AI Coding Statistics with Python | Code Card

Introduction

Python developers are using AI-assisted tools to write boilerplate, scaffold APIs, translate data transformations, and even draft tests. That adds speed, but real gains show up when you can see patterns in your own work. AI coding statistics help you quantify where suggestions save time, where they add rework, and how quality improves sprint over sprint.

With Code Card, you can publish these metrics as a clean, shareable profile that looks like a cross between a contribution graph and a year-in-review. This guide explains what to track for Python projects, how to instrument your workflow, and how to use the numbers to improve your development process without sacrificing quality or maintainability.

Language-Specific Considerations for Python

Python is dynamic, expressive, and used across very different domains. AI assistance patterns vary by context, so your tracking should reflect those differences.

• Web frameworks: Django tends to involve configuration, model definitions, and repetitive CRUD scaffolding. Flask and FastAPI favor lightweight routing and dependency injection, with strong typing via Pydantic in FastAPI. AI often excels at model and serializer stubs, path operations, and form or schema validations.
• Data science and analytics: Pandas, NumPy, and plotting libraries generate many small, vectorized transformations. AI suggestions often jump from imperative loops to vectorized expressions. Measure acceptance rates for these rewrites and the number of chained operations per cell or function.
• Machine learning: PyTorch and TensorFlow code often includes boilerplate training loops, dataset transforms, and model definitions. Track how often you accept AI-generated layers, loss functions, and training utilities versus hand-tuning.
• Typing and documentation: Because Python does not enforce types, AI assistance is frequently used to add type hints and docstrings. Track docstring coverage and type hint coverage separately, and observe whether they trend upward as a result of prompts.
• Tooling and style: Projects with ruff, black, mypy, and pytest enforce consistent standards. Measure lint errors per 100 lines after AI insertions and test pass rates for AI-generated tests.

Key Metrics and Benchmarks

These metrics are tuned for Python workflows and map well to ai-coding-statistics for day to day tracking and analyzing trends:

• Prompt sessions per day: Number of discrete AI requests. Web backends often benefit from 10 to 25 sessions per day, while notebook-heavy data work can spike higher due to iterative exploration.
• Token consumption per accepted function or class: Divide the total output tokens by the number of accepted Python objects. For typical CRUD endpoints you might see 200 to 600 tokens per accepted object. For data pipelines and ML scripts, 400 to 900 tokens is common.
• Acceptance rate: Percentage of AI-suggested lines that make it into the final commit. Good baselines are 40 to 70 percent for backend microservices and 25 to 50 percent for research notebooks, since exploration requires more edits.
• Edit distance ratio: Levenshtein distance between suggested code and committed code divided by the length of the suggestion. A lower ratio means fewer edits. Track this by file type, for example lower for schemas.py and higher for models.py with complex business logic.
• Docstring and type hint coverage: Percentage of functions and classes with docstrings and annotations. If AI is helping you add both, you should see coverage rising to 70 percent or more over a few sprints.
• Test generation and pass rate: Number of tests that were AI-generated and their first pass rate in pytest. Aim for a first pass rate of 60 percent or higher, improving over time.
• Latency per completion: Median time to first token and total completion time, especially important for autocompletion in editors. Sub 300 ms to first token is a good target for a fluid experience.
• Lint errors per 100 lines after acceptance: Track ruff or flake8 error counts before and after AI suggestions. A downward trend indicates better alignment with your style guide.
• Security flags: Count of bandit findings associated with accepted AI-generated code. The goal is a consistent zero, with exemptions documented.
• Refactor vs net-new ratio: Whether requests are used more for refactors or greenfield code. Mature codebases often shift toward refactor suggestions and doc improvements.

Benchmarks are starting points. Your best target is a steady improvement in edit distance, docstring coverage, and test pass rates while keeping lint and security flags near zero.

Practical Tips and Code Examples

Use these Python-specific patterns to capture better value from ai-assisted development and to make your ai coding statistics actionable.

1) FastAPI typing and model scaffolding

AI excels at first-draft scaffolds for routes and Pydantic models. Keep the suggestions but control complexity.

from typing import List
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field

app = FastAPI()

class Item(BaseModel):
    id: int = Field(..., ge=1)
    name: str
    tags: List[str] = []

DB: dict[int, Item] = {}

@app.post("/items", response_model=Item)
def create_item(item: Item) -> Item:
    if item.id in DB:
        raise HTTPException(status_code=409, detail="Item exists")
    DB[item.id] = item
    return item

@app.get("/items/{item_id}", response_model=Item)
def get_item(item_id: int) -> Item:
    item = DB.get(item_id)
    if not item:
        raise HTTPException(status_code=404, detail="Not found")
    return item

What to track:

• Acceptance rate for route stubs and model definitions.
• Edit distance ratio as you enforce validation and error handling.
• Type hint coverage trend across api and schemas modules.

2) Pandas vectorization and clarity

When AI proposes chained operations, ensure readability and add comments. Track rework required to make the transformation both correct and clear.

import pandas as pd

def normalize_sales(df: pd.DataFrame) -> pd.DataFrame:
    """
    Normalize sales values by region and month.
    """
    # AI often suggests chained ops - keep logical steps separated for clarity
    totals = df.groupby(["region", "month"])["sales"].transform("sum")
    df = df.assign(sales_norm=df["sales"] / totals)
    df["is_top_quartile"] = (
        df.groupby(["region"])["sales_norm"]
          .transform(lambda s: s >= s.quantile(0.75))
    )
    return df

What to track:

• Runtime correctness via lightweight pytest tests on small fixtures.
• Docstring coverage for data transforms.
• Edit distance from first AI suggestion to final version after refactor for clarity.

3) Acceptance and edit distance measurement in plain Python

Capture how much you change AI suggestions before commit. Below is a simple edit ratio and a logger for accepted code. Feed it the suggestion and your final version, then append metrics to a local JSON file for later analysis.

import json
from pathlib import Path

def edit_distance(a: str, b: str) -> int:
    """Classic Levenshtein distance."""
    m, n = len(a), len(b)
    dp = [[0] * (n + 1) for _ in range(m + 1)]
    for i in range(m + 1):
        dp[i][0] = i
    for j in range(n + 1):
        dp[0][j] = j
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            dp[i][j] = min(
                dp[i - 1][j] + 1,      # deletion
                dp[i][j - 1] + 1,      # insertion
                dp[i - 1][j - 1] + cost  # substitution
            )
    return dp[m][n]

def edit_ratio(suggestion: str, final: str) -> float:
    if not suggestion:
        return 0.0
    return edit_distance(suggestion, final) / max(1, len(suggestion))

def log_acceptance(event: dict, outfile: Path) -> None:
    """
    event keys:
      - file, function, kind
      - suggestion, final
      - usage: {input_tokens, output_tokens, latency_ms}
      - accepted_lines
    """
    e = dict(event)
    e["edit_ratio"] = edit_ratio(e.get("suggestion", ""), e.get("final", ""))
    data = []
    if outfile.exists():
        data = json.loads(outfile.read_text())
    data.append(e)
    outfile.write_text(json.dumps(data, indent=2))

Example use in a review script:

from pathlib import Path

suggestion = """
def area(r):
    return 3.14 * r * r
"""

final = """
from math import pi

def area(r: float) -> float:
    "Compute the area of a circle."
    if r < 0:
        raise ValueError("radius must be non-negative")
    return pi * r * r
"""

log_acceptance(
    {
        "file": "geometry.py",
        "function": "area",
        "kind": "refactor",
        "suggestion": suggestion,
        "final": final,
        "usage": {"input_tokens": 120, "output_tokens": 48, "latency_ms": 210},
        "accepted_lines": 5
    },
    Path(".ai_accept_log.json"),
)

4) Measuring docstring coverage with AST

AI tools often propose docstrings and examples. Track how coverage improves over time with a light AST scanner.

import ast
from pathlib import Path
from typing import Iterable

def docstring_coverage(paths: Iterable[Path]) -> float:
    total = 0
    with_doc = 0
    for path in paths:
        if path.suffix != ".py":
            continue
        tree = ast.parse(path.read_text(encoding="utf-8"))
        for node in ast.walk(tree):
            if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)):
                total += 1
                if ast.get_docstring(node):
                    with_doc += 1
    return 0.0 if total == 0 else with_doc / total

if __name__ == "__main__":
    files = list(Path("src").rglob("*.py"))
    print(f"Docstring coverage: {docstring_coverage(files):.0%}")

Store this result alongside your acceptance logs. Over a few sprints, you should see docstring coverage trending upward if you are prompting for documentation.

5) Integrating token usage from your AI client

Most AI SDKs provide token counts and latency in the response metadata. Persist them with your acceptance events for richer ai coding statistics.

def record_completion_stats(resp, outfile):
    # resp.usage.input_tokens and resp.usage.output_tokens are common patterns
    usage = {
        "input_tokens": getattr(resp.usage, "input_tokens", None),
        "output_tokens": getattr(resp.usage, "output_tokens", None),
        "latency_ms": getattr(resp, "latency_ms", None),
    }
    log_acceptance(
        {
            "file": resp.context.get("file"),
            "function": resp.context.get("symbol"),
            "kind": resp.context.get("kind", "unknown"),
            "suggestion": resp.text,
            "final": "",  # fill after you accept and edit
            "usage": usage,
            "accepted_lines": 0
        },
        outfile,
    )

Tip: capture the suggestion text before you edit it. After you commit, update the final field from the repository to accurately compute edit ratios.

Tracking Your Progress

Once you are collecting metrics locally, publish them as a profile so you can track streaks and progress over time.

1. Start collecting events: Add a small post-commit hook that compares staged changes to the last AI suggestion and logs acceptance metrics. Pair that with your docstring coverage scanner weekly.
2. Normalize your stats: Create a nightly job that merges logs into a single JSON and computes daily aggregates for sessions, tokens, acceptance rate, edit ratio, and coverage.
3. Publish your profile: Use Code Card to turn these aggregates into a clean public page. Run npx code-card, point it at your metrics JSON, and confirm the preview looks correct before you publish.
4. Compare across projects: Keep separate streams for web, data, and ML projects. Different domains have different acceptance baselines.
5. Review streaks and habits: For guidance on sustaining momentum, see Coding Streaks for Full-Stack Developers | Code Card. If you work across the stack, explore AI Code Generation for Full-Stack Developers | Code Card.

If your profile shows high token usage but low acceptance, reduce prompt size, request smaller functions, and prefer edits over long rewrites. If you see good acceptance but frequent lint violations, adjust your model prompt to include your ruff rules and ask for compliant output by default.

Conclusion

Strong ai-coding-statistics give you a clear picture of how AI contributes to your Python development. Track acceptance, edit distance, and coverage alongside tokens and latency. Use the data to prompt for smaller, typed, and testable units of code, then measure real improvements in quality and speed. When you are ready to share your progress and learn from peers, publish to your profile with npx code-card and keep iterating.

FAQ

What metrics matter most for Python-specific AI assistance?

Focus on acceptance rate, edit distance ratio, docstring and type hint coverage, lint errors per 100 lines, and test pass rate. Pair these with token usage and latency to understand cost and responsiveness.

How can I keep sensitive code private while publishing stats?

Log only derived metrics and anonymized identifiers. Do not store prompt content or code bodies in exported data. Aggregate daily totals and ratios, then publish only the aggregates.

How do I reduce low-quality suggestions for Pandas or NumPy?

Ask for vectorized solutions, include sample shapes and dtypes, and supply a small input-output example. Reject overly clever chains and favor clarity with intermediate variables and comments.

What is a good acceptance rate for backend Python services?

Start with 40 to 60 percent as a healthy range. If you consistently exceed 70 percent, you might be accepting too much boilerplate without scrutiny. If you are under 30 percent, refine prompts, request smaller diffs, or raise typing and testing expectations in the prompt.