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Conventional Commits — Team Guidelines with Lots of Examples

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  The format (one line) type(scope)!: subject type : one of  feat ,  fix ,  perf ,  refactor ,  chore ,  docs ,  test ,  build ,  ci ,  style ,  revert scope   (optional) : focus area like  frontend ,  backend ,  api ,  db ,  billing ,  search ,  auth ,  infra ! : indicates a breaking change ( also  add a  BREAKING CHANGE:  footer in the body) Rules we enforce (team policy) Header Max  100  characters No trailing period Body   (optional but recommended) Leave  one blank line  between header and body Explain  what  and  why ; wrap lines around  ~72–100 chars Breaking changes Either:  feat(scope)!: ... Or add a footer line: BREAKING CHANGE: describe impact and migration SemVer mapping (cheat-sheet) fix:  →  PATCH feat:  →  MINOR BREAKING CHANGE  or  !  →  MAJOR Copy-paste templates ...
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Excel, SQL, and Pandas: One-to-One Cheat Sheet for Everyday Data Tasks

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A compact, side-by-side reference that maps common data tasks across  Excel ,  SQL , and  Python (Pandas) . Copy/paste the snippets and adapt table/column names to your data. A lot of us think in  Excel , work with data stored in  SQL  databases, and script repeatable workflows in  Python (Pandas) . This cheat sheet lines those worlds up so you can translate muscle memory across tools: if you know how to do it in one, you’ll see the equivalent in the others. It focuses on everyday tasks—loading data, filtering, selecting, sorting, aggregating, joining, creating new columns, handling missing values, exporting, and plotting—using short, copy-pasteable patterns. What's included Loading data, selecting columns, filtering, sorting Grouping/aggregations, joins, computed columns Missing data handling, exporting, and quick charts Who this is for Analysts, engineers, and anyone who bounces between CSVs, databases, and notebooks. If you know one of these tools, ...
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