Python Automation: Merge Tens of Thousands of Records into a Single Excel Sheet with One Click

Business pain point: after OCR processing of 30,000 images, tens of thousands of scattered data rows are generated, and manually copying, pasting, and merging them would require huge effort.

Goal: achieve fully automated structuring, merging, and generation of a consolidated Excel summary.

Solution: a Python script that leverages pandas to read the temporary OCR data, map fields, build a list of dictionaries, convert the list to a DataFrame, and write a single Excel file.

import pandas as pd
import os
# 读取OCR临时数据，结构化整理
def structured_data(ocr_list):
    res_list = []
    for item in ocr_list:
        # 根据你的表格字段映射（可自定义）
        row_data = {
            "字段1": item[0] if len(item) > 0 else "",
            "字段2": item[1] if len(item) > 1 else "",
            "字段3": item[2] if len(item) > 2 else "",
            "字段4": item[3] if len(item) > 3 else ""
        }
        res_list.append(row_data)
    return res_list
# 批量生成总表
total_data = structured_data(all_ocr_data)
df = pd.DataFrame(total_data)
# 保存总Excel
df.to_excel("全部数据汇总总表.xlsx", index=False)
print(f"✅ 数据合并完成，共{len(df)}条数据")

The script prints a confirmation message showing the total number of merged records.

Benefits: completely eliminates manual copy‑paste, enforces uniform fields and formats, and merges tens of thousands of rows in seconds.

Completed workflow: image preprocessing → high‑precision OCR → full data aggregation. The remaining challenge is validating and cleaning dirty or missing data, which will be addressed in the next article.