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使用 agg 执行多种聚合

为不同列应用不同函数

使用 agg 执行多种聚合 是 CoddyKit 上的免费 Data Science Academy 课时。这是第 2 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。

Why One Number Is Not Enough

A single mean hides a lot. With agg you can request several summaries at once and see the fuller picture.

Meet agg

The agg method takes a list of functions and runs every one of them on each group in a single pass.

df.groupby("city")["sales"].agg(["mean", "max"])

A List of Function Names

Pass plain strings like "sum" or "min", and pandas maps each to its built-in aggregation for you.

g.agg(["sum", "min", "max"])

Each Function Becomes a Column

With a list of functions, the result gains one column per function, neatly side by side for comparison.

Different Functions Per Column

Pass a dictionary to apply different aggregations to different columns in the very same call.

df.groupby("city").agg({"sales": "sum", "age": "mean"})

Name Your Output Columns

Named aggregation lets you label results clearly, so you never guess what each output column means.

g.agg(total=("sales", "sum"))

Use Your Own Function

Any function returning one value works, so a custom lambda can compute a range or anything you like.

g.agg(lambda s: s.max() - s.min())

Mixing Built-ins and Custom

Inside one list you can blend string names with your own callables; pandas applies each to every group.

g.agg(["mean", lambda s: s.std()])

Aggregating Many Columns at Once

Skip selecting a column and agg runs across every numeric column, giving a wide summary table.

df.groupby("city").agg("mean")

Reset the Index to Flatten

Call reset_index afterward to turn the group keys back into normal columns for easy charting or merging.

result.reset_index()

Read the Result Carefully

When functions and columns multiply, you get a MultiIndex on columns; read it as (column, function) pairs.

Quick Check

You want sum on one column and mean on another. What do you pass to agg?

Recap: agg Is Your Swiss Knife

You can now stack many summaries, mix built-ins with custom functions, and name outputs, all through one flexible agg call. 🛠️

常见问题解答

「使用 agg 执行多种聚合」课时是免费的吗？

是的 — 「使用 agg 执行多种聚合」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。

「使用 agg 执行多种聚合」这节课中我会学到什么？

为不同列应用不同函数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 2 节课，共 4 节。

「使用 agg 执行多种聚合」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。

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