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将字符串解析为日期时间

to_datetime 和格式处理

将字符串解析为日期时间 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。

Dates Hide as Text

When you load a CSV, dates usually arrive as plain strings. They look right but pandas treats them as text, so date math just won't work yet. 📅

Enter to_datetime

The fix is one function: pd.to_datetime. It scans your text and turns each value into a real timestamp pandas can compute with.

import pandas as pd
ts = pd.to_datetime("2024-03-15")
print(ts)

Convert a Whole Column

Pass a Series and you get back a column of timestamps. This is the everyday move right after loading a table with a date field.

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

Check the dtype

After converting, the column's dtype becomes datetime64. That single check tells you the parse actually worked and date math is now unlocked.

print(df["date"].dtype)  # datetime64[ns]

Many Formats, One Call

to_datetime is smart: it reads ISO dates, slashes, and even written months automatically. For tidy ISO strings you rarely need to tell it anything else.

pd.to_datetime(["2024-01-01", "Jan 2, 2024"])

Spell Out the Format

When dates are ambiguous, give an explicit format string. It removes guessing and runs much faster on big columns.

pd.to_datetime("15/03/2024", format="%d/%m/%Y")

Day-First Dates

Is 03/04 March or April? Set dayfirst=True so pandas reads the day before the month, matching most European date styles.

pd.to_datetime("03/04/2024", dayfirst=True)

Bad Values Without Crashing

One messy cell can break a whole parse. Use errors="coerce" to turn unparseable values into NaT instead of raising an error.

pd.to_datetime("not-a-date", errors="coerce")  # NaT

Meet NaT

NaT is the datetime version of NaN: a missing timestamp. You can spot these failed parses with isna and decide how to handle them.

df["date"].isna().sum()  # count failed parses

Combine Date and Time Parts

Have separate date and hour columns? Concatenate them into one string, then parse once. You end up with a single clean timestamp.

pd.to_datetime(df["day"] + " " + df["time"])

Parse at Read Time

You can skip a step by parsing during import. Pass parse_dates to read_csv and the column arrives as datetimes already.

pd.read_csv("sales.csv", parse_dates=["date"])

Quick Check

Your date column has a few junk values you want to keep as missing.

Recap: You Can Parse Dates

You turned text into real timestamps with pd.to_datetime, controlled formats, handled bad values with coerce, and even parsed on load. Date math is now within reach. 🎉

常见问题解答

「将字符串解析为日期时间」课时是免费的吗？

是的 — 「将字符串解析为日期时间」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。

「将字符串解析为日期时间」这节课中我会学到什么？

to_datetime 和格式处理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。

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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。

「将字符串解析为日期时间」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。

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