· Public charity
Python Software Foundation
The mission of the Python Software Foundation (PSF) is to promote, protect, and advance the open source Python programming language and to support and facilitate the growth of a diverse and international community of Python programmers.
What you funded, over time
Every grant placed by its stated purpose and the recipient’s mission, by year — across FY2018–2023.
Where the money goes
Your grants by size, and where they go.
The 2 grants below total $14,400 — the rows itemised in this filing. The $686,912 headline is the total grant expense reported on the return, so the remaining $672,512 is giving the schedule does not break out: grants under the $5,000 itemisation floor, grants to individuals, and grants reported on other schedules. Every figure below describes the itemised rows only.
| Recipient | Amount |
|---|---|
| Django Events Foundation North America | $7,500 |
| PyTexas Foundation | $6,900 |
Do your dollars go where the need is?
Each grant placed by the hardship in its grantee’s ZIP, then read against the area’s typical level.
Dollar for dollar, your grants (FY18–23) land where the poverty rate runs at 15%, against an area that typically sits at 11%. 75% of your dollars go to grantees based in above-average-need neighborhoods. Most of your grants land in higher-need ZIPs.
Matched: human-services grants are those whose grantee’s IRS cause code is Human Services. Placed by people below the poverty linein the grantee’s ZIP, averaged from the tracts in that ZIP; grantee ZIP can differ from where services are delivered.
Grants abroad, by region — $363k on the FY2024 return
Schedule F, as filed: 7 regions. The IRS asks for region and purpose, not the recipient, so no country or grantee can be named here.
Stated purpose: Grant to the Creating Python Communities in Preparation for Cameroon TechSummit workshops in Cameroon during 2024 · Grant to the PUG Ho Coding Bootcamp in Ho, Ghana during 2024 · Grant to Python Ghana for 37 user group events, 8 PyLadies events, 2 PyData Meetups, 2 general community events, and a 6-week bootcamp in Ghana during 2024
Stated purpose: Grant to the EuroPython Prague conference in Prague, Czech Republic from July 8-14, 2024 · Grant to the PyConFR conference in Strasbourg, France from October 31-November 3, 2024 · Grant to PyCon DE & PyData Berlinin Berlin, Germany from April 22-24, 2024
Stated purpose: Grant to the PyCon AU conference in Melbourne, Australia, on November 22-26, 2024 · Grant to Python Conference APAC Indonesia 2024 in Yogyakarta, Indonesia from October 24th-27, 2024 · Grant to the PyCon JP conference in Tokyo, Japan from September 27-29, 2024
Stated purpose: Grant to the Python Brasil conference in Rio de Janeiro, Brazil from October 16-21, 2024 · Grant to the Plone Conference 2024 and Python Cerrado 2024 conference in Brasilia, Brazil from November 25-December 1, 2024 · Grant to the PyCon Colombia event in Medellin, Colombia from June 7-9, 2024
Stated purpose: Grant to the PyCon Latam conference in Mazatlan, Sinaloa, Mexico from September 19-22, 2024
Stated purpose: Grant to the PyCon Panama 2024 conference in Panama City, Panama from October 16-18, 2024
Stated purpose: Grant to the PyCon India conference in Bengaluru, India from September 20-23, 2024
US grants placed by each grantee’s ZIP, which can differ from where services are actually delivered. Need from public data — U.S. Census/ACS, CDC PLACES, Eviction Lab, USDA — shown as context about the area, never attributed to your giving. Grants abroad on Schedule F are filed by region, purpose and amount with no recipient name, so they are shown by region and cannot be placed on the country map or matched to a grantee.
4 grantees tracked through their own filings, 2017–2025.
Each one resolved to its own IRS returns and tracked year by year — your grant beside their revenue from every source. Association, dated; never a causal claim.
Counted here: distinct organizations you funded across 2017–2025, not grant rows in a single year — so this will not match the grant count on the cover. 4 of the 9 grantees resolved in that span have returns of their own we could reconcile; the rest are funded organizations whose filings we could not track year by year.
Where your money sits — by cause, then by grantee
Each org by its size and your share of it — top-left is where you’re load-bearing
Go grantee by grantee — a decade per org, and how each moved after you funded them
A decade per grantee — revenue shaded, your grants as bars, all rows on one timeline.
Government reliance of your grantees
Every dot is one organization Python Software Foundation funds. Left–right is the share of its income from government; up–down is the share from you. Bigger dots raise more. Filter to federal or a single department — and drag the year to watch it move.
Government income is each org’s traced federal awards (USASpending — grants and contracts) plus state payments (open checkbooks) as a share of its total revenue (IRS Form 990); the self-reported government-grant line (990 line 1e) is carried for cross-check: a grantee that reports government grants we could not trace to a source is left off the chart rather than shown as receiving none. An association, not a claim that your grant caused the public funding. Coverage is precision-first — a floor, not a census; state records exist for 9 states, so a grantee outside them shows no state figure (dimmed) rather than a false zero. Federal award amounts are obligations, which can span years, so a single-year share is indicative. 990 filings lag 12–24 months.
Warm introductions · Powered by PlinthPlus
How do I get to Python Software Foundation?
Find your warmest path to Python Software Foundation through trustees and officers whose names appear on both boards. Search for your organization and Plinth traces the shortest route it can evidence.
Each link is a name appearing on two organizations’ public IRS 990 filings, matched on that name and, where the filings support it, on location. A same-state match has geographic support, which is a second matching feature rather than confirmation that the two are one person; a cross-state one is the likeliest to be two people who share a name. Every hop shows its tier, low-confidence and distant paths are held back rather than guessed, and it is worth confirming the person before you use the introduction.