Matplotlib

Installation

# Basic installation
pip install matplotlib

# With optional dependencies
pip install matplotlib[complete]

# Check version
python -c "import matplotlib; print(matplotlib.__version__)"

# Check backend
python -c "import matplotlib; print(matplotlib.get_backend())"

Basic Setup

# Essential imports
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np

# Set backend (optional)
matplotlib.use('TkAgg')  # or 'Qt5Agg', 'Agg', etc.

# Enable inline plots in Jupyter
%matplotlib inline

# Basic figure creation
fig, ax = plt.subplots()
plt.show()

Core Functionality

Basic Plotting

# Line plot
x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.figure(figsize=(8, 6))
plt.plot(x, y, label='sin(x)', linewidth=2, color='blue')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Sine Wave')
plt.legend()
plt.grid(True)
plt.show()

# Multiple lines
plt.plot(x, np.sin(x), label='sin(x)')
plt.plot(x, np.cos(x), label='cos(x)', linestyle='--')
plt.legend()

Scatter Plot

# Basic scatter
x = np.random.randn(100)
y = np.random.randn(100)
colors = np.random.rand(100)
sizes = 1000 * np.random.rand(100)

plt.scatter(x, y, c=colors, s=sizes, alpha=0.6, cmap='viridis')
plt.colorbar()
plt.title('Scatter Plot with Color and Size')

Bar Charts

# Vertical bar chart
categories = ['A', 'B', 'C', 'D']
values = [23, 17, 35, 29]

plt.bar(categories, values, color=['red', 'green', 'blue', 'orange'])
plt.title('Bar Chart')
plt.ylabel('Values')

# Horizontal bar chart
plt.barh(categories, values)

# Grouped bar chart
x = np.arange(len(categories))
width = 0.35
plt.bar(x - width/2, values, width, label='Series 1')
plt.bar(x + width/2, [20, 25, 15, 30], width, label='Series 2')
plt.xticks(x, categories)
plt.legend()

Histograms

# Basic histogram
data = np.random.normal(100, 15, 1000)
plt.hist(data, bins=30, alpha=0.7, color='skyblue', edgecolor='black')
plt.title('Histogram')
plt.xlabel('Values')
plt.ylabel('Frequency')

# Multiple histograms
data1 = np.random.normal(100, 15, 1000)
data2 = np.random.normal(80, 20, 1000)
plt.hist([data1, data2], bins=30, alpha=0.7, label=['Dataset 1', 'Dataset 2'])
plt.legend()

Subplots and Figure Management

Creating Subplots

# Basic subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10, 8))

ax1.plot([1, 2, 3], [1, 4, 2])
ax1.set_title('Plot 1')

ax2.scatter([1, 2, 3], [1, 4, 2])
ax2.set_title('Plot 2')

ax3.bar([1, 2, 3], [1, 4, 2])
ax3.set_title('Plot 3')

ax4.hist(np.random.randn(100), bins=20)
ax4.set_title('Plot 4')

plt.tight_layout()  # Adjust spacing
plt.show()

# Subplot with different sizes
fig = plt.figure(figsize=(12, 8))
ax1 = plt.subplot(2, 2, 1)
ax2 = plt.subplot(2, 2, 2)
ax3 = plt.subplot(2, 1, 2)  # Spans two columns

GridSpec for Advanced Layouts

import matplotlib.gridspec as gridspec

fig = plt.figure(figsize=(10, 8))
gs = gridspec.GridSpec(3, 3)

ax1 = fig.add_subplot(gs[0, :])    # Top row, all columns
ax2 = fig.add_subplot(gs[1, :-1])  # Middle row, first two columns
ax3 = fig.add_subplot(gs[1:, -1])  # Right column, bottom two rows
ax4 = fig.add_subplot(gs[-1, 0])   # Bottom left
ax5 = fig.add_subplot(gs[-1, -2])  # Bottom center

Subplot with Shared Axes

# Shared x-axis
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(8, 6))
ax1.plot(x, np.sin(x))
ax2.plot(x, np.cos(x))

# Shared y-axis
fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(10, 4))

Customization

Colors and Styles

# Color specifications
plt.plot(x, y, color='red')           # Named color
plt.plot(x, y, color='#FF5733')       # Hex code
plt.plot(x, y, color=(0.1, 0.2, 0.5)) # RGB tuple
plt.plot(x, y, c='r')                 # Short form

# Line styles
plt.plot(x, y, linestyle='-')         # Solid
plt.plot(x, y, linestyle='--')        # Dashed
plt.plot(x, y, linestyle='-.')        # Dash-dot
plt.plot(x, y, linestyle=':')         # Dotted
plt.plot(x, y, ls=':')                # Short form

# Markers
plt.plot(x, y, marker='o')            # Circle
plt.plot(x, y, marker='s')            # Square
plt.plot(x, y, marker='^')            # Triangle up
plt.plot(x, y, marker='D')            # Diamond

# Combined format string
plt.plot(x, y, 'ro-')  # Red circles with solid line
plt.plot(x, y, 'g--^') # Green dashed line with triangle markers

Fonts and Text

# Font properties
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.size'] = 12

# Title and labels with custom fonts
plt.title('Title', fontsize=16, fontweight='bold')
plt.xlabel('X Label', fontsize=14, style='italic')
plt.ylabel('Y Label', fontsize=14, color='red')

# Text annotations
plt.text(0.5, 0.5, 'Sample Text', fontsize=12, 
         horizontalalignment='center', verticalalignment='center',
         transform=ax.transAxes)  # Relative coordinates

# Annotations with arrows
plt.annotate('Important Point', xy=(2, 1), xytext=(3, 4),
             arrowprops=dict(arrowstyle='->', color='red', lw=2))

Axis Customization

# Axis limits
plt.xlim(0, 10)
plt.ylim(-1, 1)
# or
ax.set_xlim(0, 10)
ax.set_ylim(-1, 1)

# Axis ticks
plt.xticks([0, 2, 4, 6, 8, 10])
plt.yticks([-1, -0.5, 0, 0.5, 1])

# Custom tick labels
plt.xticks([0, 1, 2, 3], ['A', 'B', 'C', 'D'])

# Tick formatting
from matplotlib.ticker import FuncFormatter
def currency(x, pos):
    return f'${x:.0f}'
ax.yaxis.set_major_formatter(FuncFormatter(currency))

# Logarithmic scale
plt.yscale('log')
plt.xscale('log')

# Grid customization
plt.grid(True, linestyle='--', alpha=0.7, color='gray')

Legends

# Basic legend
plt.plot(x, np.sin(x), label='sin(x)')
plt.plot(x, np.cos(x), label='cos(x)')
plt.legend()

# Legend positioning
plt.legend(loc='upper right')  # 'upper left', 'lower right', etc.
plt.legend(loc='best')         # Automatic best position
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')  # Outside plot

# Legend customization
plt.legend(frameon=True, fancybox=True, shadow=True, 
           ncol=2, fontsize=10, title='Legend Title')

Different Plot Types

Statistical Plots

# Box plot
data = [np.random.normal(0, std, 100) for std in range(1, 4)]
plt.boxplot(data, labels=['Group A', 'Group B', 'Group C'])

# Violin plot (requires seaborn or custom implementation)
# Error bars
x = [1, 2, 3, 4]
y = [1, 4, 2, 3]
yerr = [0.1, 0.2, 0.1, 0.3]
plt.errorbar(x, y, yerr=yerr, fmt='o-', capsize=5)

# Fill between
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)
plt.fill_between(x, y1, y2, alpha=0.3, color='blue')

2D Plots

# Heatmap/Image
data = np.random.rand(10, 10)
plt.imshow(data, cmap='hot', interpolation='nearest')
plt.colorbar()

# Contour plot
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

plt.contour(X, Y, Z, levels=10)
plt.contourf(X, Y, Z, levels=20, cmap='viridis')  # Filled contours
plt.colorbar()

# 3D plotting
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap='viridis')

Pie Charts

# Basic pie chart
sizes = [25, 30, 15, 30]
labels = ['A', 'B', 'C', 'D']
colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
explode = (0, 0.1, 0, 0)  # Explode slice B

plt.pie(sizes, explode=explode, labels=labels, colors=colors,
        autopct='%1.1f%%', shadow=True, startangle=90)
plt.axis('equal')  # Equal aspect ratio

Saving Figures

# Save in different formats
plt.savefig('plot.png', dpi=300, bbox_inches='tight')
plt.savefig('plot.pdf', format='pdf')
plt.savefig('plot.svg', format='svg')
plt.savefig('plot.eps', format='eps')

# High-quality publication figure
plt.savefig('publication.png', dpi=300, bbox_inches='tight', 
            facecolor='white', edgecolor='none', transparent=False)

# Save with specific size
plt.figure(figsize=(10, 6))
# ... plotting code ...
plt.savefig('sized_plot.png', dpi=150)

Interactive Features

Event Handling

# Click event handling
def onclick(event):
    if event.inaxes is not None:
        print(f'Clicked at: ({event.xdata:.2f}, {event.ydata:.2f})')

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 2])
fig.canvas.mpl_connect('button_press_event', onclick)
plt.show()

# Key press events
def onkey(event):
    print(f'Key pressed: {event.key}')

fig.canvas.mpl_connect('key_press_event', onkey)

from matplotlib.widgets import Button, Slider

# Interactive slider
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.25)

# Initial plot
t = np.linspace(0, 10, 1000)
a0 = 1
f0 = 1
s = a0 * np.sin(2 * np.pi * f0 * t)
l, = plt.plot(t, s)

# Slider
ax_freq = plt.axes([0.25, 0.1, 0.5, 0.03])
slider = Slider(ax_freq, 'Frequency', 0.1, 5.0, valinit=f0)

def update(val):
    freq = slider.val
    l.set_ydata(a0 * np.sin(2 * np.pi * freq * t))
    fig.canvas.draw_idle()

slider.on_changed(update)
plt.show()

Advanced Features

Animations

from matplotlib.animation import FuncAnimation

# Animated sine wave
fig, ax = plt.subplots()
x = np.linspace(0, 2*np.pi, 100)
line, = ax.plot(x, np.sin(x))

def animate(frame):
    line.set_ydata(np.sin(x + frame/10))
    return line,

ani = FuncAnimation(fig, animate, frames=200, interval=50, blit=True)
plt.show()

# Save animation
ani.save('animation.gif', writer='pillow', fps=20)
ani.save('animation.mp4', writer='ffmpeg', fps=30)

Custom Colormaps

from matplotlib.colors import LinearSegmentedColormap

# Create custom colormap
colors = ['red', 'yellow', 'green', 'blue']
n_bins = 100
cmap = LinearSegmentedColormap.from_list('custom', colors, N=n_bins)

# Use custom colormap
data = np.random.rand(10, 10)
plt.imshow(data, cmap=cmap)
plt.colorbar()

Dual Axes

# Two different y-axes
fig, ax1 = plt.subplots()

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.exp(x/5)

# First y-axis
ax1.plot(x, y1, 'g-')
ax1.set_xlabel('X data')
ax1.set_ylabel('sin(x)', color='g')
ax1.tick_params(axis='y', labelcolor='g')

# Second y-axis
ax2 = ax1.twinx()
ax2.plot(x, y2, 'b-')
ax2.set_ylabel('exp(x/5)', color='b')
ax2.tick_params(axis='y', labelcolor='b')

plt.show()

Customization & Best Practices

Style Sheets

# Available styles
print(plt.style.available)

# Use built-in styles
plt.style.use('seaborn-v0_8')
plt.style.use('ggplot')
plt.style.use('dark_background')

# Use multiple styles
plt.style.use(['seaborn-v0_8', 'seaborn-v0_8-darkgrid'])

# Context manager for temporary style
with plt.style.context('bmh'):
    plt.plot([1, 2, 3], [1, 4, 2])
    plt.show()

RC Parameters

# Global settings
plt.rcParams['figure.figsize'] = [10, 6]
plt.rcParams['font.size'] = 12
plt.rcParams['lines.linewidth'] = 2
plt.rcParams['grid.alpha'] = 0.3

# Context manager for temporary settings
with plt.rc_context({'font.size': 14, 'lines.linewidth': 3}):
    plt.plot([1, 2, 3], [1, 4, 2])

# Reset to defaults
plt.rcdefaults()

Performance Tips

# Use appropriate backends
import matplotlib
matplotlib.use('Agg')  # For server/batch processing

# Batch plotting
plt.ioff()  # Turn off interactive mode
for i in range(100):
    plt.figure()
    plt.plot(data[i])
    plt.savefig(f'plot_{i}.png')
    plt.close()  # Important: close figures to free memory

# Efficient line collection for many lines
from matplotlib.collections import LineCollection
lines = [np.column_stack([x, y + i]) for i in range(100)]
lc = LineCollection(lines, linewidths=0.5)
ax.add_collection(lc)

Integration with Other Libraries

NumPy Integration

# Matplotlib works seamlessly with NumPy arrays
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)

# Masked arrays
y_masked = np.ma.masked_where(y < 0, y)
plt.plot(x, y_masked)

Pandas Integration

import pandas as pd

# DataFrame plotting
df = pd.DataFrame({
    'x': range(10),
    'y1': np.random.randn(10),
    'y2': np.random.randn(10)
})

# Direct pandas plotting
df.plot(x='x', y=['y1', 'y2'])

# Using matplotlib directly
plt.plot(df['x'], df['y1'], label='y1')
plt.plot(df['x'], df['y2'], label='y2')
plt.legend()

Common Gotchas & Best Practices

Memory Management

# Always close figures when done
fig, ax = plt.subplots()
# ... plotting code ...
plt.close(fig)  # or plt.close('all')

# Clear current figure
plt.clf()

# Clear current axes
plt.cla()

Backend Issues

# Check current backend
print(plt.get_backend())

# Set backend before importing pyplot
import matplotlib
matplotlib.use('TkAgg')  # Must be before importing pyplot
import matplotlib.pyplot as plt

Common Patterns

# Professional plotting setup
def setup_plot(figsize=(10, 6)):
    fig, ax = plt.subplots(figsize=figsize)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.grid(True, alpha=0.3)
    return fig, ax

# Context manager for consistent styling
from contextlib import contextmanager

@contextmanager
def plot_style(style='seaborn-v0_8', figsize=(10, 6)):
    with plt.style.context(style):
        fig, ax = plt.subplots(figsize=figsize)
        yield fig, ax
        plt.tight_layout()

# Usage
with plot_style() as (fig, ax):
    ax.plot([1, 2, 3], [1, 4, 2])
    ax.set_title('Styled Plot')
    plt.show()

Quick Reference

Essential Functions

Function	Purpose	Example
`plt.figure()`	Create new figure	`plt.figure(figsize=(8,6))`
`plt.subplot()`	Add subplot	`plt.subplot(2,2,1)`
`plt.plot()`	Line plot	`plt.plot(x, y, 'r-')`
`plt.scatter()`	Scatter plot	`plt.scatter(x, y, c=colors)`
`plt.bar()`	Bar chart	`plt.bar(x, height)`
`plt.hist()`	Histogram	`plt.hist(data, bins=20)`
`plt.imshow()`	Display image/2D data	`plt.imshow(array, cmap='hot')`
`plt.savefig()`	Save figure	`plt.savefig('plot.png', dpi=300)`

Format Strings

Code	Meaning	Code	Meaning
`-`	Solid line	`o`	Circle marker
`--`	Dashed line	`s`	Square marker
`-.`	Dash-dot line	`^`	Triangle up marker
`:`	Dotted line	`D`	Diamond marker
`r`	Red	`g`	Green
`b`	Blue	`k`	Black
`c`	Cyan	`m`	Magenta
`y`	Yellow	`w`	White

Color Maps

Common colormaps: viridis, plasma, inferno, magma, coolwarm, RdYlBu, seismic, hot, cool, spring, summer, autumn, winter, gray

Use: plt.imshow(data, cmap='viridis')

Matplotlib

Installation

Basic Setup

Core Functionality

Basic Plotting

Scatter Plot

Bar Charts

Histograms

Subplots and Figure Management

Creating Subplots

GridSpec for Advanced Layouts

Subplot with Shared Axes

Customization

Colors and Styles

Fonts and Text

Axis Customization

Legends

Different Plot Types

Statistical Plots

2D Plots

Pie Charts

Saving Figures

Interactive Features

Event Handling

Widgets (requires widget backend)

Advanced Features

Animations

Custom Colormaps

Dual Axes

Customization & Best Practices

Style Sheets

RC Parameters

Performance Tips

Integration with Other Libraries

NumPy Integration

Pandas Integration

Common Gotchas & Best Practices

Memory Management

Backend Issues

Common Patterns

Quick Reference

Essential Functions

Format Strings

Color Maps