Step1 统计多工作表总行数，并根据数据量级（如≥1万行）动态启用Parquet格式转换以优化大文件读取性能。
pythonimport pandas as pd import os from openpyxl import load_workbook file_path = "your_excel_file.xlsx" xls = pd.ExcelFile(file_path) sheet_names = xls.sheet_names # 统计所有sheet的数据行数 total_rows = 0 for sheet in sheet_names: wb = load_workbook(file_path, read_only=True, data_only=True) ws = wb[sheet] max_row = ws.max_row data_rows = max_row - 1 if max_row > 0 else 0 total_rows += data_rows wb.close() print(f"总数据行数: {total_rows}") # 大文件优化：转换为Parquet格式读取 if total_rows >= 10000: df = pd.read_excel(file_path, sheet_name=sheet_names[0]) parquet_path = '/tmp/temp_data.parquet' df.to_parquet(parquet_path, engine='pyarrow') df = pd.read_parquet(parquet_path) else: df = pd.read_excel(file_path, sheet_name=sheet_names[0])
Step2 使用正则表达式对指定文本列进行数据清洗（例如仅保留中文字符）。
pythonimport re def clean_chinese_text(text): if pd.isna(text): return text s = str(text) # 提取所有中文字符 chinese_chars = re.findall(r'[一-鿿]', s) cleaned = ''.join(chinese_chars) return cleaned if cleaned != '' else '' target_col = '目标清洗列' # 替换为实际列名 if target_col in df.columns: df[target_col] = df[target_col].apply(clean_chinese_text)
Step3 提取关键数据进行多维度分析（分类汇总求极值或双变量线性拟合）。
pythonimport numpy as np # 模式1：分类汇总与极值提取 group_col = '分类列' value_col = '数值列' # 示例占位数据提取逻辑 summary = pd.DataFrame({ group_col: ['类别A', '类别B', '类别C'], value_col: [100, 500, 200] }) max_idx = summary[value_col].idxmax() max_type = summary.loc[max_idx, group_col] # 模式2：双变量线性关系分析 x_col = 'X轴列' y_col = 'Y轴列' if x_col in df.columns and y_col in df.columns: x_data = df[x_col].values y_data = df[y_col].values # 拟合线性趋势线 coefficients = np.polyfit(x_data, y_data, 1) trend_line = np.poly1d(coefficients)(x_data)
Step4 生成带条件格式的Excel报告（如高亮最大值）及可视化图表，并提供下载链接。
pythonfrom openpyxl import Workbook from openpyxl.styles import PatternFill, Font, Alignment, Border, Side import matplotlib.pyplot as plt # 1. 生成带样式标记的Excel文件 wb = Workbook() ws = wb.active ws.title = "分析结果" # 定义样式 header_fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid") header_font = Font(name="SimHei", bold=True, color="FFFFFF", size=12) highlight_fill = PatternFill(start_color="00B050", end_color="00B050", fill_type="solid") highlight_font = Font(name="SimHei", bold=True, color="FFFFFF", size=12) normal_font = Font(name="SimHei", size=11) center_align = Alignment(horizontal="center", vertical="center") thin_border = Border(left=Side(style="thin"), right=Side(style="thin"), top=Side(style="thin"), bottom=Side(style="thin")) # 写入表头与数据 headers = [group_col, value_col] for col, header in enumerate(headers, 1): cell = ws.cell(row=1, column=col, value=header) cell.fill = header_fill cell.font = header_font cell.alignment = center_align cell.border = thin_border for row_idx, row in summary.iterrows(): c_type = ws.cell(row=row_idx+2, column=1, value=row[group_col]) c_val = ws.cell(row=row_idx+2, column=2, value=row[value_col]) for cell in [c_type, c_val]: cell.alignment = center_align cell.border = thin_border cell.font = normal_font # 高亮最大值行 if row[group_col] == max_type: c_type.fill = highlight_fill c_type.font = highlight_font c_val.fill = highlight_fill c_val.font = highlight_font output_excel_path = "/mnt/data/analysis_report.xlsx" wb.save(output_excel_path) # 2. 生成散点图与趋势线 (如果存在拟合数据) if 'x_data' in locals(): plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False plt.figure(figsize=(10, 6), dpi=100) plt.scatter(x_data, y_data, color='blue', s=80, label='数据点') plt.plot(x_data, trend_line, color='red', linewidth=2, label=f'趋势线: y={coefficients[0]:.2f}x+{coefficients[1]:.2f}') plt.xlabel(x_col) plt.ylabel(y_col) plt.title(f'{x_col} vs {y_col} 散点图与趋势线') plt.legend() plt.grid(True) output_img_path = '/mnt/data/scatter_plot.png' plt.savefig(output_img_path, bbox_inches='tight') plt.close() print(f"文件已生成，下载链接:") print(f"- 分析报告: {output_excel_path}") if 'x_data' in locals(): print(f"- 趋势图表: {output_img_path}")
