Data Visualization

Learn Python's three major visualization libraries — Matplotlib, Seaborn, and Plotly — through practical examples.

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Installation

pip install matplotlib seaborn plotly

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Matplotlib — Basic Graphs

import matplotlib.pyplot as plt
import numpy as np

# Basic line graph
x = np.linspace(0, 2 * np.pi, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.figure(figsize=(10, 4))
plt.plot(x, y1, label="sin(x)", color="blue", linewidth=2)
plt.plot(x, y2, label="cos(x)", color="red", linestyle="--")
plt.xlabel("x")
plt.ylabel("y")
plt.title("Trigonometric Functions")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig("trig.png", dpi=150)
plt.show()

# Multiple plots (subplot)
fig, axes = plt.subplots(2, 2, figsize=(12, 8))

# Scatter plot
axes[0, 0].scatter(x_data, y_data, alpha=0.6, c=colors, cmap="viridis")
axes[0, 0].set_title("Scatter Plot")

# Bar chart
categories = ["A", "B", "C", "D"]
values = [23, 45, 12, 67]
axes[0, 1].bar(categories, values, color="steelblue", edgecolor="black")
axes[0, 1].set_title("Bar Chart")

# Histogram
data = np.random.randn(1000)
axes[1, 0].hist(data, bins=30, color="skyblue", edgecolor="white")
axes[1, 0].set_title("Histogram")

# Pie chart
sizes = [30, 25, 20, 15, 10]
labels = ["Python", "JavaScript", "Java", "Go", "Rust"]
axes[1, 1].pie(sizes, labels=labels, autopct="%1.1f%%", startangle=90)
axes[1, 1].set_title("Language Market Share")

plt.tight_layout()
plt.show()

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Seaborn — Statistical Visualization

import seaborn as sns
import pandas as pd

# Set default theme
sns.set_theme(style="whitegrid", palette="muted")

# Sample datasets
tips = sns.load_dataset("tips")       # restaurant tips data
iris = sns.load_dataset("iris")       # iris flower data


# 1. boxplot — distribution and outliers
plt.figure(figsize=(10, 5))
sns.boxplot(data=tips, x="day", y="total_bill", hue="sex")
plt.title("Total Bill by Day")
plt.show()


# 2. violinplot — shows distribution shape
sns.violinplot(data=tips, x="day", y="tip", hue="smoker", split=True)


# 3. heatmap — correlation matrix
plt.figure(figsize=(8, 6))
corr = tips.select_dtypes(include="number").corr()
sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", vmin=-1, vmax=1)
plt.title("Correlation Heatmap")
plt.show()


# 4. pairplot — relationships between all variable pairs
sns.pairplot(iris, hue="species", diag_kind="kde")
plt.show()


# 5. scatterplot + regression line
sns.lmplot(data=tips, x="total_bill", y="tip", hue="smoker",
           col="time", height=5)


# 6. barplot (mean + confidence interval)
sns.barplot(data=tips, x="day", y="total_bill", estimator="mean",
            errorbar="ci", palette="Blues_d")


# 7. countplot (frequency)
sns.countplot(data=tips, x="day", order=["Thur", "Fri", "Sat", "Sun"])

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Plotly — Interactive Charts

import plotly.express as px
import plotly.graph_objects as go

# Sample data
df = px.data.gapminder()

# 1. Scatter (bubble chart)
fig = px.scatter(
    df[df.year == 2007],
    x="gdpPercap",
    y="lifeExp",
    size="pop",
    color="continent",
    hover_name="country",
    log_x=True,
    title="2007 GDP vs Life Expectancy",
)
fig.show()


# 2. Line graph (time series)
korea = df[df.country == "Korea, Rep."]
fig = px.line(korea, x="year", y="gdpPercap", markers=True,
              title="South Korea GDP Over Time")
fig.update_traces(line_width=3)
fig.show()


# 3. Bar chart
fig = px.bar(
    df[df.year == 2007].nlargest(10, "pop"),
    x="country", y="pop",
    color="continent",
    title="Top 10 Countries by Population",
)
fig.show()


# 4. Animation (change over time)
fig = px.scatter(
    df,
    x="gdpPercap", y="lifeExp",
    size="pop", color="continent",
    animation_frame="year",
    animation_group="country",
    range_x=[100, 100000], range_y=[20, 90],
    log_x=True,
    title="GDP vs Life Expectancy Over Time",
)
fig.show()


# 5. Graph Objects — fine-grained control
fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6], mode="lines+markers", name="Data1"))
fig.add_trace(go.Bar(x=["A", "B", "C"], y=[10, 20, 15], name="Data2"))
fig.update_layout(
    title="Combined Chart",
    xaxis_title="X Axis",
    yaxis_title="Y Axis",
    template="plotly_white",
)
fig.show()

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Saving Charts

# Matplotlib
plt.savefig("chart.png", dpi=300, bbox_inches="tight")
plt.savefig("chart.pdf")  # vector format

# Plotly
fig.write_html("chart.html")       # interactive HTML
fig.write_image("chart.png")       # static image (requires kaleido)
# pip install kaleido

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Library Selection Guide

Matplotlib
  ✅ When you need complete control
  ✅ Publication/report quality output
  ✅ Custom chart implementation
  ❌ Requires more code

Seaborn
  ✅ Statistical visualization (distributions, relationships, categories)
  ✅ Perfect compatibility with pandas DataFrames
  ✅ Beautiful charts with minimal code
  ❌ No interactive features

Plotly
  ✅ Web dashboards, interactive exploration
  ✅ Animations, 3D charts
  ✅ Build web apps with Dash integration
  ❌ Larger file sizes

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Summary

Library	Strength	Key Charts
Matplotlib	Fine-grained control	line/bar/scatter/pie
Seaborn	Statistical visualization	boxplot/heatmap/pairplot
Plotly	Interactive	bubble/animation/3D

Use Seaborn for data exploration, Plotly for final presentations, and Matplotlib for papers and reports.