HaHafeng
74cf346453
Major features: 1. Missing value imputation (6 simple methods + MICE): - Mean/Median/Mode/Constant imputation - Forward fill (ffill) and Backward fill (bfill) for time series - MICE multivariate imputation (in progress, shape issue to fix) 2. Auto precision detection: - Automatically match decimal places of original data - Prevent false precision (e.g. 13.57 instead of 13.566716417910449) 3. Categorical variable detection: - Auto-detect and skip categorical columns in MICE - Show warnings for unsuitable columns - Suggest mode imputation for categorical data 4. UI improvements: - Rename button: "Delete Missing" to "Missing Value Handling" - Remove standalone "Dedup" and "MICE" buttons - 3-tab dialog: Delete / Fill / Advanced Fill - Display column statistics and recommended methods - Extended warning messages (8 seconds for skipped columns) 5. Bug fixes: - Fix sessionService.updateSessionData -> saveProcessedData - Fix OperationResult interface (add message and stats) - Fix Toolbar button labels and removal Modified files: Python: operations/fillna.py (new, 556 lines), main.py (3 new endpoints) Backend: QuickActionService.ts, QuickActionController.ts, routes/index.ts Frontend: MissingValueDialog.tsx (new, 437 lines), Toolbar.tsx, index.tsx Tests: test_fillna_operations.py (774 lines), test scripts and docs Docs: 5 documentation files updated Known issues: - MICE imputation has DataFrame shape mismatch issue (under debugging) - Workaround: Use 6 simple imputation methods first Status: Development complete, MICE debugging in progress Lines added: ~2000 lines across 3 tiers
34 lines
858 B
Python
34 lines
858 B
Python
"""测试dc_executor模块"""
|
|
print("测试dc_executor模块导入...")
|
|
try:
|
|
from services.dc_executor import validate_code, execute_pandas_code
|
|
print("✅ 模块导入成功")
|
|
|
|
# 测试验证功能
|
|
print("\n测试validate_code...")
|
|
result = validate_code("df['x'] = 1")
|
|
print(f"✅ validate_code成功: {result}")
|
|
|
|
# 测试执行功能
|
|
print("\n测试execute_pandas_code...")
|
|
test_data = [{"age": 25}, {"age": 65}]
|
|
result = execute_pandas_code(test_data, "df['old'] = df['age'] > 60")
|
|
print(f"✅ execute_pandas_code成功: success={result['success']}")
|
|
if result['success']:
|
|
print(f" 结果: {result['result_data']}")
|
|
|
|
print("\n🎉 所有模块测试通过！")
|
|
|
|
except Exception as e:
|
|
print(f"❌ 测试失败: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|