Xlsx

XLSX Creation, Editing, and Analysis

Step 0: Always Inspect First

Before reading or writing any unknown Excel file, run:

python skills/xlsx/scripts/inspect.py file.xlsx

This returns sheet names, merged cell ranges, the detected header row, formula count, and named ranges — essential for picking the right strategy. Check the recommendations field for immediate guidance.

Library Selection

Reading and Analysing Data

Standard read

import pandas as pd

df = pd.read_excel('file.xlsx')                          # first sheet
all_sheets = pd.read_excel('file.xlsx', sheet_name=None) # all sheets as dict

Header not at row 0 (metadata/logos above data)

inspect.py reports detected_header_row. Use it:

# inspect.py said detected_header_row: 3
df = pd.read_excel('file.xlsx', header=3)

Manual detection when uncertain:

df_peek = pd.read_excel('file.xlsx', header=None, nrows=15)
header_row = int(df_peek.notna().sum(axis=1).idxmax())
df = pd.read_excel('file.xlsx', header=header_row)

Merged cells

inspect.py flags merged ranges. Unmerge-and-fill before reading with pandas, otherwise merged cells produce NaN:

from openpyxl import load_workbook

def unmerge_and_fill(ws):
    for merge in list(ws.merged_cells.ranges):
        top_left = ws.cell(merge.min_row, merge.min_col).value
        ws.unmerge_cells(str(merge))
        for row in ws.iter_rows(merge.min_row, merge.max_row,
                                merge.min_col, merge.max_col):
            for cell in row:
                cell.value = top_left

wb = load_workbook('file.xlsx')
for ws in wb.worksheets:
    unmerge_and_fill(ws)
wb.save('file_unmerged.xlsx')

import pandas as pd
df = pd.read_excel('file_unmerged.xlsx')

Multi-level headers

df = pd.read_excel('file.xlsx', header=[0, 1])  # two header rows

Named ranges

from openpyxl import load_workbook

wb = load_workbook('file.xlsx', data_only=True)
dn = wb.defined_names['TotalRevenue']
for sheet_title, coord in dn.destinations:
    ws = wb[sheet_title]
    print(ws[coord].value)

Large files (read_only mode)

from openpyxl import load_workbook

wb = load_workbook('file.xlsx', read_only=True)
ws = wb.active
for row in ws.iter_rows(values_only=True):
    process(row)
wb.close()  # always close read_only workbooks explicitly

Two-pass pattern for calculated values + editing

Never open with data_only=True and then save — formulas are permanently destroyed.

from openpyxl import load_workbook

# Pass 1: snapshot calculated values (read-only, never saved)
wb_vals = load_workbook('file.xlsx', data_only=True)
ws_vals = wb_vals.active
values = {cell.coordinate: cell.value
          for row in ws_vals.iter_rows() for cell in row}
wb_vals.close()

# Pass 2: edit the formula workbook (formulas intact)
wb = load_workbook('file.xlsx')
ws = wb.active
# reference `values['B5']` etc. for current calc results
ws['A1'] = 'Updated'
wb.save('output.xlsx')

Writing Excel Files

Editing existing files (openpyxl)

from openpyxl import load_workbook

wb = load_workbook('existing.xlsx')
ws = wb.active  # or wb['SheetName']

ws['A1'] = 'New Value'
ws.insert_rows(2)
ws.delete_cols(3)

new_sheet = wb.create_sheet('NewSheet')
new_sheet['A1'] = 'Data'

wb.save('modified.xlsx')
# Always recalculate after editing if formulas are present:
# python skills/xlsx/scripts/recalc.py modified.xlsx

Creating new files from scratch (xlsxwriter — preferred for new files)

Use xlsxwriter when building a new file. It is faster, has richer chart/format APIs, and produces cleaner output than openpyxl for new workbooks.

import xlsxwriter

wb = xlsxwriter.Workbook('output.xlsx')
ws = wb.add_worksheet('Summary')

# Formats
header_fmt = wb.add_format({'bold': True, 'bg_color': '#505050', 'font_color': 'white'})
yellow_fmt = wb.add_format({'bg_color': '#f2e358'})
currency_fmt = wb.add_format({'num_format': '$#,##0'})

# Write headers
for col, name in enumerate(['SKU', 'Description', 'Revenue']):
    ws.write(0, col, name, header_fmt)

# Write data
data = [['SKU-001', 'Widget A', 12500], ['SKU-002', 'Widget B', 8300]]
for row_idx, row in enumerate(data, start=1):
    ws.write(row_idx, 0, row[0])
    ws.write(row_idx, 1, row[1])
    ws.write(row_idx, 2, row[2], currency_fmt)

# Formula
ws.write_formula(len(data) + 1, 2, f'=SUM(C2:C{len(data) + 1})', currency_fmt)

# Column widths
ws.set_column('A:A', 12)
ws.set_column('B:B', 30)
ws.set_column('C:C', 14)

wb.close()

Conditional formatting (xlsxwriter)

# Highlight cells above threshold
ws.conditional_format('C2:C100', {
    'type': 'cell',
    'criteria': '>',
    'value': 10000,
    'format': wb.add_format({'bg_color': '#f2e358'}),
})

# Color scale
ws.conditional_format('C2:C100', {
    'type': '3_color_scale',
    'min_color': '#ffffff',
    'mid_color': '#f6ea86',
    'max_color': '#f2e358',
})

Data validation / dropdown lists (xlsxwriter)

ws.data_validation('D2:D100', {
    'validate': 'list',
    'source': ['Active', 'Inactive', 'Pending'],
})

Freeze panes and auto-filter

ws.freeze_panes(1, 0)       # freeze row 1
ws.autofilter('A1:Z1')      # filter on header row

Conditional formatting (openpyxl — when editing existing file)

from openpyxl.formatting.rule import ColorScaleRule, CellIsRule
from openpyxl.styles import PatternFill

rule = CellIsRule(
    operator='greaterThan', formula=['10000'],
    fill=PatternFill(start_color='f2e358', end_color='f2e358', fill_type='solid')
)
ws.conditional_formatting.add('C2:C100', rule)

Data validation (openpyxl — when editing existing file)

from openpyxl.worksheet.datavalidation import DataValidation

dv = DataValidation(type='list', formula1='"Active,Inactive,Pending"', showDropDown=False)
ws.add_data_validation(dv)
dv.add('D2:D100')

Recalculating Formulas

Mandatory after any openpyxl edit that involves formulas:

python skills/xlsx/scripts/recalc.py output.xlsx

The script:

• Runs LibreOffice headless to recalculate all formulas
• Scans every cell for #REF!, #DIV/0!, #VALUE!, #N/A, #NAME?, #NUM!, #NULL!
• Returns JSON — if status is errors_found, fix the listed cells and recalc again

xlsxwriter files do not need recalc — they never contain unresolved formula strings.

CRITICAL: Use Formulas, Not Hardcoded Values

# WRONG
sheet['B10'] = df['Sales'].sum()

# CORRECT
sheet['B10'] = '=SUM(B2:B9)'

Corvera Branding

When creating spreadsheets for reports or deliverables, apply Corvera brand styling. Reference skills/corvera-brand/ for the full brand kit. Quick summary:

• Header rows: Charcoal (#505050) background, white text, Poppins font (Bold)
• Accent rows/highlights: Yellow (#f2e358) or Pale Yellow (#f6ea86) background
• Body text: Charcoal (#505050) colour, Poppins font (Regular)
• Background: White (#ffffff) or Oatmeal (#f7f0e6) for alternating rows

Charts with Spreadsheets

For visual data summaries alongside spreadsheets, reference skills/data-visualization/ for chart type selection and brand styling.

Embedded Excel charts (xlsxwriter — preferred):

chart = wb.add_chart({'type': 'column'})
chart.add_series({
    'name': 'Revenue',
    'categories': '=Summary!$A$2:$A$13',
    'values': '=Summary!$C$2:$C$13',
    'fill': {'color': '#f2e358'},
})
chart.set_title({'name': 'Monthly Revenue'})
ws.insert_chart('E2', chart)

Embedded Excel charts (openpyxl — when editing existing):

from openpyxl.chart import BarChart, Reference
chart = BarChart()
data = Reference(ws, min_col=2, min_row=1, max_row=13)
chart.add_data(data, titles_from_data=True)
chart.series[0].graphicalProperties.solidFill = 'f2e358'
ws.add_chart(chart, 'E2')

Requirements for Outputs

All Excel files

• Use Poppins if available, otherwise Arial for all deliverables
• Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)
• Study and EXACTLY match existing format, style, and conventions when modifying files

Financial Models

Color coding:

• Blue (0,0,255): Hardcoded inputs
• Black (0,0,0): Formulas and calculations
• Green (0,128,0): Links from other worksheets
• Red (255,0,0): External links to other files
• Yellow background (255,255,0): Key assumptions

Number formatting:

• Years: text strings ("2024" not "2,024")
• Currency: $#,##0 — always include units in headers ("Revenue ($mm)")
• Zeros: use $#,##0;($#,##0);- to display as -
• Percentages: 0.0%
• Multiples: 0.0x
• Negative numbers: parentheses (123) not minus -123

Formula rules:

• Place ALL assumptions in separate cells; use cell references not hardcoded values
• Comment beside hardcoded values: "Source: [System], [Date], [Reference]"

Formula Verification Checklist

• Run inspect.py before editing an unfamiliar file
• Test 2-3 sample references before building full model
• Confirm Excel columns match expected (column 64 = BL, not BK)
• Remember Excel rows are 1-indexed (DataFrame row 5 = Excel row 6)
• Check for NaN before division (pd.notna())
• Verify cross-sheet references use correct format (Sheet1!A1)
• Run recalc.py after every openpyxl edit with formulas

Code Style

• Write minimal, concise Python without unnecessary comments
• Avoid verbose variable names and redundant operations
• Add comments to cells with complex formulas or important assumptions