先单变量，再双变量
从单列分析到关系分析
先单变量，再双变量 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 Column, Then Two
Good EDA has an order. First understand each column alone, then study how pairs move together. Start univariate, finish bivariate. 📊
What Univariate Means
Looking at a single column on its own is univariate analysis. You ask what is typical and how spread out the values are.
Histograms Show Shape
For one numeric column, a histogram reveals its shape: bell, skewed, or lumpy. Shape hints at how to model it later.
df["age"].hist()Bar Charts for Categories
For a single label column, a bar chart of value_counts shows which categories dominate and which are rare.
df["plan"].value_counts().plot.bar()Center and Spread
Each numeric column has a center, like the mean, and a spread, like the standard deviation. Together they summarize it in two numbers.
Now Compare Two Columns
Once columns make sense alone, study pairs. Bivariate analysis asks whether two variables rise and fall together.
Number vs Number
For two numeric columns, a scatter plot shows whether they trend together, oppose, or scatter with no link at all.
df.plot.scatter(x="tenure", y="charges")Category vs Number
To compare a number across groups, group then summarize. A grouped mean shows how charges differ by plan in one line.
df.groupby("plan")["charges"].mean()Category vs Category
For two label columns, a crosstab counts every combination, so you can see which pairs appear together often.
pd.crosstab(df["plan"], df["churn"])Quantify the Link
A scatter hints, but a number confirms. The correlation between two numeric columns measures how tightly they move together.
df["tenure"].corr(df["charges"])Always Build Up in Order
Jumping to relationships before knowing each column hides traps. The univariate-first order keeps your conclusions trustworthy.
Quick Check
You want to see if two numeric columns move together.
Recap: Single Then Paired
You explore one column with histograms and bars, then pairs with scatters, groupings, and correlation. Order keeps insights honest. ✅
常见问题解答
「先单变量，再双变量」课时是免费的吗？
是的 — 「先单变量，再双变量」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。
「先单变量，再双变量」这节课中我会学到什么？
从单列分析到关系分析 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。
「先单变量，再双变量」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。