Data Science Academy · 课时

使用 AND 和 OR 组合筛选条件

安全地串联多个条件

使用 AND 和 OR 组合筛选条件 是 CoddyKit 上的免费 Data Science Academy 课时。这是第 3 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 Condition Is Rarely Enough

Real questions stack conditions: adults in Paris, or orders that are late and large. You need to combine filters cleanly. 🧩

Use the Ampersand for AND

To require both conditions, join the two masks with the ampersand. It works element by element across the rows.

df[(df["age"] > 30) & (df["city"] == "Paris")]

Use the Pipe for OR

To accept either condition, join the masks with the pipe. A row passes if at least one side is True.

df[(df["city"] == "Paris") | (df["city"] == "Lyon")]

Wrap Each Condition in Parentheses

Always put parentheses around each condition. Without them, Python evaluates the operators in the wrong order and raises an error.

df[df["age"] > 30 & df["score"] > 80]   # breaks

Why Not and or or?

The plain words and and or work on single True/False values, not on whole Series. For row masks you must use the symbols.

# wrong: df[(df["age"] > 30) and (...)]
# right: use & instead

Invert With the Tilde

The tilde flips a mask, turning True into False. It is the clean way to say not this condition.

df[~(df["city"] == "Paris")]   # everyone not in Paris

Mix AND and OR Carefully

You can blend operators, but parentheses decide the grouping. Make your intent explicit so the logic reads the way you mean it.

df[(df["age"] > 30) & ((c == "Paris") | (c == "Lyon"))]

Three or More Conditions

Stacking more masks works the same way. For readability, build them on separate lines, then combine the named masks.

old = df["age"] > 30
top = df["score"] > 80
df[old & top]

AND Narrows, OR Widens

Keep the mental model simple: AND shrinks the result toward fewer rows, while OR grows it toward more rows.

Combine Across Columns

Your conditions can touch different columns at once, letting you express rich questions in a single expression.

df[(df["age"] > 30) & (df["active"] == True)]

Save Complex Masks

When logic gets dense, give the combined mask a clear name. Future you will thank you for the readable variable.

vip = (df["age"] > 30) & (df["score"] > 80)
df[vip]

Quick Check

Pick the expression that filters correctly.

Recap: Combining Filters

Use the ampersand for AND, the pipe for OR, the tilde to invert, and always wrap each condition in parentheses. Complex questions, one clean line. ✅

常见问题解答

「使用 AND 和 OR 组合筛选条件」课时是免费的吗？

是的 — 「使用 AND 和 OR 组合筛选条件」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 AND 和 OR 组合筛选条件」这节课中我会学到什么？

安全地串联多个条件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 3 节课，共 4 节。

「使用 AND 和 OR 组合筛选条件」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