜𠯫<F0A6A09C>麨<EFBFBD>坔<EFBFBD><E59D94>㗛<EFBFBD>霈踵𠯫<E8B8B5>麨<EFBFBD>躰恣蝞㛖<E89D9E>摮䀹<E691AE><E480B9>堆<EFBFBD>靽萘<E99DBD>1雿滚<E99BBF><E6BB9A>啜<EFBFBD><E5959C><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? (df['end_date'] - df['start_date']).dt.days / 30.4<EFBFBD>?

Level 3: 銝游<E98A9D><E6B8B8>餉<EFBFBD><E9A489>孵<EFBFBD>撌亦<E6928C> (Feature Engineering)

*<2A>格<EFBFBD>嚗𡁜抅鈭𤾸龫摮衣䰻霂<E4B0BB><E99C82><EFBFBD>鞉鰵<E99E89><E9B0B5><EFBFBD><EFBFBD>鞉<EFBFBD><E99E89><EFBFBD><EFBFBD>?

3.1 憭滚<E686AD><E6BB9A>砍<EFBFBD>霈∠<E99C88> (Complex Formula)

  • *<EFBFBD>箸艶嚗? 霈∠<E99C88> eGFR (<28>曉<EFBFBD><E69B89><EFBFBD>誘餈<E8AA98><E9A488>) <20>?BMI<4D>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨅯葬<F0A885AF>𤏸恣蝞?BMI<4D><49><EFBFBD><EFBFBD>?BMI > 28嚗𣬚<E59A97><F0A3AC9A>鞉鰵<E99E89>埈<EFBFBD>霈唬蛹<E594AC>䁅<EFBFBD><E48185>砽<EFBFBD>踺<EFBFBD><E8B8BA><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? <20>煾<EFBFBD><E785BE>𤥁恣蝞?df['weight'] / (df['height']/100)**2 + <20>∩辣韏见<E99F8F>?np.where<72>?

3.2 <20>𣂼<EFBFBD><F0A382BC>交<EFBFBD><E4BAA4><EFBFBD><EFBFBD> (Cohort Selection)

  • *<EFBFBD>箸艶嚗? 蝑偦<E89D91>厩泵<E58EA9><E6B3B5>辺隞嗥<E99A9E><E597A5>亦<EFBFBD>鈭箇黎<E7AE87>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𦦵<EFBFBD><F0A6A6B5>匧枂嚗𡁶＆霂𠹺蛹<F0A0B9BA>箄<EFBFBD><E7AE84>䕘<EFBFBD>銝𥪜僑樴<E58391>之鈭?8撗<38><E69297>銝娍瓷<E5A88D>厰<EFBFBD>銵<EFBFBD><E98AB5>讠<EFBFBD><E8AEA0>脩<EFBFBD><E884A9><EFBFBD>犖<EFBFBD><E78A96><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? df.query("diagnosis == 'Lung Adenocarcinoma' & age > 18 & hypertension == 0")<29>?

3.3 <20>穃<EFBFBD><E7A983>讐<EFBFBD><E8AE90>?(One-Hot Encoding)

  • *<EFBFBD>箸艶嚗? <20><><EFBFBD><EFBFBD>?Logistic <20>𧼮<EFBFBD>嚗峕<E59A97>銝<EFBFBD>銝芣<E98A9D>摨誩<E691A8><E8AAA9><EFBFBD>掩<EFBFBD>㗛<EFBFBD><E3979B>𡏭<EFBFBD><F0A18FAD>?(A, B, AB, O)<29>腈<EFBFBD>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣<EFBFBD>銵<EFBFBD><E98AB5>讠<EFBFBD><E8AEA0>𣂼<EFBFBD><F0A382BC>㗛<EFBFBD><E3979B><EFBFBD><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? pd.get_dummies(df['blood_type'], prefix='blood')<29>?

Level 4: 蝏𤘪<E89D8F><F0A498AA>滚<EFBFBD>銝𡡞<E98A9D>蝥扳祥<E689B3>?(Reshaping & Governance)

*<2A>格<EFBFBD>嚗𡁏㺿<F0A1818F>䁅”<E48185>潛<EFBFBD><E6BD9B><EFBFBD>誑<EFBFBD><E8AA91><EFBFBD><EFBFBD>孵<EFBFBD><E5ADB5><EFBFBD><EFBFBD>霈⊥芋<E28AA5>页<EFBFBD><E9A1B5>𤥁<EFBFBD>銵屸<E98AB5><E5B1B8>嗆㺭<E59786>桐耨憭溻<E686AD>?

*4.1 <20>踹捐銵刻蓮<E588BB>?(Pivot/Melt) <20>婙<EFBFBD>?Excel <20><>埯璇?

  • *<EFBFBD>箸艶嚗? <20>桀<EFBFBD><E6A180>胼<EFBFBD>靝<EFBFBD>鈭箏<E988AD>銵𢞖<E98AB5>嘅<EFBFBD>撘牐<E69298>-蝚?甈∪<E79488>撉䕘<E69289>撘牐<E69298>-蝚?甈∪<E79488>撉䕘<E69289>嚗諹<E59A97><E8ABB9>𡁻<EFBFBD>憭齿<E686AD><E9BDBF>誩<EFBFBD><E8AAA9>琜<EFBFBD><E7909C><EFBFBD>閬<EFBFBD><E996AC><EFBFBD>鐥<EFBFBD>靝<EFBFBD>鈭箔<E988AD>銵𢞖<E98AB5>嘅<EFBFBD>撘牐<E69298>-<2D>㚚<EFBFBD>1-<2D>㚚<EFBFBD>2嚗剹<E59A97>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣<EFBFBD>銵冽聢隞𡡞鵭銵刻蓮銝箏捐銵剁<E98AB5><E58981>厩<EFBFBD>鈭截D蝝Ｗ<E89D9D>嚗𣬚鍂<F0A3AC9A>䁅挪閫<E68CAA>活摨謿<E691A8>坔<EFBFBD><E59D94>𡒊<EFBFBD>嚗屸唍撘<E5948D><E69298>条蒾蝏<E892BE><E89D8F><EFBFBD>坔<EFBFBD><E59D94><EFBFBD><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? df.pivot(index='id', columns='visit', values='wbc')<29>?

4.2 <20>箄<EFBFBD><E7AE84>駁<EFBFBD> (Smart Deduplication)

  • *<EFBFBD>箸艶嚗? <20>䔶<EFBFBD>銝芰<E98A9D>鈭箸<E988AD>銝斗辺霈啣<E99C88>嚗䔶<E59A97><E494B6>∩縑<E288A9>臬<EFBFBD>嚗䔶<E59A97><E494B6>∩縑<E288A9>舐撩<E88890>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣<EFBFBD><F0A880A3><EFBFBD>犖ID<49>駁<EFBFBD><E9A781><EFBFBD><EFBFBD><EFBFBD>𨀣<EFBFBD><F0A880A3>滚<EFBFBD>嚗䔶<E59A97><E494B6>仮<EFBFBD>䀹<EFBFBD><E480B9>交𠯫<E4BAA4>麨<EFBFBD>蹱<EFBFBD>餈𤑳<E9A488><F0A491B3><EFBFBD><EFBFBD><EFBFBD>∴<EFBFBD>憒<EFBFBD><E68692><EFBFBD>交<EFBFBD>銝<EFBFBD><E98A9D>瘀<EFBFBD>靽萘<E99DBD><E89098>䀹㺭<E480B9>桀<EFBFBD><E6A180>游漲<E6B8B8>蹱<EFBFBD>擃条<E69383><E69DA1><EFBFBD>辺<EFBFBD><E8BEBA><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? df.sort_values(['date', 'completeness']).drop_duplicates(subset=['id'], keep='last')<29>?

4.3 頝典<E9A09D><E585B8>餉<EFBFBD><E9A489>⊿<EFBFBD> (Cross-Check)

  • *<EFBFBD>箸艶嚗? <20>𤑳緵<F0A491B3>𤩺㺭<F0A4A9BA>柴<EFBFBD>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣<EFBFBD><F0A880A3>乩<EFBFBD>銝𧢲<E98A9D>瘝⊥<E7989D><E28AA5>条琸<E69DA1>把<EFBFBD>嗘<EFBFBD><E59798>胼<EFBFBD>䀹<EFBFBD><E480B9>摮閙活<E99699>豹>0<>嗵<EFBFBD><E597B5>躰秤<E8BAB0>唳旿嚗峕<E59A97>霈啣枂<E595A3>乓<EFBFBD><E4B993><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? df.loc[(df['sex']=='<27>?) & (df['preg_count']>0), 'error_flag'] = 1<>?

4.4 憭𡁻<E686AD><F0A181BB>坿‘ (Multiple Imputation) <20>婙<EFBFBD>?蝏蠘恣摮衣<E691AE>擃条漣憛怨‘

  • *<EFBFBD>箸艶嚗? <20>唳旿<E594B3><E697BF><EFBFBD>蝻箏仃<E7AE8F>潘<EFBFBD>憒?BMI 蝻箏仃嚗㚁<E59A97><E39A81>閧滲<E996A7>典<EFBFBD><E585B8>澆‵銵乩<E98AB5><E4B9A9>游<EFBFBD><E6B8B8>唳旿<E594B3><E697BF><EFBFBD><EFBFBD><EFBFBD><EFBFBD>閬<EFBFBD>⏚<EFBFBD>典<EFBFBD>隞硋<E99A9E><E7A18B>𧶏<EFBFBD>憒<EFBFBD>僑樴<E58391><E6A8B4><EFBFBD><EFBFBD>批<EFBFBD><E689B9><EFBFBD><EFBFBD><EFBFBD>琜<EFBFBD><E7909C><EFBFBD>㮾<EFBFBD>單<EFBFBD>扳䔉憸<E49489><E686B8>憛怨‘<E680A8>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>靝蝙<E99D9D>典<EFBFBD><E585B8>齿<EFBFBD>銵交<E98AB5>(MICE)撖嫖<E69296>𦲂MI<4D>坔<EFBFBD><E59D94>睃僑樴<E58391><E6A8B4>坔<EFBFBD><E59D94><EFBFBD>撩憭勗<E686AD>潸<EFBFBD>銵<EFBFBD>‵銵乓<E98AB5><E4B993><EFBFBD>?

  • Python <20>餉<EFBFBD>嚗?```python from sklearn.experimental import enable_iterative_imputer from sklearn.impute import IterativeImputer 隞<EFBFBD><EFBFBD>撖寞㺭<EFBFBD>澆<EFBFBD>餈𥡝<EFBFBD><EFBFBD>坿‘ cols = ['bmi', 'age', 'creatinine'] imp = IterativeImputer(max_iter=10, random_state=0) df[cols] = imp.fit_transform(df[cols])

Level 5: <20>䂿<EFBFBD><E482BF><EFBFBD><EFBFBD><EFBFBD><EFBFBD>𧋦<EFBFBD>𡝗<EFBFBD> (Text Mining) <20>婙<EFBFBD>?Python <20><><EFBFBD>撖寧<E69296>瘝餃躹

*<2A>格<EFBFBD>嚗帋<E59A97>憭<EFBFBD>釣<EFBFBD>𡝗𥁒<F0A19D97>𦠜<EFBFBD><F0A6A09C>砌葉<E7A08C>𨀣<EFBFBD><F0A880A3>嘥枂<E598A5>唳旿<E594B3><E697BF><EFBFBD><EFBFBD>?Excel 蝏嘥笆<E598A5>帋<EFBFBD><E5B88B>啁<EFBFBD><E59581>?

5.1 甇<><E79487>銵刻噢撘𤩺<E69298><F0A4A9BA>?(Regex Extraction)

  • *<EFBFBD>箸艶嚗? <20>芣<EFBFBD>銝<EFBFBD><E98A9D>埈<EFBFBD><E59F88>砂<EFBFBD>𦦵<EFBFBD><F0A6A6B5><EFBFBD><EFBFBD><EFBFBD>凌<EFBFBD>嘅<EFBFBD><E59885><EFBFBD>捆憒<E68D86><E68692><EFBFBD>?撌西<E6928C>銝𠰴蠏)瘚豢隋<E8B1A2>扯<EFBFBD><E689AF>䕘<EFBFBD>憭批<E686AD>3.5*2cm<63>腈<EFBFBD><E88588><EFBFBD>閬<EFBFBD><E996AC><EFBFBD>𤥁<EFBFBD><F0A4A581>文之撠譌<E692A0>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>靝<EFBFBD><E99D9D>条<EFBFBD><E69DA1><EFBFBD><EFBFBD><EFBFBD>凌<EFBFBD>䠷<EFBFBD><E4A0B7>𣂼<EFBFBD><F0A382BC>箄<EFBFBD><E7AE84>斤<EFBFBD><E696A4>踹<EFBFBD>嚗<EFBFBD><E59A97>憭抒<E686AD><E68A92><EFBFBD>葵<EFBFBD>啣<EFBFBD>嚗剹<E59A97><E589B9><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? df['text'].str.extract(r'(\d+\.?\d*)\s*[\*xX]\s*(\d+\.?\d*)') 撟嗅<E6929F><E59785><EFBFBD>憭批<E686AD>潦<EFBFBD>?

5.2 摮㛖泵銝脫芋蝟𠰴龪<F0A0B0B4>?(Fuzzy Matching)

  • *<EFBFBD>箸艶嚗? <20>駁堺<E9A781>滨妍敶訫<E695B6>瘛瑚僚嚗尠<E59A97>𨅯<EFBFBD><F0A885AF><EFBFBD>龫<EFBFBD>Ｔ<EFBFBD>腈<EFBFBD><E88588><EFBFBD>𨅯<EFBFBD>鈭砍<E988AD><E7A08D>𢞖<EFBFBD>腈<EFBFBD><E88588><EFBFBD>𨅯<EFBFBD><F0A885AF>𢞖<EFBFBD>腈<EFBFBD><E88588><EFBFBD>閬<EFBFBD><E996AC>銝<EFBFBD><E98A9D>?
  • *<EFBFBD>冽<EFBFBD><EFBFBD><EFBFBD>誘嚗? <20>𨀣<EFBFBD><F0A880A3>睃龫<E79D83>Ｗ<EFBFBD>蝘售<E89D98>坔<EFBFBD><E59D94>峕<EFBFBD><E5B395>匧<EFBFBD><E58CA7>徉<EFBFBD>睃<EFBFBD><E79D83>𢞖<EFBFBD>嗵<EFBFBD>嚗屸<E59A97>蝏煺<E89D8F><E785BA>嫣蛹<E5ABA3>婱UMCH<43>踺<EFBFBD><E8B8BA><EFBFBD>?
  • *Python <20>餉<EFBFBD>嚗? df.loc[df['hospital'].str.contains('<27>誩<EFBFBD>'), 'hospital'] = 'PUMCH'<27>
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