本文详细介绍了数据的检索以及将其处理成可用格式所需的后续步骤。作者对数据采集取得的进展表示兴奋，下一阶段将专注于将原始数据转化为可操作的输出。文章似乎是关于数据处理工作流的个人日志或技术笔记。 AI
排序理由 该条目是关于数据处理的个人日志或技术笔记，而不是重要的行业事件或发布。
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本文如何被排名
- Signal score
- 0 / 100
- Composite score across the factors below. Higher = stronger signal that this story matters right now.
- Newsworthiness bucket
- Commentary
- 该条目是关于数据处理的个人日志或技术笔记，而不是重要的行业事件或发布。
- Source corroboration
- Single-source cluster
- Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
- Topics
- other
- Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
- AI-industry relevance
- Low
- Off-topic or adjacent — cluster remains reachable but doesn't surface in AI-industry rankings.
- Story freshness
- 21 days old
- Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.
完整方法见我们的编辑标准。
报道来源 [1]
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恭喜，我们现在已经完成了数据检索工作。下一步是如何处理这些数据以生成可用的输出。在本文中，我们将介绍基本
Congratulations, we now have worked on retrieving data. Next step is now how to work on that data to produce ready to use output. In this article we will basically cover how to transform, calculate and reshape data directly inside a querry rather than pulling those raw values and…