Why Python for Visualization?
Python offers powerful libraries for creating publication-quality visualizations. Matplotlib provides complete control over every aspect of your plots, while Seaborn builds on top of it to offer beautiful statistical visualizations with minimal code.
Together, they form the foundation of data visualization in Python's data science ecosystem.
Matplotlib Basics
import matplotlib.pyplot as plt
import numpy as np
# Basic Line Plot
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.figure(figsize=(10, 6))
plt.plot(x, y, label='sin(x)')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Simple Line Plot')
plt.legend()
plt.grid(True)
plt.show()
# Multiple Lines
plt.figure(figsize=(10, 6))
plt.plot(x, np.sin(x), label='sin(x)', color='blue')
plt.plot(x, np.cos(x), label='cos(x)', color='red')
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Multiple Lines')
plt.legend()
plt.show()
# Line Styles
plt.plot(x, y, linestyle='--') # Dashed
plt.plot(x, y, linestyle='-.') # Dash-dot
plt.plot(x, y, linewidth=2) # Line width
plt.plot(x, y, marker='o') # With markersBar Charts
import matplotlib.pyplot as plt
# Simple Bar Chart
categories = ['A', 'B', 'C', 'D', 'E']
values = [23, 45, 56, 78, 32]
plt.figure(figsize=(10, 6))
plt.bar(categories, values, color='steelblue')
plt.xlabel('Category')
plt.ylabel('Value')
plt.title('Bar Chart')
plt.show()
# Horizontal Bar Chart
plt.figure(figsize=(10, 6))
plt.barh(categories, values, color='coral')
plt.xlabel('Value')
plt.ylabel('Category')
plt.title('Horizontal Bar Chart')
plt.show()
# Grouped Bar Chart
x = np.arange(len(categories))
width = 0.35
values2 = [30, 35, 60, 65, 40]
fig, ax = plt.subplots(figsize=(10, 6))
bars1 = ax.bar(x - width/2, values, width, label='2023')
bars2 = ax.bar(x + width/2, values2, width, label='2024')
ax.set_xlabel('Category')
ax.set_ylabel('Value')
ax.set_title('Grouped Bar Chart')
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.legend()
plt.show()
# Stacked Bar Chart
fig, ax = plt.subplots(figsize=(10, 6))
ax.bar(categories, values, label='Product A')
ax.bar(categories, values2, bottom=values, label='Product B')
ax.legend()
plt.show()Scatter Plots & Histograms
# Scatter Plot
np.random.seed(42)
x = np.random.randn(100)
y = x + np.random.randn(100) * 0.5
plt.figure(figsize=(10, 6))
plt.scatter(x, y, alpha=0.6, c='purple', s=50)
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Scatter Plot')
plt.show()
# Scatter with Color Mapping
colors = np.random.randn(100)
sizes = np.abs(np.random.randn(100)) * 100
plt.figure(figsize=(10, 6))
scatter = plt.scatter(x, y, c=colors, s=sizes,
alpha=0.6, cmap='viridis')
plt.colorbar(scatter, label='Value')
plt.title('Scatter with Color & Size')
plt.show()
# Histogram
data = np.random.randn(1000)
plt.figure(figsize=(10, 6))
plt.hist(data, bins=30, edgecolor='black', alpha=0.7)
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram')
plt.show()
# Multiple Histograms
data1 = np.random.normal(0, 1, 1000)
data2 = np.random.normal(2, 1.5, 1000)
plt.figure(figsize=(10, 6))
plt.hist(data1, bins=30, alpha=0.5, label='Group 1')
plt.hist(data2, bins=30, alpha=0.5, label='Group 2')
plt.legend()
plt.title('Overlapping Histograms')
plt.show()Subplots & Layouts
# Multiple Subplots
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
# Top-left: Line plot
axes[0, 0].plot(x, np.sin(x))
axes[0, 0].set_title('Line Plot')
# Top-right: Bar chart
axes[0, 1].bar(categories, values)
axes[0, 1].set_title('Bar Chart')
# Bottom-left: Scatter plot
axes[1, 0].scatter(x[:50], np.cos(x[:50]))
axes[1, 0].set_title('Scatter Plot')
# Bottom-right: Histogram
axes[1, 1].hist(data, bins=20)
axes[1, 1].set_title('Histogram')
plt.tight_layout()
plt.show()
# Different Subplot Sizes
fig = plt.figure(figsize=(12, 8))
# Large plot on left
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(x, np.sin(x))
ax1.set_title('Main Plot')
# Two smaller plots on right
ax2 = fig.add_subplot(2, 2, 2)
ax2.bar(['A', 'B', 'C'], [1, 2, 3])
ax3 = fig.add_subplot(2, 2, 4)
ax3.scatter([1, 2, 3], [1, 4, 9])
plt.tight_layout()
plt.show()Seaborn Introduction
import seaborn as sns
import pandas as pd
# Set style
sns.set_theme(style="whitegrid")
# Load sample dataset
tips = sns.load_dataset("tips")
# Distribution Plot
plt.figure(figsize=(10, 6))
sns.histplot(data=tips, x="total_bill", kde=True)
plt.title('Distribution of Total Bill')
plt.show()
# Box Plot
plt.figure(figsize=(10, 6))
sns.boxplot(data=tips, x="day", y="total_bill", hue="smoker")
plt.title('Total Bill by Day and Smoker Status')
plt.show()
# Violin Plot
plt.figure(figsize=(10, 6))
sns.violinplot(data=tips, x="day", y="total_bill", hue="sex")
plt.title('Total Bill Distribution by Day and Gender')
plt.show()Seaborn Statistical Plots
# Scatter Plot with Regression
plt.figure(figsize=(10, 6))
sns.regplot(data=tips, x="total_bill", y="tip")
plt.title('Tip vs Total Bill with Regression Line')
plt.show()
# Scatter with Categories
plt.figure(figsize=(10, 6))
sns.scatterplot(data=tips, x="total_bill", y="tip",
hue="day", size="size", sizes=(20, 200))
plt.title('Tips Analysis')
plt.show()
# Pair Plot - All numeric relationships
sns.pairplot(tips, hue="smoker", diag_kind="kde")
plt.suptitle('Pair Plot of Tips Dataset', y=1.02)
plt.show()
# Heatmap - Correlation Matrix
plt.figure(figsize=(8, 6))
correlation = tips[['total_bill', 'tip', 'size']].corr()
sns.heatmap(correlation, annot=True, cmap='coolwarm',
center=0, square=True)
plt.title('Correlation Matrix')
plt.show()Categorical Plots
# Count Plot
plt.figure(figsize=(10, 6))
sns.countplot(data=tips, x="day", hue="sex")
plt.title('Count of Visits by Day and Gender')
plt.show()
# Bar Plot (with aggregation)
plt.figure(figsize=(10, 6))
sns.barplot(data=tips, x="day", y="total_bill",
hue="sex", estimator=np.mean)
plt.title('Average Total Bill by Day and Gender')
plt.show()
# Strip Plot
plt.figure(figsize=(10, 6))
sns.stripplot(data=tips, x="day", y="total_bill",
hue="smoker", dodge=True, alpha=0.7)
plt.title('Individual Bills by Day')
plt.show()
# Swarm Plot
plt.figure(figsize=(10, 6))
sns.swarmplot(data=tips, x="day", y="total_bill", hue="sex")
plt.title('Bills Distribution (Swarm)')
plt.show()
# Cat Plot - FacetGrid for categories
g = sns.catplot(data=tips, x="day", y="total_bill",
hue="smoker", col="time", kind="box")
g.fig.suptitle('Total Bill Analysis', y=1.02)
plt.show()FacetGrid & Multi-Plot Layouts
# FacetGrid for multiple plots
g = sns.FacetGrid(tips, col="time", row="smoker",
height=4, aspect=1.2)
g.map(sns.scatterplot, "total_bill", "tip")
g.add_legend()
plt.show()
# Relational Plot with facets
sns.relplot(data=tips, x="total_bill", y="tip",
hue="day", col="time", row="smoker",
kind="scatter", height=4)
plt.show()
# Distribution across facets
g = sns.displot(data=tips, x="total_bill",
col="day", col_wrap=2,
kde=True, height=4)
plt.show()
# Joint Plot - Bivariate analysis
sns.jointplot(data=tips, x="total_bill", y="tip",
kind="reg", height=8)
plt.show()
# Joint with KDE
sns.jointplot(data=tips, x="total_bill", y="tip",
kind="kde", fill=True)
plt.show()Customizing Plots
# Seaborn Styles
styles = ['darkgrid', 'whitegrid', 'dark', 'white', 'ticks']
sns.set_style("whitegrid")
# Color Palettes
sns.set_palette("husl") # Husl colors
sns.set_palette("Set2") # Categorical
sns.color_palette("viridis") # Sequential
sns.color_palette("coolwarm") # Diverging
# Custom figure with both libraries
fig, ax = plt.subplots(figsize=(12, 6))
# Seaborn plot on matplotlib axis
sns.barplot(data=tips, x="day", y="total_bill",
hue="sex", ax=ax)
# Matplotlib customizations
ax.set_title('Average Bill by Day', fontsize=16, fontweight='bold')
ax.set_xlabel('Day of Week', fontsize=12)
ax.set_ylabel('Average Total Bill ($)', fontsize=12)
ax.legend(title='Gender', loc='upper right')
# Remove top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.show()
# Saving Figures
fig.savefig('my_plot.png', dpi=300, bbox_inches='tight')
fig.savefig('my_plot.svg', format='svg')
fig.savefig('my_plot.pdf', format='pdf')Time Series Visualization
# Create time series data
dates = pd.date_range('2023-01-01', periods=365, freq='D')
values = np.cumsum(np.random.randn(365)) + 100
df = pd.DataFrame({'date': dates, 'value': values})
# Line plot with dates
plt.figure(figsize=(14, 6))
plt.plot(df['date'], df['value'])
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Time Series Plot')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
# Seaborn line plot
plt.figure(figsize=(14, 6))
sns.lineplot(data=df, x='date', y='value')
plt.title('Seaborn Time Series')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
# Multiple time series
df['value2'] = np.cumsum(np.random.randn(365)) + 100
fig, ax = plt.subplots(figsize=(14, 6))
ax.plot(df['date'], df['value'], label='Series 1')
ax.plot(df['date'], df['value2'], label='Series 2')
ax.fill_between(df['date'], df['value'], alpha=0.3)
ax.legend()
ax.set_title('Multiple Time Series')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()Best Practices
- Choose the right chart: Bar for comparison, line for trends, scatter for relationships
- Keep it simple: Remove unnecessary gridlines, borders, and decorations
- Use color purposefully: Highlight key data, use colorblind-friendly palettes
- Label clearly: Always include titles, axis labels, and legends
- Consider your audience: Technical vs non-technical viewers
- Use appropriate figure size: Match the display medium
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