Googlesheets Analyze Data

Google Sheets Analyze Data

When to Use

• User asks to "analyze this spreadsheet" or "summarize the numbers"
• User wants a "pivot table" or "chart"
• User asks to "highlight" or "format" data
• User wants "totals by category" or any aggregation
• User asks to set up "dropdowns" or "validation"

Tools

Discovery

• GOOGLESHEETS_SEARCH_SPREADSHEETS — Find spreadsheets by name
• GOOGLESHEETS_GET_SPREADSHEET_INFO — Get spreadsheet metadata
• GOOGLESHEETS_GET_SHEET_NAMES — List sheets within a spreadsheet
• GOOGLESHEETS_VALUES_GET — Read cell values and structure
• GOOGLESHEETS_BATCH_GET — Read multiple ranges at once

Analysis

• GOOGLESHEETS_EXECUTE_SQL — Run SQL queries against sheet data
  • SELECT, WHERE, GROUP BY, ORDER BY, SUM, AVG, COUNT, etc.

Visualization

• GOOGLESHEETS_CUSTOM_CREATE_PIVOT_TABLE — Summarize data by dimensions
  • rows: Grouping columns (e.g., ["Region", "Product"])
  • values: Aggregations [{column: "Sales", aggregation: "SUM"}]
• GOOGLESHEETS_CUSTOM_CREATE_CHART — Create visual charts
  • Types: BAR, LINE, PIE, COLUMN, AREA, SCATTER, COMBO

Formatting

• GOOGLESHEETS_CUSTOM_ADD_CONDITIONAL_FORMAT — Visual rules
  • value_based: >, <, =, contains, between
  • color_scale: Gradient across range
  • custom_formula: Advanced rules
• GOOGLESHEETS_CUSTOM_SET_DATA_VALIDATION — Input restrictions
  • dropdown_list: List of allowed values
  • dropdown_range: Values from another range
  • number: Min/max constraints
  • date: Date restrictions
  • custom_formula: Advanced validation

Sharing

• GOOGLESHEETS_CUSTOM_SHARE_SPREADSHEET — Share with users
  • recipients: Email list
  • role: reader, writer, commenter

Workflow

Step 1: Understand the Data (CRITICAL)

Before any analysis, read the data:

GOOGLESHEETS_SEARCH_SPREADSHEETS(query="sales data")

GOOGLESHEETS_GET_SHEET_NAMES(spreadsheet_id="...")

GOOGLESHEETS_VALUES_GET(
    spreadsheet_id="...",
    range="Sheet1!A1:Z5"
)

Study the results to understand:

• Column headers and their meaning
• Data types (numbers, dates, text, categories)
• Number of rows (to gauge dataset size)
• Any patterns or anomalies

Step 2: Choose the Right Analysis Tool

Step 3: Execute Analysis

SQL Query Example:

GOOGLESHEETS_EXECUTE_SQL(
    spreadsheet_id="...",
    query="SELECT Category, SUM(Amount), AVG(Amount) FROM Sheet1 GROUP BY Category ORDER BY SUM(Amount) DESC"
)

Pivot Table Example:

GOOGLESHEETS_CUSTOM_CREATE_PIVOT_TABLE(
    spreadsheet_id="...",
    source_sheet="Sales",
    rows=["Region", "Product"],
    values=[
        {"column": "Revenue", "aggregation": "SUM"},
        {"column": "Orders", "aggregation": "COUNT"}
    ]
)

Chart Example:

GOOGLESHEETS_CUSTOM_CREATE_CHART(
    spreadsheet_id="...",
    sheet_name="Sales",
    chart_type="BAR",
    title="Revenue by Region"
)

Step 4: Apply Visual Formatting

Conditional formatting:

GOOGLESHEETS_CUSTOM_ADD_CONDITIONAL_FORMAT(
    spreadsheet_id="...",
    sheet_name="Sales",
    range="D2:D100",
    condition="greater_than",
    condition_values=["1000"],
    background_color="#4CAF50"
)

Data validation (dropdowns):

GOOGLESHEETS_CUSTOM_SET_DATA_VALIDATION(
    spreadsheet_id="...",
    sheet_name="Tracker",
    range="B2:B100",
    validation_type="dropdown_list",
    values=["High", "Medium", "Low"]
)

Step 5: Present Insights

Don't just return raw data — interpret it:

• "Total revenue is $X, with Region Y contributing 45%"
• "The top 3 categories account for 80% of sales"
• "There's a clear upward trend from Q1 to Q3"

Then offer: "Want me to create a chart to visualize this?"

Step 6: Range Notation Reference

• Basic: Sheet1!A1:B10
• Entire column: Sheet1!A:A
• Entire row: Sheet1!1:1
• Names with spaces: 'My Sheet'!A1:B10
• Always include sheet name in multi-sheet spreadsheets

Important Rules

1. Understand data first — Always read structure before analyzing
2. Choose right tool — SQL for queries, pivot for summaries, charts for visuals
3. Interpret results — Present insights, not just raw numbers
4. Offer visualizations — After analysis, suggest charts when appropriate
5. Respect data — Don't modify source data unless asked; create new sheets for analysis
6. Destructive actions need consent — Deleting sheets, clearing ranges, overwriting data