Data Science Academy · 课时

详解拆分—应用—合并

每个 groupby 背后的模型

详解拆分—应用—合并 是 CoddyKit 上的免费 Data Science Academy 课时。这是第 1 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Data Science Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Data Science Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

One Idea, Three Steps

Every groupby follows one rhythm: split the rows into groups, apply a calculation to each, then combine the answers back together. 🔁

Split: Cut by a Key

The split step partitions rows by a key column, so all rows sharing the same value land in the same little group.

groups = df.groupby("city")

Apply: Do Work Per Group

In the apply step, the same function runs on each group on its own, never mixing one group with another.

df.groupby("city")["sales"].mean()

Combine: Stitch Results

The combine step glues each group result into one tidy output, indexed by the group keys you split on.

groupby Is Lazy

Calling groupby alone does almost nothing; it just remembers the plan. The real work waits until you add an aggregation.

g = df.groupby("city")  # no math yet

The Group Key Becomes the Index

After aggregating, your group key moves into the result index, so each unique value labels one output row.

Pick a Column to Aggregate

Select a column after grouping to focus the math. Here you ask for the average sales within each city.

df.groupby("city")["sales"].mean()

size Counts Rows Per Group

Use size when you just want how many rows fell into each group, including any missing values.

df.groupby("city").size()

Iterating Over Groups

You can loop a groupby to inspect it: each turn hands you the group name and the matching sub-table.

for name, part in df.groupby("city"):
    print(name, len(part))

Why It Beats Manual Loops

Split-apply-combine replaces slow hand-written loops with one fast, readable line that pandas optimizes for you. ⚡

A Tiny End-to-End Example

This single line splits by region, averages each group, and combines the result, all in one readable expression.

df.groupby("region")["revenue"].mean()

Quick Check

Which step actually does the calculation?

Recap: The groupby Rhythm

You learned the heartbeat of grouping: split, apply, combine. Master this rhythm and every aggregation later feels natural. 🎯

常见问题解答

「详解拆分—应用—合并」课时是免费的吗？

是的 — 「详解拆分—应用—合并」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Data Science Academy 课程的其余内容，请升级到 CoddyKit PRO。 Data Science Academy 课程共包含 4 节课。

「详解拆分—应用—合并」这节课中我会学到什么？

每个 groupby 背后的模型你通过在浏览器中直接运行的动手代码来练习 Data Science Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Data Science Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Data Science Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 1 节课，共 4 节。

「详解拆分—应用—合并」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Data Science Academy 课中编写并运行代码吗？

能。每节 Data Science Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

← 返回 Data Science Academy