[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tb-a-2077776805250465792-2077777909589737472":3},{"detail":4,"html":18,"toc":19,"catalog":32,"tool":36},{"id":5,"title":6,"coverUrl":7,"url":8,"level":8,"description":9,"publishDate":10,"views":11,"isCollect":12,"isLike":12,"vipRequired":13,"vipFlag":14,"price":8,"contentType":15,"station":8,"seq":15,"readMinutes":16,"toolIds":17,"relatedVideo":8},"2077777909589737472","pandas 数据处理：给会写 Python 的人，生产级 DataFrame 代码","",null,"出自 Jeff Allan 的 10.6k⭐ skill 合集。代码级 pandas 专家：清洗（NaN 插值\u002F前向填充）、聚合（groupby）、多键 join、透视、时间序列重采样、大数据集性能优化。工作流强调向量化、方法链、断言校验。适合会写 Python、要把数据处理落成生产代码的人。边界：产出是 pandas 代码，不是无代码工具。","2026-07-16",0,"0",false,"1",1,2,[],"\u003Cdiv style=\"padding:0 4px 24px;font-size:14px;color:#333;line-height:1.85;word-break:break-word\">\n\n\u003Cdiv style=\"background:#FFF7ED;border:1px solid #FFEDD5;border-radius:12px;padding:14px;margin:0 0 18px;font-size:13px;line-height:1.7;color:#9A3412\">⭐ \u003Cstrong style=\"font-weight:700;color:#EA580C\">社区高星\u003C\u002Fstrong>：出自 Jeff Allan 的 \u003Cstrong style=\"font-weight:700;color:#EA580C\">10.6k⭐\u003C\u002Fstrong> skill 合集（66 个全栈开发 skill）。给会写 Python 的人用的数据处理专家。\u003C\u002Fdiv>\n\n\u003Ch1 style=\"font-size:17px;font-weight:800;color:#111;margin:26px 0 14px;padding-left:10px;border-left:5px solid #FF8916;line-height:1.5\" id=\"s1\" data-toc>一、它是干嘛的\u003C\u002Fh1>\n\u003Cp style=\"margin:0 0 14px\">代码级的 \u003Cstrong style=\"font-weight:700;color:#1a1a1a\">pandas 数据处理专家\u003C\u002Fstrong>。你要对 DataFrame 做的那些活——清洗、聚合、合并、透视、时间序列——它都给你写成\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">生产级、高性能\u003C\u002Fstrong>的 pandas 代码，而不是能跑就行的糙代码。\u003C\u002Fp>\n\n\u003Ch1 style=\"font-size:17px;font-weight:800;color:#111;margin:26px 0 14px;padding-left:10px;border-left:5px solid #FF8916;line-height:1.5\" id=\"s2\" data-toc>二、擅长的操作\u003C\u002Fh1>\n\u003Cdiv style=\"display:flex;margin:0 0 8px\">\u003Cspan style=\"color:#FF8916;margin-right:8px;flex-shrink:0;font-weight:700\">•\u003C\u002Fspan>\u003Cdiv style=\"flex:1\">\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">清洗\u003C\u002Fstrong>：NaN 插值 \u002F 前向填充、类型转换、去重\u003C\u002Fdiv>\u003C\u002Fdiv>\n\u003Cdiv style=\"display:flex;margin:0 0 8px\">\u003Cspan style=\"color:#FF8916;margin-right:8px;flex-shrink:0;font-weight:700\">•\u003C\u002Fspan>\u003Cdiv style=\"flex:1\">\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">聚合\u003C\u002Fstrong>：groupby 多级分组统计\u003C\u002Fdiv>\u003C\u002Fdiv>\n\u003Cdiv style=\"display:flex;margin:0 0 8px\">\u003Cspan style=\"color:#FF8916;margin-right:8px;flex-shrink:0;font-weight:700\">•\u003C\u002Fspan>\u003Cdiv style=\"flex:1\">\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">合并\u003C\u002Fstrong>：多键 join、拼表\u003C\u002Fdiv>\u003C\u002Fdiv>\n\u003Cdiv style=\"display:flex;margin:0 0 8px\">\u003Cspan style=\"color:#FF8916;margin-right:8px;flex-shrink:0;font-weight:700\">•\u003C\u002Fspan>\u003Cdiv style=\"flex:1\">\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">重塑\u003C\u002Fstrong>：透视表 pivot、宽窄表转换\u003C\u002Fdiv>\u003C\u002Fdiv>\n\u003Cdiv style=\"display:flex;margin:0 0 8px\">\u003Cspan style=\"color:#FF8916;margin-right:8px;flex-shrink:0;font-weight:700\">•\u003C\u002Fspan>\u003Cdiv style=\"flex:1\">\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">时间序列\u003C\u002Fstrong>：重采样 resample、滚动窗口\u003C\u002Fdiv>\u003C\u002Fdiv>\n\u003Cdiv style=\"display:flex;margin:0 0 8px\">\u003Cspan style=\"color:#FF8916;margin-right:8px;flex-shrink:0;font-weight:700\">•\u003C\u002Fspan>\u003Cdiv style=\"flex:1\">\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">性能优化\u003C\u002Fstrong>：大数据集的内存与速度调优\u003C\u002Fdiv>\u003C\u002Fdiv>\n\n\u003Ch1 style=\"font-size:17px;font-weight:800;color:#111;margin:26px 0 14px;padding-left:10px;border-left:5px solid #FF8916;line-height:1.5\" id=\"s3\" data-toc>三、它的工作方式（为什么代码质量高）\u003C\u002Fh1>\n\u003Cdiv style=\"background:#FAFAFA;border-radius:12px;padding:14px;margin:0 0 12px\">\u003Cdiv style=\"display:flex\">\u003Cspan style=\"background:#FF8916;color:#fff;width:22px;height:22px;border-radius:50%;font-size:13px;font-weight:700;display:flex;align-items:center;justify-content:center;flex-shrink:0;margin-right:10px\">1\u003C\u002Fspan>\u003Cdiv style=\"flex:1\">\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">先摸底\u003C\u002Fstrong>：看 dtypes、内存占用、缺失值、数据质量\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\n\u003Cdiv style=\"background:#FAFAFA;border-radius:12px;padding:14px;margin:0 0 12px\">\u003Cdiv style=\"display:flex\">\u003Cspan style=\"background:#FF8916;color:#fff;width:22px;height:22px;border-radius:50%;font-size:13px;font-weight:700;display:flex;align-items:center;justify-content:center;flex-shrink:0;margin-right:10px\">2\u003C\u002Fspan>\u003Cdiv style=\"flex:1\">\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">设计变换\u003C\u002Fstrong>：规划向量化操作、避开 for 循环、定索引策略\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\n\u003Cdiv style=\"background:#FAFAFA;border-radius:12px;padding:14px;margin:0 0 12px\">\u003Cdiv style=\"display:flex\">\u003Cspan style=\"background:#FF8916;color:#fff;width:22px;height:22px;border-radius:50%;font-size:13px;font-weight:700;display:flex;align-items:center;justify-content:center;flex-shrink:0;margin-right:10px\">3\u003C\u002Fspan>\u003Cdiv style=\"flex:1\">\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">高效实现\u003C\u002Fstrong>：向量化方法、方法链、合理索引\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\n\u003Cdiv style=\"background:#FAFAFA;border-radius:12px;padding:14px;margin:0 0 12px\">\u003Cdiv style=\"display:flex\">\u003Cspan style=\"background:#FF8916;color:#fff;width:22px;height:22px;border-radius:50%;font-size:13px;font-weight:700;display:flex;align-items:center;justify-content:center;flex-shrink:0;margin-right:10px\">4\u003C\u002Fspan>\u003Cdiv style=\"flex:1\">\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">断言校验\u003C\u002Fstrong>：用 assert 检查行数、空值数、列名，防止变换出错悄悄溜过去\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\n\n\u003Ch1 style=\"font-size:17px;font-weight:800;color:#111;margin:26px 0 14px;padding-left:10px;border-left:5px solid #FF8916;line-height:1.5\" id=\"s4\" data-toc>四、适合谁 & 边界\u003C\u002Fh1>\n\u003Cp style=\"margin:0 0 14px\">适合\u003Cstrong style=\"font-weight:700;color:#1a1a1a\">会写 Python、要把数据处理沉淀成可复用生产代码\u003C\u002Fstrong>的人——数据管道、定期报表脚本、模型前的特征工程。\u003C\u002Fp>\n\u003Cdiv style=\"background:#FFF7ED;border:1px solid #FFEDD5;border-radius:12px;padding:12px 14px;margin:0 0 16px;font-size:13px;line-height:1.7;color:#9A3412\">⚠️ 它的产出是 \u003Cstrong style=\"font-weight:700;color:#EA580C\">pandas 代码\u003C\u002Fstrong>，不是无代码点点点的工具。不想碰代码、只想传个 Excel 让 AI 直接出结果——请用「官方 xlsx」或「Anthropic 数据分析套件」。\u003C\u002Fdiv>\n\n\u003C\u002Fdiv>\n",[20,23,26,29],{"id":21,"text":22,"level":15},"s1","一、它是干嘛的",{"id":24,"text":25,"level":15},"s2","二、擅长的操作",{"id":27,"text":28,"level":15},"s3","三、它的工作方式（为什么代码质量高）",{"id":30,"text":31,"level":15},"s4","四、适合谁 & 边界",[33],{"id":5,"title":6,"coverUrl":7,"duration":8,"level":8,"publishDate":10,"views":11,"aiSoftapp":8,"tags":34,"contentType":15,"station":8,"seq":15,"relateId":8,"toolId":35,"readMinutes":16},[],"2077776805250465792",{"id":35,"createTime":37,"createdBy":38,"updateTime":37,"updatedBy":38,"echoMap":39,"slug":40,"name":41,"categorySlug":42,"subCategory":43,"region":8,"website":44,"appName":7,"description":45,"tags":46,"sortOrder":11,"state":15,"tenantCode":52,"tutorialContent":8,"resourceType":53,"accessTypes":7,"installCmd":54,"logo":55,"hotScore":56,"githubStars":11,"category":42},"2026-07-16 23:26:08","1744248479271616512",{},"pandas-pro-skill","pandas 数据处理","data","代码级数据分析","https:\u002F\u002Fgithub.com\u002FJeffallan\u002Fclaude-skills\u002Ftree\u002Fmain\u002Fskills\u002Fpandas-pro","出自 Jeff Allan 的 10.6k⭐ skill 合集（66 个全栈开发 skill）。代码级的 pandas 数据处理专家：DataFrame 清洗、聚合、多键 join、透视、时间序列重采样、NaN 插值\u002F前向填充、类型转换、大数据集性能优化。工作流强调向量化、方法链、结果断言校验（行数\u002F空值\u002F列名）。适合会写 Python、要把数据处理落成生产级代码的人。边界：产出是 pandas 代码，不是无代码工具。",[47,48,49,50,51],"社区高星·出自10.6k⭐合集","pandas\u002FDataFrame","清洗\u002F聚合\u002F透视","时间序列","性能优化","0000","skill","npx skills add Jeffallan\u002Fclaude-skills@pandas-pro","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F23423962?v=4",78]