Anchors
| Anchor text | Ref. domains ▾ | Top DR | Ref. pages | Links to target | Dofollow links |
|---|---|---|---|---|---|
| Talk Python to Me #359: Lifecycle of a machine learning project | 1 | — | 0 | 1 | 1 100% |
| #202 | 1 | — | 0 | 1 | 0 0% |
| Talk Python to Me #175 – Teaching Python to network engineers | 1 | — | 0 | 1 | 1 100% |
| pytest tips and tricks for better testing | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #399: Monorepos in Python | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #94 – Guaranteed packages via Conda and Conda-Forge | 1 | — | 0 | 2 | 2 100% |
| Our Blog | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #214 – Dive into CPython 3.8 and beyond | 1 | — | 0 | 2 | 2 100% |
| Talk Python to Me #35 Turbogears and the future of Python web frameworks | 1 | — | 0 | 3 | 3 100% |
| Talk Python to Me Episode #511 – From Notebooks to Production Data Science Systems | 1 | — | 0 | 2 | 2 100% |
| Talk Python to Me #498 – Algorithms for high performance terminal apps | 1 | — | 0 | 3 | 3 100% |
| Talk Python to Me #450: Versioning Web APIs in Python | 1 | — | 0 | 1 | 1 100% |
| Beginners and Experts in Software Development | 1 | — | 0 | 1 | 1 100% |
| We've moved to Hetzner | 1 | — | 0 | 3 | 3 100% |
| Talk Python to Me #80 – TinyDB: A tiny document db written in Python | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #442: Ultra High Speed Message Parsing with msgspec | 1 | — | 0 | 1 | 1 100% |
| 100th episode of the Talk Python to Me podcast | 1 | — | 0 | 1 | 1 100% |
| Python in Typeface and Font Development – An interview with Just van Rossum | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #93 – Spreading Python through the sciences with Software Carpentry | 1 | — | 0 | 1 | 1 100% |
| Escaping Excel Hell with Python and Pandas | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #86 – Python at StackOverflow | 1 | — | 0 | 1 | 1 100% |
| Listen → | 1 | — | 0 | 2 | 2 100% |
| 1 | — | 0 | 1 | 1 100% | |
| Mastodon for Python Devs | 1 | — | 0 | 3 | 3 100% |
| Talk Python to Me #410 – The Intersection of Tabular Data and Generative AI | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #331: Meet the Python Developer in Residence: Lukasz Langa | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #434: Building Mobile Apps Backed with Python | 1 | — | 0 | 3 | 3 100% |
| Screenshot of Talk Python Podcast | 1 | — | 0 | 8 | 8 100% |
| Talk Python to Me #307 – Python from 1994 to 2021, my how you’ve grown! | 1 | — | 0 | 1 | 1 100% |
| эпизод 377 | 1 | — | 0 | 1 | 0 0% |
| Say Hello to PyScript | 1 | — | 0 | 1 | 1 100% |
| Learning (and teaching) Python in a vacuum | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #48 – Building Flask-based Web Apps | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #494 – Update on Flet: Python + Flutter UIs | 1 | — | 0 | 1 | 1 100% |
| Talk Python To Me [Pro] | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #149 – 4 Python Web Frameworks, Compared | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #492 – Great Tables | 1 | — | 0 | 3 | 3 100% |
| Talk Python to Me #318: Measuring your ML impact with CodeCarbon | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me Episode #532 – 2025 Python Year in Review | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #417: Test-Driven Prompt Engineering for LLMs with Promptimize | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #534 – diskcache: Your secret Python perf weapon | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #476: Unified Python packaging with uv | 1 | — | 0 | 2 | 2 100% |
| Talk Python to Me #296 – Python in F1 racing | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #325: MicroPython + CircuitPython | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #37 Python Cybersecurity and Penetration Testing | 1 | — | 0 | 1 | 1 100% |
| Higher level Python asyncio with AnyIO | 1 | — | 0 | 3 | 3 100% |
| 1 | — | 0 | 1 | 0 0% | |
| Talk Python to Me #143 – Tuning Python Web App Performance | 1 | — | 0 | 4 | 4 100% |
| Blog | 1 | — | 0 | 64 | 64 100% |
| GitHub | 1 | — | 0 | 63 | 63 100% |
Frequently Asked Questions
What anchor texts are used to link to talkpython.fm?
This page shows all anchor texts found in backlinks pointing to talkpython.fm, sorted by the number of referring domains using each anchor. Anchor texts range from branded terms (like the domain name itself) to keyword-rich phrases that describe the linked content. The distribution of anchor texts reveals how other websites perceive and describe talkpython.fm.
What is anchor text?
Anchor text is the visible, clickable text in a hyperlink. Search engines use anchor text as a signal to understand what the linked page is about. For example, if many sites link to a page using the anchor text "best running shoes," search engines infer that the page is relevant to that topic. Anchor text appears in several forms: exact-match (contains target keywords), branded (uses the company or domain name), generic (like "click here"), and naked URLs.
Why is anchor text analysis important for SEO?
Anchor text analysis helps identify potential SEO risks and opportunities. A natural backlink profile has diverse anchor texts including branded terms, generic phrases, and topic-relevant keywords. Over-optimization, where too many backlinks use the same exact-match keyword anchor, can trigger search engine penalties. Conversely, understanding which anchors drive the most authority (measured by referring domain count and DR) helps prioritize link building efforts.
How many unique anchor texts does talkpython.fm have?
The anchor text report for talkpython.fm displays all distinct anchor texts grouped by their hash. Each row shows how many unique referring domains use that anchor, the total number of links, and the dofollow percentage. A high number of unique anchors generally indicates a healthy, natural backlink profile with diverse link sources.