Automating Validation of 300,000 Records with Python + AI to Detect Errors and Dirty Data

Even with a 99 % accuracy rate, a dataset of 300,000 rows still contains thousands of errors, making manual verification impossible and extremely time‑consuming.

The final step of the workflow combines Python code with AI‑driven rules to automatically inspect and flag anomalous records.

import pandas as pd

df = pd.read_excel("全部数据汇总总表.xlsx")
error_rows = []
# Custom business validation rules
for idx, row in df.iterrows():
    err = []
    # Rule 1: non‑empty field check
    if pd.isna(row["字段1"]) or str(row["字段1"]).strip() == "":
        err.append("字段1为空")
    # Rule 2: numeric format check
    if not str(row["字段2"]).isdigit():
        err.append("字段2格式异常")
    # Rule 3: additional length/range checks can be added here
    if err:
        error_rows.append({
            "行号": idx + 2,
            "错误类型": ",".join(err),
            "原始数据": dict(row)
        })
# Export error report
error_df = pd.DataFrame(error_rows)
error_df.to_excel("数据异常报错清单.xlsx", index=False)
print(f"✅ 检测完成，发现异常数据：{len(error_rows)} 条")

The complete process consists of four modules: (1) batch image cropping and preprocessing (denoising to improve OCR accuracy); (2) high‑precision table extraction using Python OCR; (3) merging tens of thousands of rows into a single Excel worksheet; and (4) automatic anomaly detection with the script above, which outputs an error list.

Project summary: processing 300 k‑level image‑table data proves that AI‑assisted automation excels at repetitive, large‑scale, high‑precision tasks, completing in minutes what would take humans days, while delivering higher accuracy and completeness. The four‑module source code is ready to be applied to any similar bulk data workflow.