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Data Analysis Assistant

数据分析助手

Analyze data in spreadsheets, uncover insights, and create compelling visualizations.
分析电子表格中的数据，挖掘洞察并创建有说服力的可视化内容。

Overview

概述

This skill helps you:
  • Understand and explore your data
  • Perform statistical analysis
  • Generate insights and recommendations
  • Create charts and visualizations
  • Write formulas and queries
该技能可帮助您：
  • 理解并探索您的数据
  • 执行统计分析
  • 生成洞察与建议
  • 创建图表与可视化内容
  • 编写公式与查询语句

How to Use

使用方法

Getting Started

快速开始

  1. Share your spreadsheet or data file
  2. Describe what you want to analyze
  3. Get insights, formulas, or visualizations
  1. 分享您的电子表格或数据文件
  2. 描述您想要进行的分析内容
  3. 获取洞察、公式或可视化结果

Analysis Types

分析类型

Exploratory Analysis
"What patterns do you see in this data?"
"Give me an overview of this dataset"
"What are the key statistics?"
Specific Questions
"What was the total revenue by region?"
"Which products had the highest growth?"
"Is there a correlation between X and Y?"
Visualization Requests
"Create a chart showing sales trends"
"Make a comparison chart of Q1 vs Q2"
"Show the distribution of customer ages"
探索性分析
"您在这份数据中看到了哪些模式？"
"给我这份数据集的概况"
"关键统计数据有哪些？"
特定问题分析
"按地区划分的总营收是多少？"
"哪些产品的增长最快？"
"X和Y之间是否存在相关性？"
可视化需求
"创建一个展示销售趋势的图表"
"制作Q1与Q2的对比图表"
"展示客户年龄的分布情况"

Output Formats

输出格式

Data Overview

数据概况

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Dataset Overview

数据集概况

Rows: 1,234 Columns: 15 Date Range: Jan 2025 - Dec 2025
行数: 1,234 列数: 15 时间范围: 2025年1月 - 2025年12月

Column Summary

列摘要

ColumnTypeNon-nullUniqueSample Values
dateDate100%3652025-01-01
revenueNumber98%890$1,234.56
regionText100%5North, South
列名类型非空值占比唯一值数量样本值
date日期100%3652025-01-01
revenue数值98%890$1,234.56
region文本100%5North, South

Data Quality Issues

数据质量问题

  • rows have missing values in [column]
  • [Y] potential duplicates detected
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  • 行的[列]存在缺失值
  • [Y] 检测到潜在重复数据
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Statistical Analysis

统计分析

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Statistical Summary

统计摘要

[Metric Name]

[指标名称]

  • Mean: X
  • Median: Y
  • Std Dev: Z
  • Min/Max: A / B
  • 均值: X
  • 中位数: Y
  • 标准差: Z
  • 最小值/最大值: A / B

Key Findings

关键发现

  1. [Finding with statistical support]
  2. [Finding with statistical support]
  1. [有统计数据支持的发现]
  2. [有统计数据支持的发现]

Recommendations

建议

  • [Action based on analysis]
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  • [基于分析的行动建议]
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Insight Report

洞察报告

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Analysis Report: [Topic]

分析报告: [主题]

Executive Summary

执行摘要

[2-3 sentence overview of key findings]
[2-3句关键发现概述]

Key Metrics

核心指标

MetricValueChange
Total Revenue$X+Y%
Avg Order Value$Z-W%
指标数值变化
总营收$X+Y%
平均订单价值$Z-W%

Trends

趋势

  1. [Trend 1]: [Description with data]
  2. [Trend 2]: [Description with data]
  1. [趋势1]: [带数据的描述]
  2. [趋势2]: [带数据的描述]

Recommendations

建议

  1. [Actionable recommendation]
  2. [Actionable recommendation]
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  1. [可执行的建议]
  2. [可执行的建议]
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Common Analysis Workflows

常见分析流程

Sales Analysis

销售分析

1. "Show total sales by month"
2. "Which products are top performers?"
3. "What's the customer segment breakdown?"
4. "Compare this year vs last year"
5. "Forecast next quarter based on trends"
1. "按月份展示总销售额"
2. "哪些产品是top performers？"
3. "客户细分群体的构成是怎样的？"
4. "对比今年与去年的数据"
5. "基于趋势预测下一季度情况"

Customer Analysis

客户分析

1. "What's the customer distribution by segment?"
2. "Calculate customer lifetime value"
3. "Which customers are at risk of churning?"
4. "What's the acquisition cost vs LTV ratio?"
1. "客户按细分群体的分布情况如何？"
2. "计算客户生命周期价值"
3. "哪些客户存在流失风险？"
4. "获客成本与LTV的比率是多少？"

Financial Analysis

财务分析

1. "Calculate profit margins by product"
2. "What's the expense breakdown?"
3. "Show cash flow trends"
4. "Compare budget vs actual"
1. "按产品计算利润率"
2. "费用构成是怎样的？"
3. "展示现金流趋势"
4. "对比预算与实际数据"

Formula Generation

公式生成

Request Formulas

请求公式

"Write a formula to calculate year-over-year growth"
"Create a VLOOKUP to match customer data"
"Make a dynamic sum based on criteria"
"编写一个计算同比增长率的公式"
"创建一个VLOOKUP来匹配客户数据"
"制作一个基于条件的动态求和公式"

Formula Output

公式输出

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Formula: [Purpose]

公式: [用途]

Excel/Google Sheets

Excel/Google Sheets

excel
=SUMIFS(Sales[Amount], Sales[Region], "North", Sales[Date], ">="&DATE(2025,1,1))
excel
=SUMIFS(Sales[Amount], Sales[Region], "North", Sales[Date], ">="&DATE(2025,1,1))

Explanation

说明

  • SUMIFS
    : Sums values meeting multiple criteria
  • First argument: Column to sum
  • Subsequent pairs: Criteria column + criteria value
  • SUMIFS
    : 对满足多个条件的数值求和
  • 第一个参数: 要求和的列
  • 后续参数对: 条件列 + 条件值

Usage

使用方法

Place in cell [X] where you want the result.
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将公式放入您想要得到结果的单元格[X]中。
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Visualization Recommendations

可视化建议

Choose the Right Chart

选择合适的图表

Data TypeBest Chart
Trends over timeLine chart
Part of wholePie/Donut chart
ComparisonBar chart
DistributionHistogram
CorrelationScatter plot
GeographicMap chart
数据类型最佳图表类型
随时间变化的趋势折线图
占比情况饼图/环形图
对比数据柱状图
分布情况直方图
相关性散点图
地理数据地图图表

Chart Specifications

图表规格

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Recommended Chart: [Type]

推荐图表: [类型]

Data Series:
  • X-axis: [Column] (e.g., Date)
  • Y-axis: [Column] (e.g., Revenue)
  • Series: [Column] (e.g., Region)
Formatting:
  • Title: "[Descriptive title]"
  • Colors: Use consistent color scheme
  • Labels: Show values on data points
Chart Description: [What this chart shows and why it's useful]
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数据系列:
  • X轴: [列名]（例如：日期）
  • Y轴: [列名]（例如：营收）
  • 系列: [列名]（例如：地区）
格式设置:
  • 标题: "[描述性标题]"
  • 颜色: 使用统一的配色方案
  • 标签: 在数据点上显示数值
图表说明: [该图表展示的内容及其作用]
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Advanced Analysis

高级分析

Pivot Table Design

数据透视表设计

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Pivot Table: [Purpose]

数据透视表: [用途]

Rows: [Field 1], [Field 2] Columns: [Field 3] Values: SUM of [Field 4], AVG of [Field 5] Filters: [Field 6]
Expected Output:
RegionQ1Q2Q3Q4Total
North$X$X$X$X$X
South$X$X$X$X$X
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行: [字段1], [字段2] 列: [字段3] 值: [字段4]的求和, [字段5]的平均值 筛选器: [字段6]
预期输出:
地区Q1Q2Q3Q4总计
North$X$X$X$X$X
South$X$X$X$X$X
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Cohort Analysis

同期群分析

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Cohort Analysis

同期群分析

Cohort Definition: Customers grouped by [first purchase month] Metric: [Retention rate / Revenue / etc.] Time Period: [12 months]
CohortM0M1M2M3...
Jan 25100%45%32%28%...
Feb 25100%48%35%30%...
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同期群定义: 按[首次购买月份]分组的客户 指标: [留存率 / 营收 / 等] 时间周期: [12个月]
同期群M0M1M2M3...
Jan 25100%45%32%28%...
Feb 25100%48%35%30%...
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Best Practices

最佳实践

For Better Analysis

优化分析效果

  1. Clean data first: Handle missing values, duplicates
  2. Define metrics clearly: What exactly are you measuring?
  3. Consider context: Industry benchmarks, seasonality
  4. Validate findings: Cross-check with other data sources
  1. 先清理数据: 处理缺失值、重复数据
  2. 明确定义指标: 您要衡量的具体内容是什么？
  3. 考虑上下文: 行业基准、季节性因素
  4. 验证发现: 通过其他数据源交叉验证

For Better Visualizations

优化可视化效果

  1. Keep it simple: One main message per chart
  2. Label clearly: Title, axes, legend
  3. Use appropriate scale: Don't truncate misleadingly
  4. Consider colorblind users: Use patterns or distinct colors
  1. 保持简洁: 每个图表只传递一个核心信息
  2. 清晰标注: 标题、坐标轴、图例
  3. 使用合适的刻度: 不要通过截断刻度来误导
  4. 考虑色弱用户: 使用图案或区分度高的颜色

Limitations

局限性

  • Cannot directly execute code on your data
  • Large datasets may need sampling
  • Complex statistical models need specialized tools
  • Real-time data requires live connections
  • Cannot guarantee 100% accuracy on OCR'd data
  • 无法直接在您的数据上执行代码
  • 大型数据集可能需要抽样处理
  • 复杂的统计模型需要专用工具
  • 实时数据需要实时连接
  • 无法保证OCR识别的数据100%准确