Excel Automation

import xlwings as xw
# Connect to active Excel workbookwb = xw.Book.caller()  # From Excel add-inwb = xw.books.active   # Active workbook
# Open specific filewb = xw.Book('path/to/file.xlsx')
# Create new workbookwb = xw.Book()
# Get sheetsheet = wb.sheets['Sheet1']sheet = wb.sheets[0]

# Single cellsheet['A1'].value = 'Hello'value = sheet['A1'].value
# Rangesheet['A1:C3'].value = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]data = sheet['A1:C3'].value  # Returns list of lists
# Named rangesheet['MyRange'].value = 'Named data'
# Expand range (detect data boundaries)sheet['A1'].expand().value  # All connected datasheet['A1'].expand('table').value  # Table format

# Current region (like Ctrl+Shift+End)data = sheet['A1'].current_region.value
# Used rangeused = sheet.used_range.value
# Last row with datalast_row = sheet['A1'].end('down').row
# Resize rangerng = sheet['A1'].resize(10, 5)  # 10 rows, 5 columns

# Fontsheet['A1'].font.bold = Truesheet['A1'].font.size = 14sheet['A1'].font.color = (255, 0, 0)  # RGB red
# Fillsheet['A1'].color = (255, 255, 0)  # Yellow background
# Number formatsheet['B1'].number_format = '$#,##0.00'
# Column widthsheet['A:A'].column_width = 20
# Row heightsheet['1:1'].row_height = 30
# Autofitsheet['A:D'].autofit()

# Add chartchart = sheet.charts.add(left=100, top=100, width=400, height=250)chart.set_source_data(sheet['A1:B10'])chart.chart_type = 'column_clustered'chart.name = 'Sales Chart'
# Modify existing chartchart = sheet.charts['Sales Chart']chart.chart_type = 'line'

# Create Excel Tablerng = sheet['A1'].expand()table = sheet.tables.add(source=rng, name='SalesTable')
# Refresh tabletable.refresh()
# Access table datatable_data = table.data_body_range.value

# Add picturesheet.pictures.add('logo.png', left=10, top=10, width=100, height=50)
# Update picture from matplotlibimport matplotlib.pyplot as pltfig, ax = plt.subplots()ax.plot([1, 2, 3], [1, 4, 9])sheet.pictures.add(fig, name='MyPlot', update=True)

# Run VBA macrowb.macro('MacroName')()
# With argumentswb.macro('MyMacro')('arg1', 'arg2')
# Get return valueresult = wb.macro('CalculateTotal')(100, 200)
# Access VBA modulevb_code = wb.api.VBProject.VBComponents('Module1').CodeModule.Lines(1, 10)

# Define a UDF (in Python file)import xlwings as xw
@xw.funcdef my_sum(x, y):    """Add two numbers"""    return x + y
@xw.func@xw.arg('data', ndim=2)def my_array_func(data):    """Process array data"""    import numpy as np    return np.sum(data)
# These become Excel functions: =my_sum(A1, B1)

# Excel application settingsapp = xw.apps.activeapp.screen_updating = False  # Speed upapp.calculation = 'manual'   # Manual calcapp.display_alerts = False   # Suppress dialogs
# Perform operations...
# Restoreapp.screen_updating = Trueapp.calculation = 'automatic'app.display_alerts = True

import xlwings as xw
def batch_update(data, workbook_path):    app = xw.App(visible=False)    try:        app.screen_updating = False        app.calculation = 'manual'                wb = app.books.open(workbook_path)        sheet = wb.sheets['Data']                # Write all data at once        sheet['A1'].value = data                app.calculation = 'automatic'        wb.save()    finally:        wb.close()        app.quit()

def update_dashboard(data_dict):    wb = xw.books.active        # Update data sheet    data_sheet = wb.sheets['Data']    for name, values in data_dict.items():        data_sheet[name].value = values        # Refresh all charts    dashboard = wb.sheets['Dashboard']    for chart in dashboard.charts:        chart.refresh()        # Update timestamp    from datetime import datetime    dashboard['A1'].value = f'Last Updated: {datetime.now()}'

def generate_monthly_report(month, data):    template = xw.Book('template.xlsx')        # Fill data    sheet = template.sheets['Report']    sheet['B2'].value = month    sheet['A5'].value = data        # Run calculations    template.app.calculate()        # Export to PDF    sheet.api.ExportAsFixedFormat(0, f'report_{month}.pdf')        template.save(f'report_{month}.xlsx')

import xlwings as xwimport pandas as pdfrom datetime import datetime
# Connect to running Excelwb = xw.books.activedashboard = wb.sheets['Dashboard']data_sheet = wb.sheets['Data']
# Fetch new data (simulated)new_data = pd.DataFrame({    'Date': pd.date_range('2024-01-01', periods=30),    'Sales': [1000 + i*50 for i in range(30)],    'Costs': [600 + i*30 for i in range(30)]})
# Update data sheetdata_sheet['A1'].value = new_data
# Calculate profitdata_sheet['D1'].value = 'Profit'data_sheet['D2'].value = '=B2-C2'data_sheet['D2'].expand('down').value = data_sheet['D2'].formula
# Update KPIs on dashboarddashboard['B2'].value = new_data['Sales'].sum()dashboard['B3'].value = new_data['Costs'].sum()dashboard['B4'].value = new_data['Sales'].sum() - new_data['Costs'].sum()dashboard['A1'].value = f'Updated: {datetime.now().strftime("%Y-%m-%d %H:%M")}'
# Refresh chartsfor chart in dashboard.charts:    chart.api.Refresh()
print("Dashboard updated!")

import xlwings as xwfrom pathlib import Path
def process_sales_files(folder_path, output_path):    """Consolidate multiple Excel files into one summary."""        app = xw.App(visible=False)    app.screen_updating = False        try:        # Create summary workbook        summary_wb = xw.Book()        summary_sheet = summary_wb.sheets[0]        summary_sheet.name = 'Consolidated'                headers = ['File', 'Total Sales', 'Total Units', 'Avg Price']        summary_sheet['A1'].value = headers                row = 2        for file in Path(folder_path).glob('*.xlsx'):            wb = app.books.open(str(file))            data_sheet = wb.sheets['Sales']                        # Extract summary            total_sales = data_sheet['B:B'].api.SpecialCells(11).Value  # xlCellTypeConstants            total_units = data_sheet['C:C'].api.SpecialCells(11).Value                        # Calculate and write            summary_sheet[f'A{row}'].value = file.name            summary_sheet[f'B{row}'].value = sum(total_sales) if isinstance(total_sales, (list, tuple)) else total_sales            summary_sheet[f'C{row}'].value = sum(total_units) if isinstance(total_units, (list, tuple)) else total_units            summary_sheet[f'D{row}'].value = f'=B{row}/C{row}'                        wb.close()            row += 1                # Format summary        summary_sheet['A1:D1'].font.bold = True        summary_sheet['B:D'].number_format = '$#,##0.00'        summary_sheet['A:D'].autofit()                summary_wb.save(output_path)            finally:        app.quit()        print(f"Consolidated {row-2} files to {output_path}")
# Usageprocess_sales_files('/path/to/sales/', 'consolidated_sales.xlsx')

# myudfs.py - Place in xlwings project
import xlwings as xwimport numpy as np
@xw.func@xw.arg('data', pd.DataFrame, index=False, header=False)@xw.ret(expand='table')def GROWTH_RATE(data):    """Calculate period-over-period growth rate"""    values = data.iloc[:, 0].values    growth = np.diff(values) / values[:-1] * 100    return [['Growth %']] + [[g] for g in growth]
@xw.func@xw.arg('range1', np.array, ndim=2)@xw.arg('range2', np.array, ndim=2)def CORRELATION(range1, range2):    """Calculate correlation between two ranges"""    return np.corrcoef(range1.flatten(), range2.flatten())[0, 1]
@xw.funcdef SENTIMENT(text):    """Basic sentiment analysis (placeholder)"""    positive = ['good', 'great', 'excellent', 'amazing']    negative = ['bad', 'poor', 'terrible', 'awful']        text_lower = text.lower()    pos_count = sum(word in text_lower for word in positive)    neg_count = sum(word in text_lower for word in negative)        if pos_count > neg_count:        return 'Positive'    elif neg_count > pos_count:        return 'Negative'    return 'Neutral'