Correlation recap
Correlation describes how two numeric variables move together.
- +1: strong positive relationship
- -1: strong negative relationship
- 0: no linear relationship
flowchart LR A["Numeric columns"] --> B["df.corr()
(square correlation matrix)"] B --> C["sns.heatmap(corr)"] C --> D["Color grid
(+ optional annot=True numbers)"]
Correlation heatmap
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
penguins = sns.load_dataset("penguins").dropna()
corr = penguins[[
"bill_length_mm",
"bill_depth_mm",
"flipper_length_mm",
"body_mass_g",
]].corr()
plt.figure(figsize=(6, 4))
sns.heatmap(corr, annot=True, cmap="coolwarm", vmin=-1, vmax=1)
plt.title("Penguins correlation")
plt.tight_layout()
plt.show()Visualize it
Each cell's color encodes a correlation value from -1 to +1 — amber for strong positive, blue for strong negative, and dim in the middle for near zero:
Tips
- Correlation is not causation.
- Outliers can inflate correlations.
- For non-linear relationships, correlation can be misleading.
Check your understanding
What does a correlation of -1 mean?
+1 is a strong positive relationship, -1 a strong negative one, and 0 means no linear relationship.
Show answer
C — Strong negative relationship — +1 is a strong positive relationship, -1 a strong negative one, and 0 means no linear relationship.
Why does the lesson set vmin=-1, vmax=1 in sns.heatmap?
Without fixed limits the colormap can auto-scale, making a weak correlation look as hot as a strong one.
Show answer
B — To keep the color scale comparable across plots — Without fixed limits the colormap can auto-scale, making a weak correlation look as hot as a strong one.
What does annot=True do?
annot=True prints the numbers inside the cells so you can read exact values.
Show answer
A — Writes each correlation value in its cell — annot=True prints the numbers inside the cells so you can read exact values.
Which is a warning the lesson gives about correlation?
Correlation is not causation, outliers can inflate it, and non-linear relationships can fool it.
Show answer
D — Outliers can inflate it, and it misses non-linear patterns — Correlation is not causation, outliers can inflate it, and non-linear relationships can fool it.
Next
Continue to Pair Plots to see the raw scatter relationships behind each correlation number.
🧪 Try It Yourself
Exercise 1 – Compute a Correlation Matrix
Tab indents. Press Escape, then Tab, to leave the editor. Control plus Enter runs the code; Control plus Shift plus Enter submits it.
Press Run to see what your code prints.Exercise 2 – Draw the Heatmap With Numbers
Tab indents. Press Escape, then Tab, to leave the editor. Control plus Enter runs the code; Control plus Shift plus Enter submits it.
Press Run to see what your code prints.Exercise 3 – Fix the Color Scale
Tab indents. Press Escape, then Tab, to leave the editor. Control plus Enter runs the code; Control plus Shift plus Enter submits it.
Press Run to see what your code prints.