Python-specific terminology for English learners: A complete guide from the English-level-up-tips repository

The English-level-up-tips repository maintains a curated Python word list in docs/threads/word-list/Python.md containing high-frequency English terms like async, decorator, and dataclass, enabling non-native speakers to master technical vocabulary through structured, AI-assisted study routines.

The byoungd/English-level-up-tips open-source project helps developers improve their English proficiency through themed vocabulary lists and AI-enhanced learning paths. The Python-specific terminology collection targets programmers who need to understand technical documentation, error messages, and code comments written in English. This plain-markdown resource integrates seamlessly with flashcard apps and LLM tools to create personalized study workflows.

Locating the Python word list in the repository structure

The repository organizes content as a static documentation site suitable for GitBook or GitHub Pages. The Python word list resides at docs/threads/word-list/Python.md, containing approximately 89 pedagogically ordered terms.

Supporting files include:

• README.md – Introduces the guide, explains CEFR levels, and links to AI learning workflows.
• SUMMARY.md – Drives site navigation and indexes the Python vocabulary section.
• docs/threads/part-1/6-writing.md – Provides writing exercises for contextualizing technical terms.
• docs/threads/part-1/7-ai.md – Describes how to leverage Gemini and other LLMs for generating practice material.

Because the list uses plain markdown without embedded HTML, you can parse it with standard file I/O operations or static site generators.

Key terminology covered in the Python word list

The vocabulary targets high-frequency words encountered when reading or writing Python code. Representative terms include async, decorator, dataclass, comprehension, generator, iterator, namespace, pickle, recursion, and whitespace.

Each term appears as a standalone line in the markdown file, ordered by pedagogical relevance rather than alphabetically. This sequencing helps learners prioritize concepts they will encounter most frequently in documentation and Stack Overflow discussions.

Four-step learning workflow for Python vocabulary

The repository recommends a structured approach to mastering Python-specific terminology that combines passive review with active production.

Step 1 – Review – Open docs/threads/word-list/Python.md and read each term aloud to build phonetic familiarity.

Step 2 – Contextualize – Look up the term in the official Python documentation or query an LLM (Gemini, as recommended in the AI chapter) for a concise definition suited to non-native speakers.

Step 3 – Apply – Write a minimal Python snippet demonstrating the term in context, reinforcing the connection between English meaning and technical function.

Step 4 – Reinforce – Import the terms into spaced repetition software or use the repository’s AI prompts to generate quiz questions that test recall.

Automating study with Python scripts

Because the word list is plain text, you can automate study material generation. Below are three practical scripts that interact directly with the repository’s source files.

Extracting terms from the markdown source

This script reads docs/threads/word-list/Python.md, filters out comments and blank lines, and outputs a numbered list.

import pathlib

# Path to the markdown file inside the repo

md_path = pathlib.Path(
    "docs/threads/word-list/Python.md"
)

terms = [line.strip() for line in md_path.read_text().splitlines()
         if line and not line.startswith('#')]

print("Python terms for English learners:")
for i, term in enumerate(terms, 1):
    print(f"{i:2}. {term}")

Running this against the repository produces the full set of 89 terms ready for processing.

Generating definitions via the Gemini API

The repository recommends using Gemini through the token.love gateway for vocabulary explanations. This script fetches concise definitions for the first ten terms.

import os, json, requests

API_URL = "https://api.token.love/v1/chat/completions"   # token.love gateway mentioned in the README

API_KEY = os.getenv("TOKEN_LOVE_API_KEY")               # placeholder – keep the key private

def define(term):
    payload = {
        "model": "gemini-1.5-pro",
        "messages": [{"role": "user",
                      "content": f"Give a concise English definition of the Python term '{term}' suitable for a non‑native speaker."}]
    }
    headers = {"Authorization": f"Bearer {API_KEY}"}
    resp = requests.post(API_URL, json=payload, headers=headers)
    return resp.json()["choices"][0]["message"]["content"]

for term in terms[:10]:               # show first 10 terms as a demo

    print(term, "→", define(term))

Replace the endpoint and model parameters if using OpenAI, Claude, or other providers documented in the AI chapter.

Creating Anki flashcards from the term list

This script generates a TSV file compatible with Anki’s import function, using cloze deletion format for active recall.

anki_tsv = "\n".join(f"{term}\t{{{{c1::Definition of {term}}}}}" for term in terms)

with open("python_terms.tsv", "w", encoding="utf-8") as f:
    f.write(anki_tsv)

print("Anki flashcard file created: python_terms.tsv")

Import the resulting file into Anki, then populate the definition fields using the LLM script above or manual research.

Summary

• The English-level-up-tips repository hosts a curated Python word list at docs/threads/word-list/Python.md containing ~89 high-frequency technical terms.
• The plain-markdown format enables easy parsing by Python scripts for automated flashcard generation and LLM integration.
• The repository recommends a four-step workflow: Review, Contextualize, Apply, and Reinforce.
• Gemini via the token.love gateway is the primary recommended AI engine for generating definitions and practice questions.
• Companion files like docs/threads/part-1/6-writing.md and docs/threads/part-1/7-ai.md provide frameworks for integrating vocabulary into writing practice.

Frequently Asked Questions

What file contains the Python vocabulary list in the English-level-up-tips repository?

The definitive list resides at docs/threads/word-list/Python.md. This file contains one term per line, ordered pedagogically rather than alphabetically, making it easy to parse with standard file I/O or import into study applications.

How many Python terms are included in the word list?

The repository contains approximately 89 terms covering high-frequency vocabulary found in Python documentation, tutorials, and error messages. The list focuses on concepts like async, decorator, dataclass, and comprehension that appear repeatedly in real-world codebases.

Can I use the Python word list with flashcard applications like Anki?

Yes. Because the source is plain markdown without complex formatting, you can extract terms using Python’s pathlib module and export them to TSV or CSV formats compatible with Anki, Quizlet, or other spaced repetition systems. The repository’s AI chapter also describes how to generate contextual example sentences for each card.

Which AI model does the repository recommend for learning Python terminology?

According to the docs/threads/part-1/7-ai.md chapter, the guide recommends Gemini (specifically gemini-1.5-pro) accessed through the token.love gateway. This configuration is optimized for generating concise definitions, example sentences, and quiz questions suitable for non-native English speakers studying technical content.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}