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:

• 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.

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.

Requires Python 3.10+.

pip install tenforty

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:

The functions output these fields:

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

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_income_tax': 8484.0,
 'federal_se_tax': 0.0,
 'federal_niit': 0.0,
 'federal_additional_medicare_tax': 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.

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):

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"
    )
)

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.

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",
    )
)

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",
    )
)

• 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; other states have formula-derived and property-based testing. See IRS Validation for details.

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

tenforty is released under the MIT License.

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