Matplotlib is a versatile plotting library for creating static, interactive, and animated visualizations in Python. Let's go through the basics to advanced topics with explanations and examples.

Basics

1. Basic Plotting

Line Plot:

  import matplotlib.pyplot as plt

  x = [1, 2, 3, 4, 5]
  y = [10, 20, 25, 30, 40]

  plt.plot(x, y)
  plt.xlabel('X-axis Label')
  plt.ylabel('Y-axis Label')
  plt.title('Basic Line Plot')
  plt.show()

Scatter Plot:

  x = [1, 2, 3, 4, 5]
  y = [10, 20, 25, 30, 40]

  plt.scatter(x, y, color='red')
  plt.title('Basic Scatter Plot')
  plt.show()

Bar Plot:

  categories = ['A', 'B', 'C', 'D']
  values = [10, 24, 36, 40]

  plt.bar(categories, values)
  plt.title('Basic Bar Plot')
  plt.show()

2. Styling and Customization

Changing Line Styles and Colors:

  plt.plot(x, y, linestyle='--', color='green', marker='o')
  plt.title('Line Plot with Custom Style')
  plt.show()

Adding Grid and Legends:

  plt.plot(x, y, label='Line 1')
  plt.plot(x, [i * 2 for i in y], label='Line 2')
  plt.legend()
  plt.grid(True)
  plt.show()

Adjusting Figure Size:

  plt.figure(figsize=(8, 4))
  plt.plot(x, y)
  plt.title('Figure with Custom Size')
  plt.show()

Intermediate

3. Subplots

Creating Multiple Plots:

  fig, axs = plt.subplots(2, 2)  # 2x2 grid

  axs[0, 0].plot(x, y)
  axs[0, 0].set_title('Plot 1')

  axs[0, 1].scatter(x, y)
  axs[0, 1].set_title('Plot 2')

  axs[1, 0].bar(categories, values)
  axs[1, 0].set_title('Plot 3')

  axs[1, 1].hist(values)
  axs[1, 1].set_title('Plot 4')

  plt.tight_layout()
  plt.show()

4. Histograms

Creating a Histogram:

  data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
  plt.hist(data, bins=4, color='blue', edgecolor='black')
  plt.title('Histogram')
  plt.show()

5. Pie Charts

Basic Pie Chart:

  sizes = [20, 30, 25, 25]
  labels = ['A', 'B', 'C', 'D']

  plt.pie(sizes, labels=labels, autopct='%1.1f%%')
  plt.title('Basic Pie Chart')
  plt.show()

Customizing Pie Chart:

  plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, explode=(0.1, 0, 0, 0))
  plt.title('Customized Pie Chart')
  plt.show()

Advanced

6. Advanced Plot Customization

Adding Annotations:

  plt.plot(x, y)
  plt.annotate('Highest Point', xy=(5, 40), xytext=(4, 30),
               arrowprops=dict(facecolor='black', arrowstyle='->'))
  plt.title('Line Plot with Annotation')
  plt.show()

Custom Ticks and Labels:

  plt.plot(x, y)
  plt.xticks([1, 2, 3, 4, 5], ['One', 'Two', 'Three', 'Four', 'Five'])
  plt.yticks([10, 20, 30, 40])
  plt.title('Plot with Custom Ticks')
  plt.show()

7. 3D Plotting

3D Line and Scatter Plots:

  from mpl_toolkits.mplot3d import Axes3D

  fig = plt.figure()
  ax = fig.add_subplot(111, projection='3d')

  z = [1, 2, 3, 4, 5]
  ax.plot(x, y, z)
  ax.set_xlabel('X Label')
  ax.set_ylabel('Y Label')
  ax.set_zlabel('Z Label')
  plt.title('3D Line Plot')
  plt.show()

  # 3D Scatter Plot
  ax.scatter(x, y, z)
  plt.title('3D Scatter Plot')
  plt.show()

8. Seaborn Integration

Using Seaborn for Enhanced Visuals:

  import seaborn as sns

  # Seaborn pairplot
  iris = sns.load_dataset('iris')
  sns.pairplot(iris, hue='species')
  plt.title('Seaborn Pairplot')
  plt.show()

  # Seaborn heatmap
  data = iris.corr()
  sns.heatmap(data, annot=True, cmap='coolwarm')
  plt.title('Seaborn Heatmap')
  plt.show()

9. Advanced Plots with Multiple Axes

Twin Axes:

  fig, ax1 = plt.subplots()

  ax2 = ax1.twinx()  # Create another axes sharing the same x-axis
  ax1.plot(x, y, 'g-')
  ax2.plot(x, [i ** 2 for i in y], 'b-')

  ax1.set_xlabel('X Data')
  ax1.set_ylabel('Y1', color='g')
  ax2.set_ylabel('Y2', color='b')
  plt.title('Twin Axes Plot')
  plt.show()

10. Animation

Creating Animations with FuncAnimation:

  from matplotlib.animation import FuncAnimation

  fig, ax = plt.subplots()
  line, = ax.plot([], [], 'r-')

  def init():
      ax.set_xlim(0, 2 * np.pi)
      ax.set_ylim(-1, 1)
      return line,

  def update(frame):
      x = np.linspace(0, 2 * np.pi, 100)
      y = np.sin(x + frame)
      line.set_data(x, y)
      return line,

  ani = FuncAnimation(fig, update, frames=np.linspace(0, 2 * np.pi, 128),
                      init_func=init, blit=True)
  plt.title('Sine Wave Animation')
  plt.show()

11. Interactive Plots with Widgets

Using ipywidgets for Interaction:

  from ipywidgets import interact
  import numpy as np

  def plot_function(frequency=1.0):
      x = np.linspace(0, 2 * np.pi, 100)
      y = np.sin(frequency * x)
      plt.plot(x, y)
      plt.title(f'Sine Wave with Frequency {frequency}')
      plt.show()

  interact(plot_function, frequency=(0.1, 2.0, 0.1))

Examples Combining Techniques

Example 1: Customizing Subplots

fig, axs = plt.subplots(2, 2, figsize=(10, 6))

# Line plot
axs[0, 0].plot(x, y, linestyle='--', color='purple')
axs[0, 0].set_title('Custom Line Plot')

# Scatter plot with grid and annotations
axs[0, 1].scatter(x, y, color='red')
axs[0, 1].grid(True)
axs[0, 1].annotate('Point 1', xy=(1, 10), xytext=(2, 20),
                   arrowprops=dict(facecolor='black', arrowstyle='->'))
axs[0, 1].set_title('Scatter Plot with Annotations')

# Histogram with custom bins and colors
data = np.random.randn(100)
axs[1, 0].hist(data, bins=20, color='blue', edgecolor='black')
axs[1, 0].set_title('Histogram with Custom Bins')

# Pie chart
sizes = [30, 40, 20, 10]
labels = ['A', 'B', 'C', 'D']
axs[1, 1].pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
axs[1, 1].set_title('Pie Chart')

plt.tight_layout()
plt.show()

Advanced Topics (Continued)

12. Customizing Color Maps

Using Built-in Color Maps:

  import numpy as np

  # Generate some data
  data = np.random.rand(10, 10)

  # Display data using a color map
  plt.imshow(data, cmap='viridis')
  plt.colorbar()  # Show a color scale
  plt.title('Heatmap with Viridis Color Map')
  plt.show()

Creating Custom Color Maps:

  from matplotlib.colors import LinearSegmentedColormap

  # Define colors for the custom colormap
  colors = ['blue', 'white', 'red']
  cmap = LinearSegmentedColormap.from_list('custom_cmap', colors)

  # Display data using the custom color map
  plt.imshow(data, cmap=cmap)
  plt.colorbar()
  plt.title('Heatmap with Custom Color Map')
  plt.show()

13. Contour Plots

Creating a Contour Plot:

  x = np.linspace(-5, 5, 100)
  y = np.linspace(-5, 5, 100)
  X, Y = np.meshgrid(x, y)
  Z = np.sin(np.sqrt(X**2 + Y**2))

  plt.contour(X, Y, Z, levels=20, cmap='coolwarm')
  plt.colorbar()
  plt.title('Contour Plot')
  plt.show()

Filled Contour Plot:

  plt.contourf(X, Y, Z, levels=20, cmap='viridis')
  plt.colorbar()
  plt.title('Filled Contour Plot')
  plt.show()

14. Plotting with Error Bars

Adding Error Bars to a Plot:

  x = np.arange(0, 10, 1)
  y = np.sin(x)
  y_err = 0.2

  plt.errorbar(x, y, yerr=y_err, fmt='-o', ecolor='red', capsize=5)
  plt.title('Plot with Error Bars')
  plt.show()

15. Logarithmic and Polar Plots

Logarithmic Scale:

  x = np.linspace(0.1, 10, 100)
  y = np.exp(x)

  plt.plot(x, y)
  plt.xscale('log')
  plt.yscale('log')
  plt.title('Logarithmic Scale Plot')
  plt.show()

Polar Plot:

  theta = np.linspace(0, 2 * np.pi, 100)
  r = np.abs(np.sin(2 * theta) * np.cos(2 * theta))

  plt.polar(theta, r)
  plt.title('Polar Plot')
  plt.show()

16. Statistical Plots

Box Plot:

  data = np.random.rand(100, 5)

  plt.boxplot(data)
  plt.title('Box Plot')
  plt.show()

Violin Plot:

  plt.violinplot(data, showmeans=True)
  plt.title('Violin Plot')
  plt.show()

17. Saving Figures

Saving to File:

  plt.plot(x, y)
  plt.title('Plot to Save')
  plt.savefig('plot.png')  # Save as PNG
  plt.savefig('plot.pdf')  # Save as PDF
  plt.show()

Controlling Output Quality:

  plt.plot(x, y)
  plt.title('High-Resolution Plot')
  plt.savefig('high_res_plot.png', dpi=300)  # Save with higher resolution
  plt.show()

Examples Combining Advanced Techniques

Example 2: Comprehensive Visualization

fig, ax = plt.subplots(figsize=(10, 6))

# Plot data with error bars
x = np.linspace(0, 10, 50)
y = np.sin(x)
y_err = 0.2

ax.errorbar(x, y, yerr=y_err, fmt='o', ecolor='gray', label='Sine with error bars')

# Adding annotations and customizing ticks
for i in range(0, 50, 5):
    ax.annotate(f'({x[i]:.1f}, {y[i]:.1f})', (x[i], y[i]), textcoords="offset points", xytext=(0, 10), ha='center')

# Customize the plot with grid, title, labels, and legend
ax.set_title('Comprehensive Visualization Example', fontsize=16)
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.grid(True)
ax.legend()

# Save the plot
plt.savefig('comprehensive_plot.png', dpi=300)
plt.show()

Example 3: Heatmap with Annotations

# Generate a heatmap with annotated values
data = np.random.rand(10, 10)

fig, ax = plt.subplots()
cax = ax.imshow(data, cmap='viridis')

# Annotate each cell in the heatmap
for i in range(data.shape[0]):
    for j in range(data.shape[1]):
        ax.text(j, i, f'{data[i, j]:.2f}', ha='center', va='center', color='white')

# Add colorbar and titles
fig.colorbar(cax)
ax.set_title('Heatmap with Annotations')
plt.show()

18. Interactive Plotting with `mpl_toolkits`

Zoomable and Panable Plots using mpl_toolkits and Widgets:

  from mpl_toolkits.mplot3d import Axes3D
  import numpy as np
  import matplotlib.pyplot as plt

  fig = plt.figure()
  ax = fig.add_subplot(111, projection='3d')

  # Generate data for 3D scatter plot
  x = np.random.rand(100)
  y = np.random.rand(100)
  z = np.random.rand(100)

  scatter = ax.scatter(x, y, z, c='b', marker='o')
  ax.set_xlabel('X Label')
  ax.set_ylabel('Y Label')
  ax.set_zlabel('Z Label')
  ax.set_title('Interactive 3D Scatter Plot')

  plt.show()

Interactive Widgets with %matplotlib notebook in Jupyter Notebooks:

  %matplotlib notebook
  import matplotlib.pyplot as plt
  import numpy as np

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

  # Interactive plot update
  def update_plot(phase, amplitude):
      line.set_ydata(np.sin(x + phase) * amplitude)
      fig.canvas.draw()

  from ipywidgets import interact

  interact(update_plot, phase=(0, 10, 0.1), amplitude=(0, 2, 0.1))

19. Advanced 3D Surface Plotting

Surface Plot with Contour Projections:

  from mpl_toolkits.mplot3d import Axes3D

  fig = plt.figure()
  ax = fig.add_subplot(111, projection='3d')

  x = np.linspace(-5, 5, 50)
  y = np.linspace(-5, 5, 50)
  X, Y = np.meshgrid(x, y)
  Z = np.sin(np.sqrt(X**2 + Y**2))

  # Surface plot
  surf = ax.plot_surface(X, Y, Z, cmap='viridis', edgecolor='none')
  fig.colorbar(surf)

  # Add contour projections
  ax.contour3D(X, Y, Z, 50, cmap='binary')

  ax.set_title('3D Surface Plot with Contours')
  plt.show()

20. Animation with `FuncAnimation`

Advanced Animation with Custom Functions:

  import numpy as np
  from matplotlib.animation import FuncAnimation

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

  def init():
      ax.set_xlim(0, 2*np.pi)
      ax.set_ylim(-1.5, 1.5)
      return line,

  def update(frame):
      line.set_ydata(np.sin(x + frame))
      return line,

  ani = FuncAnimation(fig, update, frames=np.linspace(0, 2*np.pi, 128),
                      init_func=init, blit=True)

  plt.show()

21. Custom Legends and Annotations

Custom Legend Positioning:

  x = np.linspace(0, 10, 100)
  y1 = np.sin(x)
  y2 = np.cos(x)

  plt.plot(x, y1, label='Sine Wave')
  plt.plot(x, y2, label='Cosine Wave')

  # Customizing legend
  plt.legend(loc='upper right', fontsize='small', shadow=True, title='Waves', title_fontsize='medium')
  plt.title('Custom Legend Example')
  plt.show()

Advanced Annotations with Box Styling:

  plt.plot(x, y1)
  plt.title('Advanced Annotation Example')

  # Adding an annotation with a custom style
  plt.annotate('Local Max', xy=(np.pi/2, 1), xytext=(np.pi/2 + 1, 1.5),
               arrowprops=dict(facecolor='black', shrink=0.05),
               bbox=dict(boxstyle='round,pad=0.3', edgecolor='blue', facecolor='lightyellow'))

  plt.show()

22. Advanced Data Visualization Techniques

Hexbin Plot for Density Estimation:

  x = np.random.randn(1000)
  y = np.random.randn(1000)

  plt.hexbin(x, y, gridsize=30, cmap='Blues')
  plt.colorbar(label='Counts')
  plt.title('Hexbin Plot Example')
  plt.show()

Pair Plot with seaborn:

  import seaborn as sns
  iris = sns.load_dataset('iris')

  # Pair plot with kde (kernel density estimate)
  sns.pairplot(iris, kind='scatter', hue='species', diag_kind='kde')
  plt.suptitle('Pair Plot with KDE', y=1.02)
  plt.show()

Examples to Combine Techniques for Advanced Analysis

Example 4: Data Exploration with Custom Annotations and Complex Layouts

fig, axs = plt.subplots(2, 2, figsize=(12, 8))

# Line plot with custom annotation
axs[0, 0].plot(x, np.sin(x), label='Sine Wave')
axs[0, 0].annotate('Peak', xy=(np.pi/2, 1), xytext=(np.pi/2, 1.5),
                   arrowprops=dict(arrowstyle='->', lw=1.5, color='blue'),
                   bbox=dict(boxstyle='round,pad=0.3', color='yellow'))
axs[0, 0].set_title('Annotated Sine Wave')
axs[0, 0].legend()

# Filled contour plot with color bar
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)
contour = axs[0, 1].contourf(X, Y, Z, cmap='viridis', levels=20)
fig.colorbar(contour, ax=axs[0, 1])
axs[0, 1].set_title('Filled Contour Plot')

# Heatmap with annotations
data = np.random.rand(10, 10)
heatmap = axs[1, 0].imshow(data, cmap='hot')
for i in range(data.shape[0]):
    for j in range(data.shape[1]):
        axs[1, 0].text(j, i, f'{data[i, j]:.2f}', ha='center', va='center', color='black')
fig.colorbar(heatmap, ax=axs[1, 0])
axs[1, 0].set_title('Heatmap with Annotations')

# Polar plot with grid and ticks
theta = np.linspace(0, 2*np.pi, 100)
r = np.abs(np.sin(theta) * np.cos(theta))
axs[1, 1].polar(theta, r, color='magenta')
axs[1, 1].set_title('Polar Plot with Grid')

plt.tight_layout()
plt.show()

Example 5: Combining Seaborn and Matplotlib for Enhanced Plots

import seaborn as sns

# Load dataset
tips = sns.load_dataset('tips')

# Create a Seaborn style plot using Matplotlib
fig, ax = plt.subplots(figsize=(10, 6))
sns.histplot(tips['total_bill'], bins=20, kde=True, color='green', ax=ax)

# Customize with Matplotlib
ax.set_title('Total Bill Distribution')
ax.set_xlabel('Total Bill')
ax.set_ylabel('Frequency')

# Adding annotation to show a specific feature
mean_total_bill = tips['total_bill'].mean()
ax.axvline(mean_total_bill, color='red', linestyle='--')
ax.text(mean_total_bill + 1, 20, f'Mean: {mean_total_bill:.2f}', color='red')

plt.show()

Matplotlib basics to advance topics with examples

Basics

1. Basic Plotting

2. Styling and Customization

Intermediate

3. Subplots

4. Histograms

5. Pie Charts

Advanced

6. Advanced Plot Customization

7. 3D Plotting

8. Seaborn Integration

9. Advanced Plots with Multiple Axes

10. Animation

11. Interactive Plots with Widgets

Examples Combining Techniques

Example 1: Customizing Subplots

Advanced Topics (Continued)

12. Customizing Color Maps

13. Contour Plots

14. Plotting with Error Bars

15. Logarithmic and Polar Plots

16. Statistical Plots

17. Saving Figures

Examples Combining Advanced Techniques

Example 2: Comprehensive Visualization

Example 3: Heatmap with Annotations

18. Interactive Plotting with `mpl_toolkits`

19. Advanced 3D Surface Plotting

20. Animation with `FuncAnimation`

21. Custom Legends and Annotations

22. Advanced Data Visualization Techniques

Examples to Combine Techniques for Advanced Analysis

Example 4: Data Exploration with Custom Annotations and Complex Layouts

Example 5: Combining Seaborn and Matplotlib for Enhanced Plots

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NAGARAJU ALOORI

NAGARAJU ALOORI

Matplotlib basics to advance topics with examples

Basics

1. Basic Plotting

2. Styling and Customization

Intermediate

3. Subplots

4. Histograms

5. Pie Charts

Advanced

6. Advanced Plot Customization

7. 3D Plotting

8. Seaborn Integration

9. Advanced Plots with Multiple Axes

10. Animation

11. Interactive Plots with Widgets

Examples Combining Techniques

Example 1: Customizing Subplots

Advanced Topics (Continued)

12. Customizing Color Maps

13. Contour Plots

14. Plotting with Error Bars

15. Logarithmic and Polar Plots

16. Statistical Plots

17. Saving Figures

Examples Combining Advanced Techniques

Example 2: Comprehensive Visualization

Example 3: Heatmap with Annotations

18. Interactive Plotting with mpl_toolkits

19. Advanced 3D Surface Plotting

20. Animation with FuncAnimation

21. Custom Legends and Annotations

22. Advanced Data Visualization Techniques

Examples to Combine Techniques for Advanced Analysis

Example 4: Data Exploration with Custom Annotations and Complex Layouts

Example 5: Combining Seaborn and Matplotlib for Enhanced Plots

Subscribe to my newsletter

NAGARAJU ALOORI

NAGARAJU ALOORI

18. Interactive Plotting with `mpl_toolkits`

20. Animation with `FuncAnimation`