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xlsx Excel spreadsheet generation using openpyxl and xlsxwriter. Convert Claude output, data analysis results, and tabular data into formatted .xlsx files with charts, formulas, conditional formatting, and multiple sheets. Use for generating Excel reports programmatically.
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More from this repository name xlsx description Excel spreadsheet generation using openpyxl and xlsxwriter. Convert Claude output, data analysis results, and tabular data into formatted .xlsx files with charts, formulas, conditional formatting, and multiple sheets. Use for generating Excel reports programmatically. version 1.0.0 model sonnet invoked_by both user_invocable true tools ["Bash","Read","Write","Edit"] best_practices ["Use openpyxl for reading/modifying existing files","Use xlsxwriter for creating new files with advanced formatting","Never mix openpyxl and xlsxwriter on the same file","Always close xlsxwriter workbooks to flush output","Use named styles for consistent formatting across sheets"] error_handling graceful streaming not_applicable verified true lastVerifiedAt "2026-03-15T00:00:00.000Z" source builtin trust_score 100 provenance_sha 4658c37a3793b006
Excel Spreadsheet Generation (xlsx)
Overview
Generate .xlsx files programmatically to export data, build reports, and create dashboards. Two primary libraries:
openpyxl — read/write/modify existing Excel files, formula support, full feature setxlsxwriter — write-only, high-performance for large datasets, richer chart/format API
When to Invoke
Skill({ skill: 'xlsx' }) when:
User asks to "export to Excel", "create a spreadsheet", or "generate a report in Excel"
Converting tabular data, query results, or analysis output to .xlsx
Building financial models, dashboards, or structured reports
Creating templated Excel outputs for recurring workflows
Installation
pip install openpyxl xlsxwriter
uv add openpyxl xlsxwriter
Quick Start (openpyxl)
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from openpyxl.utils import get_column_letter
wb = Workbook()
ws = wb.active
ws.title = "Sales Report"
headers = ["Product" , "Q1" , "Q2" , "Q3" , "Q4" , "Total" ]
for col, header in enumerate (headers, 1 ):
cell = ws.cell(row=1 , column=col, value=header)
cell.font = Font(bold=True , color="FFFFFF" )
cell.fill = PatternFill(start_color="1F497D" , fill_type="solid" )
cell.alignment = Alignment(horizontal="center" )
data = [
("Widget A" , 45000 , 52000
61000
58000
"Widget B"
32000
38000
41000
44000
"Widget C"
18000
21000
19000
23000
for
in
enumerate
2
for
in
enumerate
1
6
f"=SUM(B{row_idx} :E{row_idx} )"
for
in
max
len
str
or
""
for
in
0
4
"sales-report.xlsx"
Named Styles from openpyxl.styles import NamedStyle, Font, Border, Side, PatternFill, Alignment
def create_styles (wb ):
header_style = NamedStyle(name="header" )
header_style.font = Font(bold=True , color="FFFFFF" , size=12 )
header_style.fill = PatternFill(start_color="2E4057" , fill_type="solid" )
header_style.alignment = Alignment(horizontal="center" , vertical="center" )
thin = Side(border_style="thin" , color="000000" )
header_style.border = Border(bottom=thin)
wb.add_named_style(header_style)
currency_style = NamedStyle(name="currency" )
currency_style.number_format = '"$"#,##0.00'
wb.add_named_style(currency_style)
percent_style = NamedStyle(name="percent" )
percent_style.number_format = '0.0%'
wb.add_named_style(percent_style)
return wb
wb = create_styles(Workbook())
ws = wb.active
ws["A1" ].style = "header"
ws["B2" ].style = "currency"
Multiple Sheets def build_multi_sheet_report (data_by_region: dict , output_path: str ):
wb = Workbook()
wb.remove(wb.active)
summary_ws = wb.create_sheet("Summary" , 0 )
for region, data in data_by_region.items():
ws = wb.create_sheet(region)
for row_idx, row in enumerate (data, 1 ):
for col_idx, value in enumerate (row, 1 ):
ws.cell(row=row_idx, column=col_idx, value=value)
summary_ws.append([region, f"='{region} '!B2" ])
wb.save(output_path)
Conditional Formatting from openpyxl.formatting.rule import ColorScaleRule, DataBarRule, CellIsRule
from openpyxl.styles import PatternFill
color_scale = ColorScaleRule(
start_type="min" , start_color="63BE7B" ,
mid_type="percentile" , mid_value=50 , mid_color="FFEB84" ,
end_type="max" , end_color="F8696B"
)
ws.conditional_formatting.add("B2:E10" , color_scale)
red_fill = PatternFill(start_color="FFC7CE" , fill_type="solid" )
negative_rule = CellIsRule(operator="lessThan" , formula=["0" ], fill=red_fill)
ws.conditional_formatting.add("B2:E10" , negative_rule)
Charts (openpyxl) from openpyxl.chart import BarChart, Reference
def add_bar_chart (ws, title, data_range, categories_range, position="G1" ):
chart = BarChart()
chart.type = "col"
chart.title = title
chart.style = 10
chart.y_axis.title = "Value"
chart.x_axis.title = "Category"
chart.width = 20
chart.height = 12
data = Reference(ws, **data_range)
cats = Reference(ws, **categories_range)
chart.add_data(data, titles_from_data=True )
chart.set_categories(cats)
ws.add_chart(chart, position)
return chart
High-Performance with xlsxwriter (Large Datasets) import xlsxwriter
def export_large_dataset (rows: list [list ], output_path: str , sheet_name="Data" ):
"""Use xlsxwriter for 10K+ row exports — significantly faster than openpyxl."""
workbook = xlsxwriter.Workbook(output_path)
worksheet = workbook.add_worksheet(sheet_name)
header_fmt = workbook.add_format({
"bold" : True , "bg_color" : "#1F497D" , "font_color" : "#FFFFFF" ,
"border" : 1 , "align" : "center"
})
money_fmt = workbook.add_format({"num_format" : "$#,##0.00" })
pct_fmt = workbook.add_format({"num_format" : "0.0%" })
date_fmt = workbook.add_format({"num_format" : "yyyy-mm-dd" })
headers = rows[0 ] if rows else []
for col, header in enumerate (headers):
worksheet.write(0 , col, header, header_fmt)
for row_idx, row in enumerate (rows[1 :], 1 ):
worksheet.write_row(row_idx, 0 , row)
worksheet.autofilter(0 , 0 , len (rows) - 1 , len (headers) - 1 )
worksheet.freeze_panes(1 , 0 )
workbook.close()
return output_path
Agent Workflow: Claude Output → Excel from dataclasses import dataclass
from typing import Any
@dataclass
class SheetSpec :
name: str
headers: list [str ]
rows: list [list [Any ]]
formats: dict [int , str ] = None
def claude_output_to_excel (sheets: list [SheetSpec], output_path: str ) -> str :
"""
Convert Claude structured output to Excel file.
sheets: List of SheetSpec objects describing each worksheet.
"""
wb = Workbook()
wb.remove(wb.active)
for spec in sheets:
ws = wb.create_sheet(spec.name)
for col, header in enumerate (spec.headers, 1 ):
cell = ws.cell(row=1 , column=col, value=header)
cell.font = Font(bold=True )
cell.fill = PatternFill(start_color="D9E1F2" , fill_type="solid" )
for row_idx, row in enumerate (spec.rows, 2 ):
for col_idx, value in enumerate (row, 1 ):
ws.cell(row=row_idx, column=col_idx, value=value)
for col in ws.columns:
max_len = max (len (str (cell.value or "" )) for cell in col)
ws.column_dimensions[get_column_letter(col[0 ].column)].width = min (max_len + 4 , 50 )
wb.save(output_path)
return output_path
from openpyxl.styles import Font
from openpyxl.utils import get_column_letter
from openpyxl import Workbook
result = claude_output_to_excel([
SheetSpec(
name="Summary" ,
headers=["Metric" , "Value" , "Change" ],
rows=[
["Revenue" , "$2.1M" , "+23%" ],
["Users" , "45,230" , "+15%" ],
["NPS" , "72" , "+8pts" ],
]
),
SheetSpec(
name="Details" ,
headers=["Date" , "Product" , "Amount" , "Region" ],
rows=[
["2026-01-15" , "Widget A" , 12500 , "North" ],
["2026-01-16" , "Widget B" , 8900 , "South" ],
]
),
], "analysis-output.xlsx" )
Choosing: openpyxl vs xlsxwriter Feature openpyxl xlsxwriter Read existing files YES No Modify existing files YES No Write new files YES YES Performance (large data) Moderate Excellent Formula support YES YES Charts YES YES (better API) Conditional formatting YES YES Tables (ListObject) YES YES Max rows (practical) ~100K Millions
Rule: Use openpyxl when reading/modifying, xlsxwriter when creating large new files.
Output Location Save generated .xlsx files to:
User-specified path, or
.claude/context/artifacts/ for agent-generated reports
Anti-Patterns
Never use openpyxl write_only mode then try to read cells back
Never forget workbook.close() with xlsxwriter — file is not flushed until close
Never use string values for numbers in Excel cells — use native Python int/float
Never build large spreadsheets cell-by-cell with openpyxl — use write_row with xlsxwriter
Never leave merged cells in auto-width calculation — causes AttributeError
Related Skills
pptx — PowerPoint generation (for visual slide presentations)data-expert — Data processing, pandas, transformation pipelinesmarkitdown-converter — Convert existing Excel files to Markdown for Claude
Memory Protocol (MANDATORY) Before starting: Read .claude/context/memory/learnings.md
After completing: Record library version gotchas or performance patterns.
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
