读取相关性热力图
快速发现冗余特征
读取相关性热力图 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。
Why a Heatmap
With many columns, reading correlations one pair at a time is painful. A heatmap shows every pairing at once as a grid of colors. 🔥
Start With corr
First build a correlation matrix. In pandas, calling corr on a numeric DataFrame returns a square table of every column paired with every other.
corr = df.corr(numeric_only=True)
print(corr.round(2))Plot It With seaborn
Then turn that matrix into color. The seaborn heatmap function maps each cell to a shade, so patterns jump out instantly.
import seaborn as sns
sns.heatmap(corr, annot=True, cmap="coolwarm")Reading the Colors
Warm colors usually mark strong positive links and cool colors mark negative ones. Pale, washed-out cells sit near zero, signaling little relationship.
The Diagonal Is Always One
The main diagonal is always 1, since every column correlates perfectly with itself. Ignore it; the real story lives in the off-diagonal cells.
The Grid Is Symmetric
A heatmap is symmetric across the diagonal because the link between A and B equals the link between B and A. You only need to read one triangle.
Annotate the Numbers
Set annot to True so each cell prints its exact value. Color shows the big picture while the numbers let you confirm the precise strength.
Choose a Diverging Palette
Pick a diverging colormap like coolwarm so negative and positive get opposite hues, with zero sitting neutral in the middle. It makes direction obvious.
Spotting Redundant Features
Two features that are deeply correlated are often redundant. The heatmap flags them so you can drop one and simplify a model without losing much signal.
Hunt for the Target
Scan the row or column for your target variable. The strongest cells there hint at which features may carry the most predictive value.
Mask Half for Clarity
To cut clutter, many analysts hide the upper triangle with a mask. The result reads cleaner while keeping every meaningful pair visible.
Quick Check
You spot two features with a correlation near 0.97 on your heatmap. What is a common next step?
Recap
Build a matrix with corr, plot it with seaborn, and read warm and cool cells. Ignore the diagonal, watch for redundant features, and scan the target row. ✅
常见问题解答
「读取相关性热力图」课时是免费的吗？
是的 — 「读取相关性热力图」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。
「读取相关性热力图」这节课中我会学到什么？
快速发现冗余特征 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。
「读取相关性热力图」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。