Matplotlib/Seaborn - Interview Questions and Answers

You can install Matplotlib using pip:

pip install matplotlib

A Matplotlib figure consists of:

• Figure: The overall container holding the plot.
• Axes: The plotting area (can be multiple in a figure).
• Axis: The x and y coordinate system.
• Ticks & Labels: Markings along the axes.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 25, 30, 40]

plt.plot(x, y)
plt.show()

pyplot is a module that provides a simple interface for creating figures. figure is used to create a blank canvas for plotting multiple subplots.

plt.figure(figsize=(10, 5))  # Width=10, Height=5

fig, ax = plt.subplots(2, 2)  # 2x2 grid of plots

plt.xlabel("X-axis Label")
plt.ylabel("Y-axis Label")
plt.title("Plot Title")

plt.show() displays the plot on the screen, while plt.savefig("plot.png") saves it as an image file.

plt.style.use('ggplot')  # Apply the ggplot style

plt.bar(x, y, color='blue')

plt.plot(x, y, label="Line 1")
plt.plot(x, [y_i + 5 for y_i in y], label="Line 2")
plt.legend()

A scatter plot shows individual points, while a line plot connects them.

plt.hist(data, bins=10, color='green')

plt.plot(x, y, linestyle='--', marker='o', color='red')

plt.xticks([0, 2, 4, 6])
plt.yticks([10, 20, 30])

fig, ax1 = plt.subplots()
ax2 = ax1.twinx()

import pandas as pd
data = pd.date_range(start="2023-01-01", periods=10, freq="D")
plt.plot(data, y)

plt.savefig("plot.png", dpi=300)

• Use scatter() instead of plot() for large datasets.
• Reduce transparency with alpha=0.5.
• Optimize with downsampling techniques.

Seaborn is a higher-level visualization library built on Matplotlib that provides better defaults and advanced statistical plotting.

import seaborn as sns
sns.scatterplot(x="column1", y="column2", data=df)

It groups data points by color.

sns.scatterplot(x="x", y="y", hue="category", data=df)

sns.violinplot(x="category", y="value", data=df)

sns.heatmap(data.corr(), annot=True, cmap="coolwarm")

sns.regplot(x="x", y="y", data=df)

g = sns.FacetGrid(df, col="category")
g.map(sns.histplot, "value")

sns.despine()

Use sample(), agg() for aggregation, or FacetGrid for breaking data into smaller plots.

• ImportError: Ensure libraries are installed.
• ValueError: Check data types before plotting.

import numpy as np
x = np.arange(3)
values1 = [5, 7, 9]
values2 = [6, 8, 10]

plt.bar(x - 0.2, values1, width=0.4, label='Group 1')
plt.bar(x + 0.2, values2, width=0.4, label='Group 2')
plt.xticks(x, ['A', 'B', 'C'])
plt.legend()
plt.show()

plt.text(2, 15, "Annotation Here", fontsize=12, color='red')

alpha controls the transparency of a plot.

plt.scatter(x, y, alpha=0.5)

plt.grid(True, linestyle='--', linewidth=0.5)

plt.bar(x, values1, label="Category 1")
plt.bar(x, values2, bottom=values1, label="Category 2")
plt.legend()

plt.xscale("log")
plt.yscale("log")

plt.legend(loc='upper left', bbox_to_anchor=(1,1))

labels = ['A', 'B', 'C']
sizes = [40, 35, 25]
plt.pie(sizes, labels=labels, autopct='%1.1f%%')

plt.title("Title", fontsize=14, fontweight='bold')

Use bar() with cumulative sums and a baseline shift.

sns.boxplot(x="category", y="value", data=df)

• It sets the color scheme for the plots.

sns.scatterplot(x="x", y="y", data=df, palette="coolwarm")

sns.kdeplot(df["column"], shade=True)

sns.pairplot(df)

• stripplot() places points randomly along the x-axis.
• swarmplot() avoids overlapping points.

sns.swarmplot(x="category", y="value", data=df)

plt.figure(figsize=(10, 5))

sns.heatmap(df.corr(), annot=True, cmap="Blues")

A FacetGrid allows multiple plots to be displayed side by side for different subsets of the data.

sns.jointplot(x="x", y="y", data=df, kind="scatter")

fig, ax = plt.subplots(2, 2)

plt.scatter(x, y, c=y, cmap="viridis")

Seaborn provides better aesthetics and statistical plots.

%matplotlib notebook

sns.set_theme(style="darkgrid")

sns.set_theme() provides better control over aesthetics.

for index, value in enumerate(y):
    plt.text(index, value, str(value))

sns.heatmap(df.corr(), annot=True)

plt.violinplot(data)

• Large datasets slow down rendering.
• Incompatible versions cause unexpected behavior.
• Matplotlib styles can sometimes override Seaborn themes.