Setting Up Jupyter, Inline Plotting & Figure Sizing0%
Setting Up Jupyter, Inline Plotting & Figure Sizing
Beginner12 min read•Updated: Jul 11, 2026
Setting Up Jupyter, Inline Plotting & Figure Sizing
Reading Time: 8 Mins | Level: Intermediate
1. Setting Up the Visualization Environment
To create clean charts in Jupyter Notebooks or VS Code:
Python
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# Enable inline rendering inside Jupyter Notebooks
%matplotlib inline
# Set high-resolution display for Retina / 4K screens
%config InlineBackend.figure_format = 'retina'
# Apply standard clean styling
sns.set_theme(style='whitegrid')
plt.rcParams['font.sans-serif'] = 'DejaVu Sans'
2. Managing Figure Size & DPI
Never accept default cramped charts. Always control canvas dimensions:
Python
# figsize=(width_in_inches, height_in_inches), dpi=dots_per_inch
fig, ax = plt.subplots(figsize=(10, 6), dpi=150)
Interactive Quiz
Multiple Choice Questions
Question 1
In Jupyter Notebooks, what magic command ensures that generated plots are displayed directly beneath the code cell?
Question 2
In
plt.subplots(figsize=(12, 6)), what units do the numbers 12 and 6 represent?Question 3
What does setting
dpi=300 achieve when creating or saving a figure?Question 4
Which function sets the global visual aesthetic theme across all Seaborn and Matplotlib plots?
Question 5
What does
plt.tight_layout() do?Next Lesson
Matplotlib Anatomy: Figure, Axes, Subplots & Ticks
Continue learning with hands-on practice, examples, and exercises in the upcoming topic.
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Practice Quiz
Test your understanding of this lesson with 5 questions. Each question has one correct answer.