Correlation matrix visualization in Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a correlation matrix?

A correlation matrix is a table showing correlation coefficients between many variables. Each cell shows how two variables move together, with values from -1 (perfect negative) to 1 (perfect positive).

beginner

Why do we visualize a correlation matrix?

Visualizing a correlation matrix helps us quickly see patterns, strong or weak relationships, and groups of variables that behave similarly. It makes understanding data easier than just numbers.

beginner

Which Python library is commonly used to create heatmaps for correlation matrices?

Seaborn is commonly used, often together with matplotlib, to create heatmaps that color-code correlation values for easy visual analysis.

intermediate

What does a dark color in a correlation heatmap usually represent?

A dark color often represents a strong correlation, either positive or negative, depending on the color scale used. For example, dark red might mean strong positive correlation.

intermediate

How can you improve readability of a correlation matrix heatmap?

You can add labels, use a clear color scale, rotate axis labels for better fit, and add a color bar legend to explain the colors.

What value in a correlation matrix indicates no linear relationship between two variables?

A 0.5

B 1

C -1

D 0

A correlation coefficient of 0 means no linear relationship between variables.

Which Python function from matplotlib can be used to display a correlation matrix as a heatmap?

A plt.imshow()

B plt.scatter()

C plt.plot()

D plt.bar()

plt.imshow() displays matrix data as an image, useful for heatmaps.

What does a correlation coefficient of -1 signify?

A Perfect positive correlation

B Perfect negative correlation

C No correlation

D Weak correlation

A value of -1 means variables move perfectly in opposite directions.

Which color scheme is commonly used for correlation heatmaps?

A Grayscale

B Rainbow

C Diverging (e.g., blue to red)

D Random colors

Diverging color schemes highlight positive and negative correlations clearly.

What is the main purpose of adding a color bar to a correlation heatmap?

A To show the scale of correlation values

B To decorate the plot

C To add axis labels

D To display data points

A color bar explains what correlation values the colors represent.

Explain how to create and visualize a correlation matrix using matplotlib.

Think about steps from data to visual output.

Describe how to interpret colors in a correlation matrix heatmap.

Focus on what color means about relationships.