| name | run2_gh-pr-analysis |
| description | How to compute PR statistics (merged, closed, avg_merge_days, top_contributor) from GitHub Search API items using Python. |
GitHub PR Analysis from Search API Results
Input Format
Each PR item from /search/issues has:
state: "open" or "closed" (merged PRs are also "closed")created_at: ISO 8601 string e.g. "2024-12-05T10:00:00Z"pull_request.merged_at: timestamp string or nulluser.login: author username
Classifying PRs
def classify_prs(prs):
merged, closed, open_prs = [], [], []
for pr in prs:
pr_field = pr.get("pull_request") or {}
merged_at = pr_field.get("merged_at")
if merged_at:
merged.append({**pr, "_merged_at": merged_at})
elif pr["state"] == "closed":
closed.append(pr)
else:
open_prs.append(pr)
return merged, closed, open_prs
Average Merge Time (days, rounded to 1 decimal)
from datetime import datetime, timezone
def parse_dt(s):
return datetime.strptime(s, "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc)
def avg_merge_days(merged_prs):
if not merged_prs:
return 0.0
diffs = []
for pr in merged_prs:
pr_field = pr.get("pull_request") or {}
merged_at = pr_field.get("merged_at")
created_at = pr.get("created_at")
if merged_at and created_at:
delta = (parse_dt(merged_at) - parse_dt(created_at)).total_seconds() / 86400
diffs.append(delta)
return round(sum(diffs) / len(diffs), 1) if diffs else 0.0
Top Contributor
from collections import Counter
def top_contributor(prs):
authors = Counter(pr["user"]["login"] for pr in prs if pr.get("user"))
return authors.most_common(1)[0][0] if authors else "unknown"
Key Distinction: "closed" means unmerged+closed
merged: pull_request.merged_at is not nullclosed: state == "closed" AND pull_request.merged_at is null- These two are mutually exclusive and exhaustive for closed PRs.
