tenforty 2025.2

Library to compute US federal taxes, and state taxes for some states.

tenforty

Overview

tenforty is an open-source Python package designed to help demystify US federal and state tax computations. This project offers an accessible way to explore tax scenarios, compare different tax situations, and understand the impact of various factors on tax liabilities. It's particularly useful for those who would like to understand or optimize their taxes by evaluating how tax form inputs affect their outputs.

The package is built on top of the Open Tax Solver project, wrapping its functionality into a Python library.

A GPT interface to tenforty is available with a ChatGPT+ account here. This GPT, and the tenforty package itself, are discussed in a blog post here.

You can try tenforty out immediately in your browser via the included Colab notebook:

Features

• Compute US federal taxes, as well as taxes for several US states.
• Explore how taxes vary as a function of income, state, filing status, and year.
• Easily integrate with data analysis and visualization tools in Python with polars support.
• Evaluate "what if" tax scenarios efficiently and reproducibly.
• Graph backend: Optional Rust-based computation engine with automatic differentiation for exact marginal rates and Newton's method inverse solving.

Graph Backend

tenforty includes an optional graph-based computation backend built in Rust, offering automatic differentiation (autodiff) and inverse solving capabilities.

To use the graph backend, pass backend="graph" to evaluate_return or evaluate_returns:

from tenforty import evaluate_return

evaluate_return(w2_income=100_000, backend="graph")

The graph backend currently supports tax years 2024-2025 and a subset of states (California, New York, and all no-income-tax states).

Marginal Tax Rate (Autodiff)

The marginal_rate function computes exact marginal tax rates using automatic differentiation -- no finite-difference approximation needed:

from tenforty import marginal_rate

# Federal marginal rate on wages
marginal_rate(w2_income=100_000)  # => 0.22

# Marginal rate on long-term capital gains
marginal_rate(w2_income=100_000, long_term_capital_gains=50_000, wrt="long_term_capital_gains")

# Combined federal + state marginal rate
marginal_rate(w2_income=100_000, state="CA")

Inverse Solver

The solve_for_income function uses Newton's method with autodiff gradients to find the input value that produces a target tax amount:

from tenforty import solve_for_income

# What wages produce $15,000 in federal tax?
solve_for_income(target_tax=15_000)  # => ~$82,000

Interactive Demo

An interactive WebAssembly demo of the graph backend is available at mmacpherson.github.io/tenforty, featuring real-time tax curve visualization, bracket breakdowns, gradient sensitivity analysis, and the inverse solver.

Disclaimer

tenforty is an open-source tool intended for informational and educational purposes only and does not provide tax advice.

Known limitations of this package are detailed in the Limitations section below.

Installation

Requires Python 3.10+.

pip install tenforty

Main Functions Documentation

The two functions evaluate_return and evaluate_returns are the main interface to tenforty. They take exactly the same arguments, except that any of the arguments to evaluate_returns may either be a single value, or a list of values. evaluate_return is for evaluating one single return, and evaluate_returns evaluates all combinations of inputs subtended by the provided values and collects the results into a dataframe.

The inputs to either function are validated, and if for example a filing status is misspelled, you'll get an informative error message along with a list of the valid options.

Here are all arguments available for those two functions:

Argument	Type	Default	Notes
year	int	2025	2018-2025 inclusive
state	str | None	None	Two-letter state code, e.g. "CA", "NY". Several income-tax states supported; no-income-tax states (e.g. "TX", "WA") also accepted.
filing_status	str	Single	"Single", "Married/Joint", "Head_of_House", "Married/Sep", "Widow(er)"
num_dependents	int	0
standard_or_itemized	str	Standard	"Standard" or "Itemized"
w2_income	float	0.0
taxable_interest	float	0.0
qualified_dividends	float	0.0
ordinary_dividends	float	0.0
short_term_capital_gains	float	0.0
long_term_capital_gains	float	0.0
schedule_1_income	float	0.0
itemized_deductions	float	0.0
state_adjustment	float	0.0
incentive_stock_option_gains	float	0.0

The functions output these fields:

Output Field	Description
total_tax	Combined federal and state tax liability
federal_adjusted_gross_income	Federal Adjusted Gross Income (Form 1040 Line 11)
federal_effective_tax_rate	Percentage of AGI paid in federal tax
federal_tax_bracket	Marginal federal tax bracket (0-37%)
federal_taxable_income	Income subject to federal tax after deductions
federal_amt	Federal Alternative Minimum Tax
federal_total_tax	Total federal tax liability
state_adjusted_gross_income	State-level Adjusted Gross Income
state_taxable_income	Income subject to state tax after deductions
state_total_tax	Total state tax liability
state_tax_bracket	Marginal state tax bracket
state_effective_tax_rate	Percentage of state AGI paid in state tax

Examples

Here are some examples of what you can do with tenforty:

Basic Evaluation

The evaluate_return function computes the outputs for a single tax return given some inputs:

from tenforty import evaluate_return

evaluate_return(
    w2_income=100_000, state="CA", filing_status="Married/Joint", num_dependents=2
).model_dump()

This results in the following:

{'total_tax': 8484.0,
 'federal_adjusted_gross_income': 100000.0,
 'federal_effective_tax_rate': 11.4,
 'federal_tax_bracket': 12.0,
 'federal_taxable_income': 74100.0,
 'federal_amt': 0.0,
 'federal_total_tax': 8484.0,
 'state_adjusted_gross_income': 0.0,
 'state_taxable_income': 0.0,
 'state_total_tax': 0.0,
 'state_tax_bracket': 0.0,
 'state_effective_tax_rate': 0.0}

No year= argument was specified here, so the current tax year, 2025, was used. The output is a pydantic model, and we've called its .model_dump() method to show the result as a dictionary.

Creating Tax Tables: Federal/State Tax Brackets as a Function of W2 Income

The evaluate_returns method sweeps out a grid over any input arguments that are provided as lists, allowing you to evaluate a wide array of tax scenarios. Here we make a simple tax table by varying W2 income:

from tenforty import evaluate_returns

evaluate_returns(
    w2_income=list(range(50_000, 250_001, 50_000)),
    state="CA",
    filing_status="Married/Joint",
    num_dependents=2,
)[
    [
        "w2_income",
        "federal_effective_tax_rate",
        "federal_tax_bracket",
        "state_effective_tax_rate",
        "state_tax_bracket",
    ]
]

This results in a polars.DataFrame (use .to_pandas() if you need pandas compatibility):

w2_income	federal_effective_tax_rate	federal_tax_bracket	state_effective_tax_rate	state_tax_bracket
50000	10.3	12	1.5	2
100000	11.4	12	3	6
150000	14.9	22	4.6	9.3
200000	17	22	5.9	9.3
250000	18.5	24	6.6	9.3

Plot: Federal Tax as a Function of W2 Income

Since the output is a dataframe, one may readily use any of numerous visualization tools to make plots. Here we revisit the example above, evaluating a wider range of W2 incomes at finer resolution than before.

import seaborn.objects as so

df = evaluate_returns(w2_income=list(range(0, 250_001, 1_000)))

(
    so.Plot(df, x="w2_income", y="total_tax")
    .add(so.Line())
    .label(
        x="W2 Income", y="Federal Tax", title="Federal Tax as a Function of W2 Income"
    )
)

Plot: Federal Tax Over Time

The good people at Open Tax Solver have published editions each year for 21 years, so one can just as easily vary the year as any other parameter. At the moment tenforty supports back to the 2018 tax year. Here we show the federal tax on $100K of W2 income for the past five years.

import polars as pl

df = evaluate_returns(
    year=[2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025], w2_income=100_000
).cast({"year": pl.Utf8})

(
    so.Plot(df, x="year", y="total_tax")
    .add(so.Line())
    .add(so.Dot())
    .label(
        x="Year",
        y="Federal Tax",
        title="Federal Tax on $100K W2 Income Over Time",
    )
)

This one's a little melodramatic because we don't make the y-axis go to zero; it's only about a 3% drop over the years.

Plot: Impact of Long-Term Capital Gains

Because Open Tax Solver supports short- and long-term capitals gains calculations -- although, see the Limitations section below -- you can ask questions about the impact on your taxes of selling some appreciated stock this year, and show the breakdown between state and federal taxes:

import polars as pl

df = (
    evaluate_returns(
        w2_income=75_000,
        state="CA",
        long_term_capital_gains=list(range(0, 125_001, 5000)),
    )
    .select(["long_term_capital_gains", "state_total_tax", "federal_total_tax"])
    .unpivot(index="long_term_capital_gains", variable_name="Type", value_name="tax")
    .with_columns(
        pl.col("Type").replace(
            {"state_total_tax": "State", "federal_total_tax": "Federal"}
        )
    )
)

(
    so.Plot(df, x="long_term_capital_gains", y="tax", color="Type").add(
        so.Area(alpha=0.7), so.Stack()
    )
    .label(
        x="Long-Term Capital Gains",
        y="Total Tax",
        title="Impact of LTCG on Total Tax for California Resident",
    )
)

Plot: Will I Incur Alternative Minimum Tax (AMT)?

Employees at tech companies are commonly issued incentive stock options, the exercise of which can put them in a situation where they need to pay actual money in taxes on paper gains, via the alternative minimum tax. With tenforty's help you can see it coming at least: ;)

import polars as pl

df = (
    tenforty.evaluate_returns(
        w2_income=100_000, incentive_stock_option_gains=list(range(0, 100_001, 2500))
    )
    .select(["incentive_stock_option_gains", "federal_total_tax", "federal_amt"])
    .unpivot(index="incentive_stock_option_gains", variable_name="Type", value_name="tax")
    .with_columns(
        pl.col("Type").replace(
            {"federal_amt": "AMT", "federal_total_tax": '"Regular" Tax'}
        )
    )
)

(
    so.Plot(df, x="incentive_stock_option_gains", y="tax", color="Type")
    .add(so.Area(alpha=0.7), so.Stack())
    .label(
        x="Incentive Stock Option Gains",
        y="Total Federal Tax",
        title="Effect of ISO Gains on Federal Alternative Minimum Tax\nGiven $100K W2 Income",
    )
)

Known Limitations

• Currently does not support Windows. The Colab notebook linked above, or using WSL are workarounds. Attempts have been made to get Windows builds working, but runtime crashes persist due to compiler interoperability challenges; see Windows Build Research for details.
• Medicare and Net Investment Income Tax are not automatically computed on capital gains, so if those apply to your situation the output tax will be underestimated.
• State income tax is supported for a growing number of states, with more being added. No-income-tax states (e.g. Texas, Nevada) are also accepted. Only California has been tested against tax returns prepared independently by professional tax software; support for other states is provisional.

Development & Contributing

Contributions are welcome! See our Contributing Guidelines for the development process and coding standards.

Documentation for developers:

• Development Guide - Package internals, Cython extensions, and environment setup
• Error Handling - Configuring on_error policies and exception types
• IRS Validation - Testing methodology against official IRS and state sources

License

tenforty is released under the MIT License.

Acknowledgments

This project relies on the Open Tax Solver project for the underlying tax computation logic.