1. What is Matplotlib?
A 2D plotting library for creating static, animated, and interactive visualizations in Python. It's the foundation for most data viz in Python, with full customization control.
2. How to create a basic line plot in Matplotlib?
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()
3. What is Seaborn and how is it different?
Seaborn is built on top of Matplotlib and makes complex plots simpler with better aesthetics. It integrates well with Pandas DataFrames, offering high-level functions for statistical viz like heatmaps or violin plots—less code, prettier defaults than raw Matplotlib.
4. How to create a bar plot with Seaborn?
import seaborn as sns
sns.barplot(x='category', y='value', data=df)
5. How to customize plot titles, labels, legends?
plt.title('Sales Over Time')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.legend()6. What is a heatmap and when do you use it?
A heatmap visualizes matrix-like data using colors. Often used for correlation matrices.
sns.heatmap(df.corr(), annot=True)
7. How to plot multiple plots in one figure?
plt.subplot(1, 2, 1) # 1 row, 2 cols, plot 1
plt.plot(data1)
plt.subplot(1, 2, 2)
plt.plot(data2)
plt.show()
8. How to save a plot as an image file?
plt.savefig('plot.png')9. When to use boxplot vs violinplot?
⦁ Boxplot: Summary of distribution (median, IQR) for quick outliers.
⦁ Violinplot: Adds distribution shape (kernel density) for richer insights into data spread.
10. How to set plot style in Seaborn?
sns.set_style("whitegrid")Double Tap ❤️ For More!