Anchors
| Anchor text | Ref. domains ▾ | Top DR | Ref. pages | Links to target | Dofollow links |
|---|---|---|---|---|---|
| Talk Python To Me | 16 | — | 0 | 105 | 102 97.1% |
| Talk Python to Me | 13 | — | 0 | 14 | 13 92.9% |
| Talk Python | 4 | — | 0 | 48 | 48 100% |
| TalkPython | 3 | — | 0 | 3 | 3 100% |
| Talk Python to Me #424: Shiny for Python | 2 | — | 0 | 3 | 3 100% |
| Podcast | 2 | — | 0 | 105 | 105 100% |
| Talk Python to Me #489 – Anaconda Toolbox for Excel and more with Peter Wang | 2 | — | 0 | 2 | 2 100% |
| Talk Python to Me #315 – Awesome FastAPI extensions and add ons | 2 | — | 0 | 2 | 2 100% |
| https://talkpython.fm/ | 2 | — | 0 | 2 | 1 50% |
| Talk Python to Me #479: Designing Effective Load Tests for Your Python App | 2 | — | 0 | 2 | 2 100% |
| Talk Python to Me #480: Ahoy, Narwhals are bridging the data science APIs | 2 | — | 0 | 4 | 4 100% |
| Talk Python to Me #491 – DuckDB and Python: Ducks and Snakes living together | 2 | — | 0 | 3 | 3 100% |
| Talk Python to Me #493 – Quarto: Open-source technical publishing | 2 | — | 0 | 3 | 3 100% |
| Talk Python to Me #439: Pixi, A Fast Package Manager | 2 | — | 0 | 3 | 3 100% |
| Talk Python to Me #495 – OSMnx: Python and OpenStreetMap | 2 | — | 0 | 2 | 2 100% |
| TalkPython.fm | 2 | — | 0 | 2 | 2 100% |
| Talk Python to Me #461: Python in Neuroscience and Academic Labs | 2 | — | 0 | 2 | 2 100% |
| Talk Python to Me #193 – Data Science Year in Review 2018 Edition | 2 | — | 0 | 2 | 2 100% |
| Talk Python to Me #497 – Outlier Detection with Python | 2 | — | 0 | 3 | 3 100% |
| Talk Python to Me #512 – Building a JIT Compiler for CPython | 2 | — | 0 | 5 | 5 100% |
| Talk Python to Me Episode #522 – Data Sci Tips and Tricks from CodeCut.ai | 2 | — | 0 | 2 | 2 100% |
| Talk Python to Me #447: Parallel Python Apps with Sub Interpreters | 2 | — | 0 | 2 | 2 100% |
| Talk Python to Me #453: uv – The Next Evolution in Python Packages? | 2 | — | 0 | 3 | 3 100% |
| Talk Python to Me Episode #520 – pyx – the other side of the uv coin (announcing pyx) | 2 | — | 0 | 7 | 7 100% |
| Talk Python to Me Episode #516 – Accelerating Python Data Science at NVIDIA | 2 | — | 0 | 4 | 4 100% |
| Talk Python to Me #436: An Unbiased Evaluation of Environment and Packaging Tools | 2 | — | 0 | 2 | 2 100% |
| Talk Python to Me #521 – Red Teaming LLMs and GenAI with PyRIT | 2 | — | 0 | 4 | 4 100% |
| Talk Python to Me #466: Pydantic Performance Tips | 2 | — | 0 | 4 | 4 100% |
| Talk Python to Me #326: Building Desktop Apps with wxPython | 2 | — | 0 | 3 | 3 100% |
| Talk Python to Me Episode #507: Agentic AI Workflows with LangGraph | 2 | — | 0 | 7 | 7 100% |
| Talk Python to Me #484: From React to a Django+HTMX based stack | 2 | — | 0 | 3 | 3 100% |
| Talk Python to Me #416: Open Source Sports Analytics with PySport | 2 | — | 0 | 2 | 2 100% |
| Talk Python to Me Episode #513 – Stories from Python History | 2 | — | 0 | 5 | 5 100% |
| Talk Python to Me Episode #518 – Celebrating Django’s 20th Birthday With Its Creators | 2 | — | 0 | 2 | 2 100% |
| Talk Python to Me #485: Secure coding for Python with SheHacksPurple | 2 | — | 0 | 5 | 5 100% |
| Talk Python to Me #396: AI Goes on Trial For Writing Code (crossover) | 2 | — | 0 | 3 | 3 100% |
| Talk Python to Me #428: Django Trends in 2023 | 2 | — | 0 | 3 | 3 100% |
| Listen | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me: #318: Measuring your ML impact with CodeCarbon | 1 | — | 0 | 2 | 0 0% |
| Talk Python to Me #35 Turbogears and the future of Python web frameworks | 1 | — | 0 | 3 | 3 100% |
| pipx - Installable, Isolated Python Applications | 1 | — | 0 | 5 | 5 100% |
| 100th episode of the Talk Python to Me podcast | 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% |
| Learning (and teaching) Python in a vacuum | 1 | — | 0 | 1 | 1 100% |
| Talk Python to Me #369: Getting Lazy with Python Imports and PEP 690 | 1 | — | 0 | 1 | 1 100% |
| 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% |
| Talk Python to Me #122 – Home Assistant: Pythonic Home Automation | 1 | — | 0 | 2 | 2 100% |
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.