Chapter 9 — Data Visualization with Matplotlib, Seaborn, and Plotly
Visualization reveals patterns numbers hide. This chapter tours the three main Python plotting libraries: Matplotlib for fine control, Seaborn for statistical plots, and Plotly for interactive graphics.
Learning Objectives
- Create line, bar, scatter, histogram, and box plots with Matplotlib.
- Use Seaborn for statistical, good-looking defaults.
- Build interactive plots with Plotly.
- Choose the right chart for the data and question.
- Label axes, add titles, and save figures.
Prerequisites / Imports
In [1]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
sns.set_theme(style='whitegrid')
1 The Grammar of Matplotlib
A Figure holds one or more Axes. Plot, then label, then show.
In [1]:
x = np.linspace(0, 10, 100)
fig, ax = plt.subplots(figsize=(7,4))
ax.plot(x, np.sin(x), label='sin')
ax.plot(x, np.cos(x), label='cos')
ax.set_title('Sine and Cosine')
ax.set_xlabel('x'); ax.set_ylabel('y')
ax.legend()
plt.show()
2 Bar Charts
Compare categorical quantities.
In [1]:
revenue = pd.DataFrame({'product':['A','B','C','D'], 'sales':[250, 180, 320, 140]})
fig, ax = plt.subplots(figsize=(7,4))
ax.bar(revenue['product'], revenue['sales'], color='steelblue')
ax.set_title('Sales by Product')
ax.set_ylabel('Sales ($)')
plt.show()
3 Histograms
Show the distribution of a numeric variable.
In [1]:
tips = sns.load_dataset('tips')
plt.figure(figsize=(7,4))
plt.hist(tips['total_bill'], bins=20, color='coral', edgecolor='black')
plt.title('Distribution of Total Bill')
plt.xlabel('Total bill ($)'); plt.ylabel('Frequency')
plt.show()
4 Scatter Plots
Reveal relationships between two numeric variables.
In [1]:
plt.figure(figsize=(7,4))
plt.scatter(tips['total_bill'], tips['tip'], alpha=0.6)
plt.title('Tip vs Total Bill')
plt.xlabel('Total bill ($)'); plt.ylabel('Tip ($)')
plt.show()
5 Seaborn Statistical Plots
Seaborn makes attractive statistical graphics with little code.
In [1]:
plt.figure(figsize=(7,4))
sns.boxplot(data=tips, x='day', y='total_bill')
plt.title('Total bill by day')
plt.show()
In [1]:
penguins = sns.load_dataset('penguins').dropna()
plt.figure(figsize=(7,5))
sns.scatterplot(data=penguins, x='flipper_length_mm', y='body_mass_g', hue='species')
plt.title('Penguin flipper length vs body mass')
plt.show()
6 Correlation Heatmap
A heatmap of the correlation matrix spotlights linear relationships.
In [1]:
num = tips.select_dtypes('number').corr()
plt.figure(figsize=(6,5))
sns.heatmap(num, annot=True, cmap='coolwarm', vmin=-1, vmax=1)
plt.title('Correlation matrix (tips)')
plt.show()
7 Interactive Plots with Plotly
Plotly figures are interactive (zoom, hover). We render a static PNG here; in a live notebook the figure is fully interactive.
In [1]:
import plotly.express as px
tips = sns.load_dataset('tips')
fig = px.scatter(tips, x='total_bill', y='tip', color='time',
size='size', hover_data=['day'],
title='Tip vs Total Bill (interactive)')
fig
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'legendgroup': 'Dinner',
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'sizemode': 'area',
'sizeref': 0.015,
'symbol': 'circle'},
'mode': 'markers',
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'showlegend': True,
'type': 'scatter',
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4.08, 2.75, 2.23, 7.58, 3.18, 2.34, 2. , 2. , 4.3 , 3. ,
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2.24, 2.54, 3.06, 1.32, 5.6 , 3. , 5. , 6. , 2.05, 3. ,
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3.21, 2. , 1.98, 3.76, 2.64, 3.15, 2.47, 1. , 2.01, 2.09,
1.97, 3. , 3.14, 5. , 2.2 , 1.25, 3.08, 3. , 3.5 , 1. ,
4.3 , 3.25, 4.73, 4. , 1.5 , 3. , 1.5 , 2.5 , 3. , 2.5 ,
3.48, 4.08, 1.64, 4.06, 4.29, 3.76, 4. , 3. , 1. , 4. ,
2.55, 4. , 3.5 , 5.07, 2.5 , 2. , 2.74, 2. , 2. , 5.14,
5. , 3.75, 2.61, 2. , 3.5 , 2.5 , 2. , 2. , 3. , 3.48,
2.24, 4.5 , 1.61, 2. , 10. , 3.16, 5.15, 3.18, 4. , 3.11,
2. , 2. , 4. , 3.55, 3.68, 5.65, 3.5 , 6.5 , 3. , 5. ,
3.5 , 2. , 3.5 , 4. , 1.5 , 3.41, 3. , 2.03, 2.23, 2. ,
5.16, 9. , 2.5 , 6.5 , 1.1 , 3. , 1.5 , 1.44, 3.09, 3. ,
2.72, 2.88, 2. , 3. , 3.39, 1.47, 3. , 1.25, 1. , 1.17,
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'yaxis': 'y'},
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'hovertemplate': ('time=Lunch<br>total_bill=%{x}<' ... '{customdata[0]}<extra></extra>'),
'legendgroup': 'Lunch',
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'yaxis': 'y'}],
'layout': {'legend': {'itemsizing': 'constant', 'title': {'text': 'time'}, 'tracegroupgap': 0},
'template': '...',
'title': {'text': 'Tip vs Total Bill (interactive)'},
'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'total_bill'}},
'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'tip'}}}
})In [1]:
fig2 = px.bar(tips.groupby('day', as_index=False)['total_bill'].mean(),
x='day', y='total_bill', title='Average total bill by day')
fig2
<cell-prefix>:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
Figure({
'data': [{'alignmentgroup': 'True',
'hovertemplate': 'day=%{x}<br>total_bill=%{y}<extra></extra>',
'legendgroup': '',
'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
'name': '',
'offsetgroup': '',
'orientation': 'v',
'showlegend': False,
'textposition': 'auto',
'type': 'bar',
'x': array(['Thur', 'Fri', 'Sat', 'Sun'], dtype=object),
'xaxis': 'x',
'y': array([17.68274194, 17.15157895, 20.44137931, 21.41 ]),
'yaxis': 'y'}],
'layout': {'barmode': 'relative',
'legend': {'tracegroupgap': 0},
'template': '...',
'title': {'text': 'Average total bill by day'},
'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'day'}},
'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'total_bill'}}}
})8 Choosing the Right Chart
| Question | Chart |
|---|---|
| Distribution of one variable | histogram, box plot |
| Relationship of two variables | scatter, line |
| Comparison across categories | bar chart |
| Part of a whole | stacked bar, pie (sparingly) |
| Correlation among many variables | heatmap |
Case Study: Sales Dashboard Elements
Combine a bar chart of sales by category and a line chart of monthly sales — the building blocks of a business dashboard.
In [1]:
rng = np.random.default_rng(1)
cats = ['Electronics','Clothing','Books','Home']
sales_by_cat = pd.DataFrame({'category':cats, 'sales':rng.integers(1000,5000,size=4)})
months = pd.date_range('2026-01-01', periods=12, freq='MS')
monthly = pd.DataFrame({'month':months, 'sales':np.cumsum(rng.normal(200, 40, size=12)).round(0)})
fig, axes = plt.subplots(1, 2, figsize=(12,4))
axes[0].bar(sales_by_cat['category'], sales_by_cat['sales'], color='teal')
axes[0].set_title('Sales by category'); axes[0].set_ylabel('Sales ($)')
axes[1].plot(monthly['month'], monthly['sales'], marker='o', color='darkorange')
axes[1].set_title('Cumulative monthly sales'); axes[1].tick_params(axis='x', rotation=45)
plt.tight_layout()
plt.show()
<cell-expr>:1: UserWarning:
Exercises
- Plot the functions $y=x^2$ and $y=x^3$ on the same axes for $x\in[-2,2]$.
- Make a bar chart of five cities and their populations.
- Draw a histogram with 30 bins of a normal random sample of 1000 points.
- Create a scatter plot of two numeric columns and color by a category.
- Use Seaborn to make a box plot of total bill by smoker status.
- Produce a correlation heatmap of the penguins numeric columns.
- Build an interactive Plotly scatter of penguin flipper vs body mass.
- Save a Matplotlib figure to a PNG file with
plt.savefig. - Create a 2x1 subplot with a line chart and a bar chart.
- Explain when you would choose a heatmap over a scatter plot.
Python Data Science: From Foundations to Applications — Chapter 9