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修正 dtypes 并删除重复行

转换类型并删除重复项

修正 dtypes 并删除重复行 是 CoddyKit 上的免费 Data Science Academy 课时。这是第 4 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。

Clean Goes Beyond Gaps

Filling missing values is only half the job. Clean data also needs correct dtypes and no accidental duplicate rows muddying your counts. 🧹

What a dtype Is

Every column has a dtype, its data type, like int, float, object, or datetime. The dtype controls which operations the column even allows.

df.dtypes

Numbers Stuck as Text

A frequent mess: numbers loaded as object strings because one stray symbol crept in. You cannot do math on them until the type is fixed.

Coerce With to_numeric

Convert a text column to numbers with to_numeric. Pass errors='coerce' to turn anything unparseable into NaN instead of crashing.

df['price'] = pd.to_numeric(df['price'], errors='coerce')

Cast With astype

When a column is already clean, astype changes its type directly, for example turning whole-number floats back into compact integers.

df['count'] = df['count'].astype(int)

Fix Date Columns

Dates often arrive as text. Convert them with to_datetime so you can sort, filter by range, and extract parts like month later.

df['date'] = pd.to_datetime(df['date'])

Category to Save Memory

Columns with few repeated text values shrink dramatically as the category dtype, which stores each label once and references it.

df['city'] = df['city'].astype('category')

Spotting Duplicate Rows

Repeated records inflate totals and averages. Find them with duplicated(), which flags each row that has appeared before as True.

df.duplicated().sum()

Dropping Duplicates

Remove repeats with drop_duplicates(). By default it keeps the first occurrence of each row and discards the rest.

df = df.drop_duplicates()

Duplicates by Key Columns

Sometimes only certain columns define a duplicate. Pass subset to detect repeats based on a key like an id, ignoring the other columns.

df.drop_duplicates(subset=['user_id'])

Verify Before You Move On

After fixing types and repeats, run info() once more. Confirming dtypes and row counts protects you from cleaning errors slipping downstream.

df.info()

Quick Check

A price column loaded as object text. Which call safely turns bad entries into NaN?

Recap: Types and Dupes Tamed

You now fix dtypes with to_numeric, astype, and to_datetime, and clear repeats with drop_duplicates. Your table is finally analysis-ready.

常见问题解答

「修正 dtypes 并删除重复行」课时是免费的吗？

是的 — 「修正 dtypes 并删除重复行」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。

「修正 dtypes 并删除重复行」这节课中我会学到什么？

转换类型并删除重复项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 4 节课，共 4 节。

「修正 dtypes 并删除重复行」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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