Excellent.
That’s a powerful move — mastering Matplotlib + Seaborn together gives you complete
control over both low-level (custom) and high-level (statistical) visualizations, making you look like
a data storytelling pro.
30-Day Combined Matplotlib + Seaborn Mastery Roadmap
This will train you to build:
Beautiful exploratory plots (Seaborn)
Highly customized visuals (Matplotlib)
Smart data insights (with Pandas/NumPy underneath)
30-Day Plan
Day Matplotlib Focus Seaborn Focus
1 Install & import matplotlib.pyplot Install & import seaborn as sns
2 Simple plt.plot() line plot sns.set_theme() basic styling
3 plt.scatter() scatter plots sns.scatterplot()
4 plt.bar(), plt.barh() sns.barplot() with estimator=mean
5 plt.hist() histograms sns.histplot()
6 plt.boxplot() boxplots sns.boxplot()
7 Titles, labels, grid sns.set_style() (white, dark)
8 plt.legend() Seaborn built-in legend control
9 Ticks: xticks, yticks Seaborn smart tick management
10 Colors, markers, linestyles palette, hue for color separation
11 Multiple lines on same axes sns.lineplot() with hue
12 Subplots: plt.subplot() FacetGrid basics
13 plt.subplots() & ax sns.catplot() multiple facets
14 Grid layouts + figure size sns.FacetGrid with col or row
15 plt.style.use() Seaborn themes: darkgrid, ticks
16 Annotate points: plt.annotate() Seaborn point annotations via ax
17 Save figures: plt.savefig() Same with Seaborn plots
Day Matplotlib Focus Seaborn Focus
18 plt.imshow() heatmaps sns.heatmap()
19 Advanced annotations on plots sns.heatmap(annot=True)
20 plt.errorbar() sns.pointplot() with CI
21 Hist2D & contour plots sns.kdeplot()
22 Axes limits: plt.xlim, ylim Seaborn auto-limits
23 Twin axes: ax.twinx() Layer Seaborn on Matplotlib axes
24 Seaborn pairplots for EDA sns.pairplot()
25 Seaborn correlation plots sns.heatmap(df.corr())
26 Advanced FacetGrid customization
27 Matplotlib custom ticks & rotation Seaborn + ax.set_xticklabels()
28 Build a complex dashboard plot grid Mix Seaborn inside plt.subplots()
29 Small case study on dataset (Titanic) Combined Matplotlib + Seaborn EDA
30 Review + build your own style template Create a “signature style” template
Smart daily structure
Each day:
• 20 min Matplotlib practice (try 3 plots)
• 20 min Seaborn practice (use same dataset)
• 20 min experiment: customize style, add annotations, mix the two.
Essential free resources
Matplotlib
• Official Pyplot Tutorial
• Matplotlib Cheatsheets
Seaborn
• Seaborn Tutorial
• Seaborn API reference
• Kaggle Seaborn Micro-course
Both Together
• Python Graph Gallery — brilliant examples.
• Python Data Science Handbook by Jake VanderPlas
Cheat code mini notes
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = sns.load_dataset('tips')
# Matplotlib
plt.figure(figsize=(8,5))
plt.plot(df['total_bill'])
plt.title("Total Bills Over Index")
plt.xlabel("Index")
plt.ylabel("Total Bill")
plt.grid(True)
plt.show()
# Seaborn
sns.set_style("darkgrid")
sns.scatterplot(x='total_bill', y='tip', data=df, hue='time', style='time')
plt.title("Tip vs Total Bill by Time")
plt.show()
Forward-thinking power tips
Use:
• Matplotlib for ultimate control (e.g. multi-axis plots, exact ticks, manual annotations).
• Seaborn for quick statistical plots (boxplots, violinplots, FacetGrid EDA).
Always bind Seaborn plots to Matplotlib axes if you want fine control:
fig, ax = plt.subplots()
sns.boxplot(x='day', y='total_bill', data=df, ax=ax)
ax.set_title("Boxplot with Custom Title")
Build your signature style sheet by tweaking:
plt.style.use('seaborn-darkgrid')
sns.set_context("talk", font_scale=1.2)
Want me to prepare:
• A printable one-page combined roadmap for Matplotlib + Seaborn,
• Or a CSV daily checklist to track your progress?
Just tell me which you’d prefer — I can instantly make it for you.
