Matplotlib basics to advance topics with examples
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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()
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NAGARAJU ALOORI
NAGARAJU ALOORI
"As an Agricultural Engineering graduate with a strong foundation in data analysis, I am passionate about pursuing a career in data science. I am eager to work in a dynamic environment that supports both professional and personal growth. My goal is to leverage my analytical skills to derive actionable insights that align with company objectives. I am committed to driving innovation and efficiency while prioritizing organizational goals and contributing to overall success."