Data Science Academy · 课时

将数字区间划分为类别

使用 cut 和 qcut 划分范围

将数字区间划分为类别 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。

Why Bin a Number

Sometimes a raw number like age tells you less than a label like young or senior. Binning turns a continuous value into tidy groups. 🪣

Bins Reveal Patterns

Models and charts often read categories more clearly than exact figures. Grouping prices into low, mid, and high can surface trends you would otherwise miss.

Meet pandas cut

The cut function slices a column into bins you define by edges. You give it the boundaries, and it labels each value for you.

import pandas as pd
ages = pd.Series([7, 22, 45, 70])
bins = pd.cut(ages, [0, 18, 60, 100])

Name Your Bins

Pass labels so each bin has a friendly name instead of an interval. Now your output reads like a real category, not a math range.

pd.cut(ages, [0, 18, 60, 100],
       labels=['child', 'adult', 'senior'])

Edges Are Inclusive Right

By default cut includes the right edge of each bin and excludes the left. So a value of 18 lands in the first bin, not the second.

Flip With right=False

Want the left edge included instead? Set right=False and the boundary behavior flips, so 18 moves into the next bin up.

pd.cut(ages, [0, 18, 60, 100], right=False)

Equal-Width Bins

Give cut a single number instead of edges and it creates that many equal-width bins automatically across the value range.

pd.cut(ages, 4)

Meet pandas qcut

The qcut function splits by quantiles, so each bin holds roughly the same number of rows. Great when your data is lopsided.

pd.qcut(ages, 4)

cut vs qcut

Use cut for fixed, meaningful edges like price tiers. Use qcut when you want balanced groups such as quartiles of income.

Bins Become a Feature

The result is a categorical column you can drop straight into your DataFrame and feed to grouping, plotting, or a model.

df['age_group'] = pd.cut(df['age'],
                         [0, 18, 60, 100])

Watch the Out-of-Range

Values outside your bin edges become NaN. Always set edges that cover your real minimum and maximum, or you will silently lose rows.

Quick Check

You want four groups that each hold about the same number of rows. Which tool fits?

Recap: Binning

You learned to group numbers into labels: cut for chosen edges and equal widths, qcut for equal counts. Mind the edges and you turn raw values into useful features. 🎉

常见问题解答

「将数字区间划分为类别」课时是免费的吗？

是的 — 「将数字区间划分为类别」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。

「将数字区间划分为类别」这节课中我会学到什么？

使用 cut 和 qcut 划分范围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。

「将数字区间划分为类别」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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