xlsxturbo 0.10.6

High-performance Excel writer with automatic type detection (pandas, polars, CSV)

xlsxturbo

High-performance Excel writer with automatic type detection. Written in Rust, usable from Python.

Features

• Direct DataFrame support for pandas and polars
• Excel tables - filterable tables with 61 built-in styles (banded rows, autofilter)
• Conditional formatting - color scales, data bars, icon sets for visual data analysis
• Formula columns - add calculated columns with Excel formulas
• Merged cells - merge cell ranges for headers and titles
• Hyperlinks - add clickable links to cells
• Comments/Notes - add cell annotations with optional author
• Data validation - dropdowns, number ranges, text length constraints
• Rich text - multiple formats within a single cell
• Images - embed PNG, JPEG, GIF, BMP in cells
• Auto-fit columns - automatically adjust column widths to fit content
• Custom column widths - set specific widths per column or cap all with _all
• Header styling - bold, colors, font size for header row
• Named tables - set custom table names
• Custom row heights - set specific heights per row
• Freeze panes - freeze header row for easier scrolling
• Multi-sheet workbooks - write multiple DataFrames to one file
• Per-sheet options - override settings per sheet in multi-sheet workbooks
• Constant memory mode - minimize RAM usage for very large files
• Parallel CSV processing - optional multi-core parsing for large files
• Automatic type detection from CSV strings and Python objects:
  • Integers and floats → Excel numbers
  • true/false → Excel booleans
  • Dates (2024-01-15, 15/01/2024, etc.) → Excel dates with formatting
  • Datetimes (ISO 8601) → Excel datetimes
  • NaN/Inf → Empty cells (graceful handling)
  • Everything else → Text
• ~6x faster than pandas + openpyxl (see benchmarks)
• Memory efficient - streams data with 1MB buffer
• Available as both Python library and CLI tool

Installation

pip install xlsxturbo

Or build from source:

pip install maturin
maturin develop --release

Python Usage

DataFrame Export (pandas/polars)

import xlsxturbo
import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'name': ['Alice', 'Bob'],
    'age': [30, 25],
    'salary': [50000.50, 60000.75],
    'active': [True, False]
})

# Export to XLSX (preserves types: int, float, bool, date, datetime)
rows, cols = xlsxturbo.df_to_xlsx(df, "output.xlsx")
print(f"Wrote {rows} rows and {cols} columns")

# Works with polars too!
import polars as pl
df_polars = pl.DataFrame({'x': [1, 2, 3], 'y': [4.0, 5.0, 6.0]})
xlsxturbo.df_to_xlsx(df_polars, "polars_output.xlsx", sheet_name="Data")

Excel Tables with Styling

import xlsxturbo
import pandas as pd

df = pd.DataFrame({
    'Product': ['Widget A', 'Widget B', 'Widget C'],
    'Price': [19.99, 29.99, 39.99],
    'Quantity': [100, 75, 50],
})

# Create a styled Excel table with autofilter, banded rows, and auto-fit columns
xlsxturbo.df_to_xlsx(df, "report.xlsx",
    table_style="Medium9",   # Excel's default table style
    autofit=True,            # Fit column widths to content
    freeze_panes=True        # Freeze header row for scrolling
)

# Available styles: Light1-Light21, Medium1-Medium28, Dark1-Dark11
xlsxturbo.df_to_xlsx(df, "dark_table.xlsx", table_style="Dark1", autofit=True)

Custom Column Widths and Row Heights

import xlsxturbo
import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Department': ['Engineering', 'Marketing', 'Sales'],
    'Salary': [75000, 65000, 55000]
})

# Set specific column widths (column index -> width in characters)
xlsxturbo.df_to_xlsx(df, "report.xlsx", 
    column_widths={0: 20, 1: 25, 2: 15}
)

# Set specific row heights (row index -> height in points)
xlsxturbo.df_to_xlsx(df, "report.xlsx",
    row_heights={0: 25}  # Make header row taller
)

# Combine with other options
xlsxturbo.df_to_xlsx(df, "styled.xlsx",
    table_style="Medium9",
    freeze_panes=True,
    column_widths={0: 20, 1: 30, 2: 15},
    row_heights={0: 22}
)

Global Column Width Cap

Use column_widths={'_all': value} to cap all columns at a maximum width:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'VeryLongDescription': ['A' * 100, 'B' * 100],
    'Score': [95, 87]
})

# Cap all columns at 30 characters
xlsxturbo.df_to_xlsx(df, "capped.xlsx", column_widths={'_all': 30})

# Mix specific widths with global cap (specific overrides '_all')
xlsxturbo.df_to_xlsx(df, "mixed.xlsx", column_widths={0: 15, '_all': 30})

# Autofit with cap: fit content, but never exceed 25 characters
xlsxturbo.df_to_xlsx(df, "fitted.xlsx", autofit=True, column_widths={'_all': 25})

Named Excel Tables

Set custom names for Excel tables:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({'Product': ['A', 'B'], 'Price': [10, 20]})

# Name the Excel table
xlsxturbo.df_to_xlsx(df, "report.xlsx", 
    table_style="Medium2", 
    table_name="ProductPrices"
)

# Invalid characters are auto-sanitized, digits get underscore prefix
xlsxturbo.df_to_xlsx(df, "report.xlsx",
    table_style="Medium2",
    table_name="2024 Sales Data!"  # Becomes "_2024_Sales_Data_"
)

Header Styling

Apply custom formatting to header cells:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Score': [95, 87]})

# Bold headers
xlsxturbo.df_to_xlsx(df, "bold.xlsx", header_format={'bold': True})

# Full styling with colors
xlsxturbo.df_to_xlsx(df, "styled.xlsx", header_format={
    'bold': True,
    'bg_color': '#4F81BD',   # Blue background
    'font_color': 'white'    # White text
})

# Available options:
# - bold (bool): Bold text
# - italic (bool): Italic text
# - font_color (str): '#RRGGBB' or named color (white, black, red, blue, etc.)
# - bg_color (str): Background color
# - font_size (float): Font size in points
# - underline (bool): Underlined text

Column Formatting

Apply formatting to data columns using pattern matching:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({
    'product_id': [1, 2, 3],
    'product_name': ['Widget A', 'Widget B', 'Widget C'],
    'price_usd': [19.99, 29.99, 39.99],
    'price_eur': [17.99, 26.99, 35.99],
    'quantity': [100, 75, 50]
})

# Format columns by pattern
xlsxturbo.df_to_xlsx(df, "report.xlsx", column_formats={
    'price_*': {'num_format': '$#,##0.00', 'bg_color': '#E8F5E9'},  # All price columns
    'quantity': {'bold': True}  # Exact match
})

# Wildcard patterns:
# - 'prefix*' matches columns starting with 'prefix'
# - '*suffix' matches columns ending with 'suffix'
# - '*contains*' matches columns containing 'contains'
# - 'exact' matches column name exactly

# Available format options:
# - bg_color (str): Background color ('#RRGGBB' or named)
# - font_color (str): Text color
# - num_format (str): Excel number format ('0.00', '#,##0', '0.00%', etc.)
# - bold (bool): Bold text
# - italic (bool): Italic text
# - underline (bool): Underlined text
# - border (bool): Add thin border

# First matching pattern wins (order preserved)
xlsxturbo.df_to_xlsx(df, "report.xlsx", column_formats={
    'price_usd': {'bg_color': '#FFEB3B'},  # Specific: yellow for USD
    'price_*': {'bg_color': '#E3F2FD'}     # General: blue for other prices
})

Multi-Sheet Workbooks

import xlsxturbo
import pandas as pd

# Write multiple DataFrames to separate sheets
df1 = pd.DataFrame({'product': ['A', 'B'], 'sales': [100, 200]})
df2 = pd.DataFrame({'region': ['East', 'West'], 'total': [500, 600]})

xlsxturbo.dfs_to_xlsx([
    (df1, "Products"),
    (df2, "Regions")
], "report.xlsx")

# With styling applied to all sheets
xlsxturbo.dfs_to_xlsx([
    (df1, "Products"),
    (df2, "Regions")
], "styled_report.xlsx", table_style="Medium2", autofit=True, freeze_panes=True)

# With column widths applied to all sheets
xlsxturbo.dfs_to_xlsx([
    (df1, "Products"),
    (df2, "Regions")
], "report.xlsx", column_widths={0: 20, 1: 15})

Per-Sheet Options

Override global settings for individual sheets using a 3-tuple with options dict:

import xlsxturbo
import pandas as pd

df_data = pd.DataFrame({'Product': ['A', 'B'], 'Price': [10, 20]})
df_instructions = pd.DataFrame({'Step': [1, 2], 'Action': ['Open file', 'Review data']})

# Different settings per sheet:
# - "Data" sheet: has header, table style, autofit
# - "Instructions" sheet: no header (raw data), no table style
xlsxturbo.dfs_to_xlsx([
    (df_data, "Data", {"header": True, "table_style": "Medium2"}),
    (df_instructions, "Instructions", {"header": False, "table_style": None})
], "report.xlsx", autofit=True)

# Old 2-tuple API still works - uses global defaults
xlsxturbo.dfs_to_xlsx([
    (df_data, "Sheet1"),  # Uses global header=True, table_style=None
    (df_instructions, "Sheet2", {"header": False})  # Override just header
], "mixed.xlsx", header=True, autofit=True)

Available per-sheet options:

• header (bool): Include column names as header row
• autofit (bool): Automatically adjust column widths
• table_style (str|None): Excel table style or None to disable
• freeze_panes (bool): Freeze header row
• column_widths (dict): Custom column widths
• row_heights (dict): Custom row heights
• table_name (str): Custom Excel table name
• header_format (dict): Header cell styling
• column_formats (dict): Column formatting with pattern matching
• conditional_formats (dict): Conditional formatting (color scales, data bars, icons)
• formula_columns (dict): Calculated columns with Excel formulas (column name -> formula template)
• merged_ranges (list): List of (range, text) or (range, text, format) tuples to merge cells
• hyperlinks (list): List of (cell, url) or (cell, url, display_text) tuples to add clickable links
• comments (dict): Cell comments/notes (cell_ref -> text or {text, author})
• validations (dict): Data validation rules (column name/pattern -> validation config)
• rich_text (dict): Rich text with multiple formats (cell_ref -> list of segments)
• images (dict): Embedded images (cell_ref -> path or {path, scale_width, scale_height, alt_text})

Conditional Formatting

Apply visual formatting based on cell values:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'score': [95, 72, 88, 45],
    'progress': [0.9, 0.5, 0.75, 0.3],
    'status': [3, 2, 3, 1]
})

xlsxturbo.df_to_xlsx(df, "report.xlsx",
    autofit=True,
    conditional_formats={
        # 2-color gradient: red (low) to green (high)
        'score': {
            'type': '2_color_scale',
            'min_color': '#FF6B6B',
            'max_color': '#51CF66'
        },
        # Data bars: in-cell bar chart
        'progress': {
            'type': 'data_bar',
            'bar_color': '#339AF0',
            'solid': True  # Solid fill instead of gradient
        },
        # Icon set: traffic lights
        'status': {
            'type': 'icon_set',
            'icon_type': '3_traffic_lights'
        }
    }
)

Supported conditional format types:

Type	Options
2_color_scale	min_color, max_color
3_color_scale	min_color, mid_color, max_color
data_bar	bar_color, border_color, solid, direction
icon_set	icon_type, reverse, icons_only

Available icon types:

• 3 icons: 3_arrows, 3_arrows_gray, 3_flags, 3_traffic_lights, 3_traffic_lights_rimmed, 3_signs, 3_symbols, 3_symbols_uncircled
• 4 icons: 4_arrows, 4_arrows_gray, 4_traffic_lights, 4_rating
• 5 icons: 5_arrows, 5_arrows_gray, 5_quarters, 5_rating

Column patterns work with conditional formats:

# Apply data bars to all columns starting with "price_"
conditional_formats={'price_*': {'type': 'data_bar', 'bar_color': '#9B59B6'}}

Formula Columns

Add calculated columns to your Excel output. Formulas are written after data columns and use {row} as a placeholder for the row number:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({
    'price': [100, 200, 150],
    'quantity': [5, 3, 8],
    'tax_rate': [0.1, 0.1, 0.2]
})

xlsxturbo.df_to_xlsx(df, "sales.xlsx",
    autofit=True,
    formula_columns={
        'Subtotal': '=A{row}*B{row}',      # price * quantity
        'Tax': '=D{row}*C{row}',            # subtotal * tax_rate
        'Total': '=D{row}+E{row}'           # subtotal + tax
    }
)

Formula columns appear after data columns (A=price, B=quantity, C=tax_rate, D=Subtotal, E=Tax, F=Total).

Notes:

• {row} is replaced with the Excel row number (1-based, starting at 2 for data rows when header=True)
• Formula columns inherit header formatting if specified
• Column order is preserved (first formula = first new column)
• Works with both df_to_xlsx and dfs_to_xlsx (global or per-sheet)

Merged Cells

Merge cell ranges to create headers, titles, or grouped labels:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({
    'product': ['Widget A', 'Widget B'],
    'sales': [1500, 2300],
    'revenue': [7500, 11500]
})

# Merge cells for a title above the data
xlsxturbo.df_to_xlsx(df, "report.xlsx",
    header=True,
    merged_ranges=[
        # Simple merge with text (auto-centered)
        ('A1:C1', 'Q4 Sales Report'),
        # Merge with custom formatting
        ('A2:C2', 'Regional Data', {
            'bold': True,
            'bg_color': '#4F81BD',
            'font_color': 'white'
        })
    ]
)

Merged range format:

• Tuple of (range, text) or (range, text, format_dict)
• Range uses Excel notation: 'A1:D1', 'B3:B10', etc.
• Format options same as header_format: bold, italic, font_color, bg_color, font_size, underline

Notes:

• Merged cells are applied after data is written, so plan row positions accordingly
• When using with header=True, data starts at row 2 (Excel row 2)
• Works with both df_to_xlsx and dfs_to_xlsx (global or per-sheet)

Hyperlinks

Add clickable links to cells:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({
    'company': ['Anthropic', 'Google', 'Microsoft'],
    'product': ['Claude', 'Gemini', 'Copilot'],
})

# Add hyperlinks to a new column (D) after the data columns (A, B, C with header)
xlsxturbo.df_to_xlsx(df, "companies.xlsx",
    autofit=True,
    hyperlinks=[
        # Header for the links column
        ('C1', 'https://example.com', 'Website'),
        # Links with company names as display text
        ('C2', 'https://anthropic.com', 'anthropic.com'),
        ('C3', 'https://google.com', 'google.com'),
        ('C4', 'https://microsoft.com', 'microsoft.com'),
    ]
)

Hyperlink format:

• Tuple of (cell, url) or (cell, url, display_text)
• Cell uses Excel notation: 'A1', 'B5', etc.
• Display text is optional; if omitted, the URL is shown

Notes:

• Hyperlinks write to the specified cell position (overwrites existing content)
• To add a "links column", target cells beyond your DataFrame columns (as shown above)
• Works with both df_to_xlsx and dfs_to_xlsx (global or per-sheet)
• Not available in constant memory mode

Comments/Notes

Add cell annotations (hover to view):

import xlsxturbo
import pandas as pd

df = pd.DataFrame({
    'product': ['Widget A', 'Widget B'],
    'price': [19.99, 29.99]
})

xlsxturbo.df_to_xlsx(df, "report.xlsx",
    comments={
        # Simple text comment
        'A1': 'This column contains product names',
        # Comment with author
        'B1': {'text': 'Prices in USD', 'author': 'Finance Team'}
    }
)

Comment format:

• Simple: {'A1': 'Note text'}
• With author: {'A1': {'text': 'Note text', 'author': 'Name'}}

Notes:

• Comments appear as small red triangles in the cell corner
• Hover over the cell to see the comment
• Works with both df_to_xlsx and dfs_to_xlsx (global or per-sheet)
• Not available in constant memory mode

Data Validation

Add dropdowns and input constraints:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({
    'status': ['Open', 'Closed'],
    'score': [85, 92],
    'price': [19.99, 29.99],
    'code': ['ABC', 'XYZ']
})

xlsxturbo.df_to_xlsx(df, "validated.xlsx",
    validations={
        # Dropdown list
        'status': {
            'type': 'list',
            'values': ['Open', 'Closed', 'Pending', 'Review']
        },
        # Whole number range (0-100)
        'score': {
            'type': 'whole_number',
            'min': 0,
            'max': 100,
            'error_title': 'Invalid Score',
            'error_message': 'Score must be between 0 and 100'
        },
        # Decimal range
        'price': {
            'type': 'decimal',
            'min': 0.0,
            'max': 999.99
        },
        # Text length constraint
        'code': {
            'type': 'text_length',
            'min': 3,
            'max': 10
        }
    }
)

Validation types:

Type	Aliases	Description	Options
list	-	Dropdown menu	values (list of strings, max 255 chars total)
whole_number	whole, integer	Integer range	min, max
decimal	number	Decimal range	min, max
text_length	textlength, length	Character count	min, max

Optional message options:

• input_title, input_message: Prompt shown when cell is selected
• error_title, error_message: Message shown when invalid data is entered

Notes:

• Validations apply to the data rows of the specified column
• Column patterns work: 'score_*': {...} matches all columns starting with score_
• If only min or only max is specified, the other defaults to the type's extreme value
• List validation values are limited to 255 total characters (Excel limitation)
• Works with both df_to_xlsx and dfs_to_xlsx (global or per-sheet)
• Not available in constant memory mode

Rich Text

Multiple formats within a single cell:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})

xlsxturbo.df_to_xlsx(df, "rich.xlsx",
    rich_text={
        'D1': [
            ('Important: ', {'bold': True, 'font_color': 'red'}),
            'Please review ',
            ('all', {'italic': True}),
            ' values'
        ],
        'D2': [
            ('Status: ', {'bold': True}),
            ('OK', {'font_color': 'green', 'bold': True})
        ]
    }
)

Segment format:

• Formatted: ('text', {'bold': True, 'font_color': 'blue'})
• Plain: 'plain text' (no formatting)

Available format options:

• bold (bool)
• italic (bool)
• font_color (str): '#RRGGBB' or named color
• bg_color (str): Background color
• font_size (float)
• underline (bool)

Notes:

• Rich text writes to the specified cell position (overwrites existing content)
• Works with both df_to_xlsx and dfs_to_xlsx (global or per-sheet)
• Not available in constant memory mode

Images

Embed images in cells:

import xlsxturbo
import pandas as pd

df = pd.DataFrame({'Product': ['Widget A', 'Widget B'], 'Price': [19.99, 29.99]})

xlsxturbo.df_to_xlsx(df, "catalog.xlsx",
    autofit=True,
    images={
        # Simple path
        'C2': 'images/widget_a.png',
        # With options
        'C3': {
            'path': 'images/widget_b.png',
            'scale_width': 0.5,
            'scale_height': 0.5,
            'alt_text': 'Widget B photo'
        }
    }
)

Image format:

• Simple: {'C2': 'path/to/image.png'}
• With options: {'C2': {'path': '...', 'scale_width': 0.5, ...}}

Available options:

• path (str, required): Path to image file
• scale_width (float): Width scale factor (1.0 = original)
• scale_height (float): Height scale factor (1.0 = original)
• alt_text (str): Alternative text for accessibility

Supported formats: PNG, JPEG, GIF, BMP

Notes:

• Images are positioned at the specified cell (overlays any existing content)
• Image file must exist; non-existent files will raise an error
• Works with both df_to_xlsx and dfs_to_xlsx (global or per-sheet)
• Not available in constant memory mode

Constant Memory Mode (Large Files)

For very large files (millions of rows), use constant_memory=True to minimize RAM usage:

import xlsxturbo
import polars as pl

# Generate a large DataFrame
large_df = pl.DataFrame({
    'id': range(1_000_000),
    'value': [i * 1.5 for i in range(1_000_000)]
})

# Use constant_memory mode for large files
xlsxturbo.df_to_xlsx(large_df, "big_file.xlsx", constant_memory=True)

# Also works with dfs_to_xlsx
xlsxturbo.dfs_to_xlsx([
    (large_df, "Data")
], "multi_sheet.xlsx", constant_memory=True)

Note: Constant memory mode disables some features that require random access:

• table_style (Excel tables)
• freeze_panes
• row_heights
• conditional_formats
• merged_ranges
• hyperlinks
• comments
• validations
• rich_text
• images
• autofit
• formula_columns

Column widths still work in constant memory mode.

CSV Conversion

import xlsxturbo

# Convert CSV to XLSX with automatic type detection
rows, cols = xlsxturbo.csv_to_xlsx("input.csv", "output.xlsx")
print(f"Converted {rows} rows and {cols} columns")

# Custom sheet name
xlsxturbo.csv_to_xlsx("data.csv", "report.xlsx", sheet_name="Sales Data")

# For large files (100K+ rows), use parallel processing
xlsxturbo.csv_to_xlsx("big_data.csv", "output.xlsx", parallel=True)

# Handle ambiguous dates (01-02-2024: is it Jan 2 or Feb 1?)
xlsxturbo.csv_to_xlsx("us_data.csv", "output.xlsx", date_order="us")   # January 2
xlsxturbo.csv_to_xlsx("eu_data.csv", "output.xlsx", date_order="eu")   # February 1

# date_order options:
# - "auto" (default): ISO first, then European (DMY), then US (MDY)
# - "mdy" or "us": US format (MM-DD-YYYY)
# - "dmy" or "eu": European format (DD-MM-YYYY)

CLI Usage

xlsxturbo input.csv output.xlsx [OPTIONS]

Options

• -s, --sheet-name <NAME>: Name of the Excel sheet (default: "Sheet1")
• -d, --date-order <ORDER>: Date parsing order for ambiguous dates (default: "auto")
  • auto: ISO first, then European, then US
  • mdy or us: US format (01-02-2024 = January 2)
  • dmy or eu: European format (01-02-2024 = February 1)
• -v, --verbose: Show progress information

Examples

# Basic conversion
xlsxturbo sales.csv report.xlsx

# With US date format
xlsxturbo sales.csv report.xlsx --date-order us

# With European date format and verbose output
xlsxturbo sales.csv report.xlsx -d eu -v --sheet-name "Q4 Sales"

Performance

Reference benchmark on 100,000 rows x 50 columns with mixed data types. Your results will vary by system - run the benchmark yourself (see Benchmarking).

Library	Time (s)	Rows/sec	vs xlsxturbo
xlsxturbo	6.65	15,033	1.0x
polars	25.07	3,988	3.8x
pandas + xlsxwriter	35.60	2,809	5.4x
pandas + openpyxl	38.85	2,574	5.8x

Test system: Windows 11, Python 3.14, AMD Ryzen 9 (32 threads)

Type Detection Examples

CSV Value	Excel Type	Notes
123	Number	Integer
3.14159	Number	Float
true / FALSE	Boolean	Case insensitive
2024-01-15	Date	Formatted as date
2024-01-15T10:30:00	DateTime	ISO 8601 format
NaN	Empty	Graceful handling
hello world	Text	Default

Supported date formats: YYYY-MM-DD, YYYY/MM/DD, DD-MM-YYYY, DD/MM/YYYY, MM-DD-YYYY, MM/DD/YYYY

Known Limitations

• Datetime precision: Sub-second precision (microseconds) is not preserved. Datetimes are written with second-level granularity, matching Excel's practical display precision.
• Large integers: Integers exceeding 2^53 (9,007,199,254,740,992) are written as strings to prevent silent precision loss in Excel's floating-point representation.
• Validation lists: Limited to 255 total characters (Excel limitation).

Building from Source

Requires Rust toolchain and maturin:

# Install maturin
pip install maturin

# Development build
maturin develop

# Release build (optimized)
maturin develop --release

# Build wheel for distribution
maturin build --release

Benchmarking

Run the included benchmark scripts:

# Compare xlsxturbo vs other libraries (100K rows default)
python benchmarks/benchmark.py

# Full benchmark: small, medium, large datasets
python benchmarks/benchmark.py --full

# Custom size
python benchmarks/benchmark.py --rows 500000 --cols 100

# Output formats for CI/documentation
python benchmarks/benchmark.py --markdown
python benchmarks/benchmark.py --json

# Test parallel vs single-threaded CSV conversion
python benchmarks/benchmark_parallel.py

License

MIT