excel-sort

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📖 技能介绍


name: excel-sort description: | Sort Excel data by specified column(s). Supports single or multi-column sort, ascending/descending, numeric/date/text sorting. 对 Excel 数据按指定列排序,支持单列或多列排序、升序/降序、数值/日期/文本排序。 Trigger keywords: "sort" "ascending" "descending" "sort by column" "from small to large" "newest first" 触发词包括"排序""升序""降序""按xx列排列""从小到大""从新到旧"。


This skill follows [[excel-safe-workflow]] four-step method. Must scout and confirm sort column and range before execution, and verify correct order after. 本技能遵循 [[excel-safe-workflow]] 四步法。执行前必须勘察确认排序列和范围,执行后验证顺序正确。

Excel Sort / Excel 排序

第零步:需求解析

要素 常见表述 默认值
排序列 "按公开日排序""E列排序" 必须明确
方向 "从小到大""升序""asc" → asc;"从大到小""降序""desc" → desc asc
多列排序 "先按A列再按B列" 按优先级排列
数据范围 默认包含表头行(第1行),自动识别数据区 有表头

解析示例

用户说 提取
"按公开日升序排列" 列=公开日, asc
"按金额从大到小排序" 列=金额, desc
"先按类别排,再按日期排" 列=[类别,日期], 默认asc

第一步:勘察

from openpyxl import load_workbook

FILE = '目标文件.xlsx'
wb = load_workbook(FILE)
ws = wb.active
print(f'{ws.max_row}行 x {ws.max_column}列')

# 定位排序列
print('\n=== 表头 ===')
for col_idx in range(1, ws.max_column + 1):
    h = ws.cell(row=1, column=col_idx).value
    if h:
        print(f'  列{col_idx}: {h}')

# 确认数据类型
sort_col = None  # 排序列号
print(f'\n排序列数据样本:')
for row in [2, 3, 4, ws.max_row // 2, ws.max_row]:
    v = ws.cell(row=row, column=sort_col).value
    print(f'  行{row}: {type(v).__name__} = {repr(v)[:40]}')

wb.close()

第二步:执行

策略:大文件统一走「读格式→pandas处理→刷回格式」三步。

更多技能请访问小葱技能站7w4.net。

import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import Font, Alignment, PatternFill
from copy import copy

FILE = '目标文件.xlsx'
SORT_COLS = [('列名或列号', 'asc')]  # asc/desc
HEADER_ROW = 1

# ====== 第一步:读取格式 ======
print('① 读取格式...')
wb = load_workbook(FILE)
ws = wb.active

header_formats, data_formats, col_widths = {}, {}, {}
for col in range(1, ws.max_column + 1):
    header_formats[col] = {
        'font': copy(ws.cell(row=HEADER_ROW, column=col).font),
        'alignment': copy(ws.cell(row=HEADER_ROW, column=col).alignment),
        'fill': copy(ws.cell(row=HEADER_ROW, column=col).fill),
    }
    data_formats[col] = {
        'font': copy(ws.cell(row=HEADER_ROW + 1, column=col).font),
        'alignment': copy(ws.cell(row=HEADER_ROW + 1, column=col).alignment),
        'fill': copy(ws.cell(row=HEADER_ROW + 1, column=col).fill),
    }
    col_letter = chr(64 + col) if col <= 26 else ''
    if col_letter and col_letter in ws.column_dimensions:
        col_widths[col] = ws.column_dimensions[col_letter].width

freeze = ws.freeze_panes
col_names = [ws.cell(row=HEADER_ROW, column=c).value for c in range(1, ws.max_column + 1)]
wb.close()

# ====== 第二步:pandas 排序 ======
print('② 排序...')
df = pd.read_excel(FILE)

# 列名归一化
sort_by = []
ascending = []
for spec, direction in SORT_COLS:
    name = col_names[spec - 1] if isinstance(spec, int) else spec
    sort_by.append(name)
    ascending.append(direction == 'asc')

df = df.sort_values(by=sort_by, ascending=ascending)
print(f'已排序: {list(zip(sort_by, ["asc" if a else "desc" for a in ascending]))}')

# ====== 第三步:写回 + 轻量格式 ======
print('③ 写回并恢复关键格式...')
df.to_excel(FILE, index=False)

wb = load_workbook(FILE)
ws = wb.active

# 核心格式(始终恢复,秒级)
for col in range(1, ws.max_column + 1):
    cl = chr(64 + col) if col <= 26 else ''
    if col in header_formats:
        hf = header_formats[col]
        c = ws.cell(row=HEADER_ROW, column=col)
        c.font, c.alignment, c.fill = hf['font'], hf['alignment'], hf['fill']
    if cl and col in col_widths and col_widths[col]:
        ws.column_dimensions[cl].width = col_widths[col]

# 数据格式:仅小文件(<1万行)逐格恢复
if ws.max_row <= 10000 and data_formats:
    for row in range(HEADER_ROW + 1, ws.max_row + 1):
        for col in range(1, ws.max_column + 1):
            if col in data_formats:
                df2 = data_formats[col]
                c = ws.cell(row=row, column=col)
                c.font, c.alignment, c.fill = df2['font'], df2['alignment'], df2['fill']

if freeze: ws.freeze_panes = freeze
wb.save(FILE)
print('完成')

第三步:验证

wb = load_workbook(FILE, read_only=True, data_only=True)
ws = wb.active

for col_idx, direction in sort_specs:
    print(f'\n验证列{col_idx} ({direction}):')
    prev = None
    ok = True
    for row in range(HEADER_ROW + 1, min(HEADER_ROW + 20, ws.max_row + 1)):
        v = ws.cell(row=row, column=col_idx).value
        if prev is not None and v is not None:
            if direction == 'asc' and v < prev:
                print(f'  ❌ 行{row}: {v} < {prev}')
                ok = False
            elif direction == 'desc' and v > prev:
                print(f'  ❌ 行{row}: {v} > {prev}')
                ok = False
        if v is not None:
            prev = v
        print(f'  行{row}: {v}')
    print(f'  {"✅" if ok else "❌"}')

wb.close()

注意事项

  1. None 值始终排到最后(无论升序降序)
  2. 表头不参与排序,固定在原位
  3. 公式列排序会导致引用错乱——勘察时检查排序列是否为公式,如是则先转为值再排
  4. 格式保留 vs 速度
  5. < 1万行:默认保留全部格式(几秒)
  6. 1万~5万行:保留格式(~1分钟)
  7. > 5万行:询问用户——「格式保留需要约 N 分钟,是否保留原格式?」
  8. 列内格式差异:格式以数据首行为模板统一应用。如列内格式不一致,统一后会丢失差异
  9. 操作前必备份:遵循 [[excel-safe-workflow]] 第零步——操作前自动备份(时间戳命名),成功后保留最新3份,失误后立即删除损坏文件并从备份恢复

🤖 AI 评测

这是一个质量较高的 Excel 排序技能。它能处理单列或多列排序,保留原有的格式和样式,并且有备份机制防止误操作出错。文档写得很详细,步骤清晰,但缺少示例文件和常见问题解答,新手初次使用可能需要花点时间理解。总体来说功能实用、考虑周全,是一个可靠的处理 Excel 排序的工具。

📊 多维度评分

适应性4.2
规范性4.4
有效性4.4
可靠性4.2
可信度5

📁 包含文件 (1 个)

📄 SKILL.md 6.4 KB