导入、检查、重复
日常的导入与探索循环
导入、检查、重复 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。
The Daily Rhythm
Most analysis follows a simple loop: import a tool, inspect what you have, then repeat. Master this rhythm and notebooks stop feeling scary.
Import a Library
The first cell almost always brings in your tools. An import loads a library so its functions are ready for the rest of your work.
import pandasGive It a Short Alias
Typing pandas constantly is tiring, so people alias it to pd. The as keyword renames the import to something quick.
import pandas as pdThe Famous Two
Almost every data notebook opens with two imports: pandas as pd for tables and numpy as np for fast math.
import numpy as npLoad Some Data
Now pull data into the notebook. Reading a CSV into a table gives you a DataFrame, the object you will inspect and explore.
df = pd.read_csv("sales.csv")Peek at the Top
Never trust data you have not looked at. Calling head shows the first few rows so you can sanity-check what loaded.
df.head()Check the Shape
How big is this table? The shape attribute returns rows and columns as a pair, telling you the scale at a glance.
df.shapeList the Columns
Before analysis, learn what you have to work with. The columns attribute lists every column name in the table.
df.columnsOne-Line Overview
Want types and missing counts together? The info method prints a compact summary of every column in one go.
df.info()Inspect, Then Refine
What you see guides your next move. You might repeat the loop: import another tool, reload, or inspect a different slice.
Small Steps Beat Big Leaps
Run one cell, read the output, then write the next. This tight loop catches mistakes early instead of after a giant script.
Quick Check
You just loaded a CSV and want a fast look at the first handful of rows.
Recap: Import, Inspect, Repeat
You learned the everyday loop: import your tools, load data, inspect with head, shape, and info, then refine. That is data work in a nutshell. 🔁
常见问题解答
「导入、检查、重复」课时是免费的吗？
是的 — 「导入、检查、重复」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。
「导入、检查、重复」这节课中我会学到什么？
日常的导入与探索循环 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。
「导入、检查、重复」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。