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lofyer
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factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
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#!/usr/bin/env python3
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"""
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二期数据验证脚本
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检查 ETL 产出的 parquet 文件是否符合预期，
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并与一期数据做结构和字段ID的对比
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"""
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import pandas as pd
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import json
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).parent.parent.parent # report-admin/
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ERA1_DATA = PROJECT_ROOT / "data"
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ERA2_DATA = PROJECT_ROOT / "data" / "era2"
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def section(title):
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print(f"\n{'=' * 70}")
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print(f" {title}")
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print(f"{'=' * 70}")
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def main():
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print("📊 二期数据验证 & 一期对比")
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# ===== 加载二期数据 =====
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section("1. 加载二期 ETL 输出")
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df_basic = pd.read_parquet(ERA2_DATA / "era2_基础信息表.parquet")
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df_course = pd.read_parquet(ERA2_DATA / "era2_课程实施情况表.parquet")
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df_subject = pd.read_parquet(ERA2_DATA / "era2_学科课程实施情况表.parquet")
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print(f" 基础信息表: {df_basic.shape}")
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print(f" 课程实施情况表: {df_course.shape}")
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print(f" 学科课程实施情况表: {df_subject.shape}")
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# ===== 加载一期数据 =====
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section("2. 加载一期数据")
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df1_basic = pd.read_excel(ERA1_DATA / "长宁区_基础信息表.xlsx")
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df1_course = pd.read_excel(ERA1_DATA / "长宁区_课程实施情况表.xlsx")
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df1_subject = pd.read_excel(ERA1_DATA / "长宁区_学科课程实施情况表.xlsx")
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print(f" 基础信息表: {df1_basic.shape}")
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print(f" 课程实施情况表: {df1_course.shape}")
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print(f" 学科课程实施情况表: {df1_subject.shape}")
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# ===== 列名对比 =====
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section("3. 列名对比")
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for name, (d1, d2) in {
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'基础信息表': (df1_basic, df_basic),
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'课程实施情况表': (df1_course, df_course),
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'学科课程实施情况表': (df1_subject, df_subject),
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}.items():
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c1 = set(d1.columns)
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c2 = set(d2.columns)
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print(f"\n [{name}]")
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print(f" 一期列: {sorted(c1)}")
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print(f" 二期列: {sorted(c2)}")
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print(f" 一期有/二期无: {sorted(c1 - c2) or '无'}")
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print(f" 二期有/一期无: {sorted(c2 - c1) or '无'}")
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print(f" 共有列: {sorted(c1 & c2)}")
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# ===== 字段ID对比（课程实施情况表） =====
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section("4. 课程实施情况表 - 字段ID对比")
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# 一期用 "字段ID"，二期用 "字段ID"
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id_col_1 = '字段ID' if '字段ID' in df1_course.columns else None
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id_col_2 = '字段ID' if '字段ID' in df_course.columns else None
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if id_col_1 and id_col_2:
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ids1 = set(df1_course[id_col_1].dropna().unique())
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ids2 = set(df_course[id_col_2].dropna().unique())
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print(f" 一期字段ID数: {len(ids1)}")
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print(f" 二期字段ID数: {len(ids2)}")
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common = ids1 & ids2
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print(f" 共有: {len(common)}")
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print(f" 一期有/二期无: {len(ids1 - ids2)}")
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if ids1 - ids2:
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print(f" 缺失: {sorted(ids1 - ids2)[:20]}{'...' if len(ids1 - ids2) > 20 else ''}")
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print(f" 二期有/一期无: {len(ids2 - ids1)}")
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if ids2 - ids1:
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print(f" 新增: {sorted(ids2 - ids1)[:20]}{'...' if len(ids2 - ids1) > 20 else ''}")
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else:
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print(f" ⚠️ 无法对比: 一期列={id_col_1}, 二期列={id_col_2}")
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# 尝试用字段名称对比
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names1 = set(df1_course['字段名称'].dropna().unique())
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names2 = set(df_course['字段名称'].dropna().unique())
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print(f" [改用字段名称对比]")
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print(f" 一期字段名称数: {len(names1)}")
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print(f" 二期字段名称数: {len(names2)}")
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common = names1 & names2
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print(f" 共有: {len(common)}")
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only1 = sorted(names1 - names2)
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only2 = sorted(names2 - names1)
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print(f" 一期有/二期无: {len(only1)}")
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if only1:
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for n in only1[:30]:
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print(f" - {n}")
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print(f" 二期有/一期无: {len(only2)}")
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if only2:
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for n in only2[:30]:
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print(f" + {n}")
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# ===== 学科课程表 - 字段名称对比 =====
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section("5. 学科课程实施情况表 - 字段名称对比")
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names1 = set(df1_subject['字段名称'].dropna().unique())
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names2 = set(df_subject['字段名称'].dropna().unique())
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print(f" 一期字段名称数: {len(names1)}")
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print(f" 二期字段名称数: {len(names2)}")
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common = names1 & names2
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print(f" 共有: {len(common)}")
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only1 = sorted(names1 - names2)
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only2 = sorted(names2 - names1)
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print(f" 一期有/二期无: {len(only1)}")
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if only1:
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for n in only1[:20]:
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print(f" - {n}")
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if len(only1) > 20:
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print(f" ... (共{len(only1)}个)")
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print(f" 二期有/一期无: {len(only2)}")
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if only2:
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for n in only2[:20]:
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print(f" + {n}")
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if len(only2) > 20:
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print(f" ... (共{len(only2)}个)")
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# ===== 长宁区数据提取验证 =====
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section("6. 长宁区数据提取")
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cn_course = df_course[df_course['所在区'] == '长宁区']
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cn_subject = df_subject[df_subject['所在区'] == '长宁区']
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print(f" 课程实施情况表(长宁区): {len(cn_course)}行")
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print(f" 学科课程实施情况表(长宁区): {len(cn_subject)}行")
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print(f" 长宁区学校: {sorted(cn_course['学校简称'].unique())}")
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print(f" 一期学校: {sorted(df1_course['学校简称'].unique()) if '学校简称' in df1_course.columns else sorted(df1_course['学校名称'].unique())}")
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# ===== 数据质量 =====
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section("7. 数据质量检查")
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for name, df in [('课程实施情况表', df_course), ('学科课程实施情况表', df_subject)]:
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total = len(df)
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nulls = df['字段取值'].isna().sum() + (df['字段取值'] == '').sum() + (df['字段取值'] == 'nan').sum()
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print(f" [{name}]")
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print(f" 总行数: {total}")
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print(f" 字段取值为空/nan: {nulls} ({nulls/total*100:.1f}%)")
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print(f" 学校数: {df['学校简称'].nunique()}")
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print(f"\n✅ 验证完成!")
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if __name__ == "__main__":
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main()
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