Analyzing Spreadsheets

When to use

• •User shares an Excel workbook or asks about spreadsheet analysis
• •Tasks include summarizing metrics, spotting anomalies, or drafting charts
• •Data lives in tabular form (CSV or XLSX)

Workflow

1. •Inspect workbook structure
   python

   import pandas as pd
   xl = pd.ExcelFile("input.xlsx")
   xl.sheet_names
   

2. •Load relevant sheets
   python

   df = pd.read_excel("input.xlsx", sheet_name="Sheet1")
   df.head()
   

3. •Clean and validate
   • •Drop empty columns/rows
   • •Normalize date formats with pd.to_datetime
   • •Verify numeric columns with df.describe()
4. •Analyze and summarize
   • •Use groupby/pivot patterns from reference/pandas-recipes.md
   • •Highlight KPIs, trends, and outliers
5. •Recommend visuals
   • •Suggest chart types (line for time series, bar for categorical comparisons, heatmap for correlations)
   • •Provide short rationale per recommendation

Output expectations

• •Concise summary (1–3 paragraphs) covering key findings
• •Bullet list of insights with supporting numbers
• •Optional chart suggestions with column mappings

Validation checklist

• • Loaded the correct sheet(s) and reported row/column counts
• • Highlighted missing or unusual data
• • Referenced actual values from the workbook
• • Included next-step recommendations (e.g., further slicing, charting)

Additional resources

• •reference/pandas-recipes.md – common aggregation patterns
• •python -m pip install pandas openpyxl – install requirements if missing (Claude Code already includes pandas)