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根据数据规模动态选择处理策略,对多表数据进行合并、统计筛选,并利用 openpyxl 实现关键指标的自动化样式高亮与格式化导出。
Step1 提取并合并多个 Sheet 中的关键维度数据,进行数据清洗、类型转换及 Top-N 筛选。
# 示例:合并两个 Sheet 的数据
# 读取 Sheet1 并清洗
df1 = pd.read_excel(file_path, sheet_name='Sheet1', header=None)
# 假设 group_col 在第0列,value_col 在第2列
data1 = df1.iloc[20:, [0, 2]].copy()
data1.columns = ['group_col', 'value_col_1']
data1['value_col_1'] = pd.to_numeric(data1['value_col_1'], errors='coerce')
data1['group_col'] = data1['group_col'].ffill() # 处理合并单元格产生的缺失
# 读取 Sheet2 并清洗
df2 = pd.read_excel(file_path, sheet_name='Sheet2', header=None)
data2 = df2.iloc[5:, [0, 1]].copy()
data2.columns = ['value_col_2', 'value_col_3']
# 合并数据
merged_df = pd.concat([data1.reset_index(drop=True), data2.reset_index(drop=True)], axis=1)
merged_df = merged_df.dropna(subset=['value_col_1'])
# 筛选关键指标前五的数据
top_results = merged_df.nlargest(5, 'value_col_1').copy()
# 占位示例:修正特定缺失值
# top_results.loc[top_results['group_col'].isna(), 'group_col'] = 'Default_Value'
Step2 使用 openpyxl 创建格式化表格,应用条件样式(如特定列标红、最大值高亮)并设置边框与对齐方式。
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
output_path = 'analysis_report.xlsx'
# 创建工作簿
wb = Workbook()
ws = wb.active
ws.title = 'Analysis_Results'
# 定义样式
npx skills add OpenSenseNova/SenseNova-Skills --skill top-value-coloringHow clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
Mostly clear, but there are still a few confusing or poorly structured parts.
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
Partially actionable with several concrete steps, but still missing important details.