Subplots in Matplotlib¶

Traditionally, Matplotlib's pyplot functions are used for creating single plots. However, for visualizing complex data or comparing datasets, it’s often helpful to create multiple plots within the same figure. Subplots are a powerful feature in Matplotlib that allow you to arrange multiple plots within a single figure.

Setting Up with Subplots¶

To get started, let’s import the necessary libraries and set up our data.

In [1]:

import matplotlib.pyplot as plt
import pandas as pd

# Load sample data using pandas
data = {
    'Date': pd.date_range(start='2023-01-01', periods=10, freq='D'),
    'Temperature': [30, 32, 35, 31, 30, 29, 33, 34, 36, 32],
    'Humidity': [70, 65, 60, 75, 80, 78, 72, 68, 64, 66]
}
df = pd.DataFrame(data)

# Explanation of figure and axes objects
fig, ax = plt.subplots()  # Default 1x1 configuration

import matplotlib.pyplot as plt import pandas as pd # Load sample data using pandas data = { 'Date': pd.date_range(start='2023-01-01', periods=10, freq='D'), 'Temperature': [30, 32, 35, 31, 30, 29, 33, 34, 36, 32], 'Humidity': [70, 65, 60, 75, 80, 78, 72, 68, 64, 66] } df = pd.DataFrame(data) # Explanation of figure and axes objects fig, ax = plt.subplots() # Default 1x1 configuration

Explanation:

• Figure and Axes: fig is the entire figure object, and ax (or axes) represents the individual plot area within the figure.
• Using plt.subplots(): Calling plt.subplots() without any arguments creates a single 1x1 subplot configuration, ideal for cases where you need one plot but prefer using the object-oriented interface.

Plotting with a Single Axis¶

Instead of using plt.plot(), we can use the ax object to plot data. This method is recommended when you want better control over individual plots.

In [2]:

fig, ax = plt.subplots()  # Initialize figure and axes
ax.plot(df['Date'], df['Temperature'], label='Temperature')  # Plot data using ax object

# Set axis labels and title
ax.set_xlabel("Date")
ax.set_ylabel("Temperature (°C)")
ax.set_title("Daily Temperature")
ax.legend()  # Use ax.legend() instead of plt.legend()

plt.show()

fig, ax = plt.subplots() # Initialize figure and axes ax.plot(df['Date'], df['Temperature'], label='Temperature') # Plot data using ax object # Set axis labels and title ax.set_xlabel("Date") ax.set_ylabel("Temperature (°C)") ax.set_title("Daily Temperature") ax.legend() # Use ax.legend() instead of plt.legend() plt.show()

Explanation:

• Axes Methods: We use ax.plot() instead of plt.plot() to work directly on the specific subplot (axis). Similarly, ax.set_xlabel(), ax.set_ylabel(), ax.set_title(), and ax.legend() are used for labels, title, and legend, respectively.
• Benefits: Using the ax object makes it easy to manage multiple plots within the same figure.

Configuring Multiple Subplots¶

To create multiple subplots, specify the number of rows and columns when calling plt.subplots().

In [16]:

fig, axes = plt.subplots(2, 1)  # Create a 2x1 grid (2 rows, 1 column)

# Plot on each subplot individually
axes[0].plot(df['Date'], df['Temperature'], label='Temperature')
axes[0].set_title("Temperature Over Time")
axes[0].set_ylabel("Temperature (°C)")
axes[0].legend()
axes[0].tick_params(axis='x', rotation=45)

axes[1].plot(df['Date'], df['Humidity'], label='Humidity', color='orange')
axes[1].set_title("Humidity Over Time")
axes[1].set_ylabel("Humidity (%)")
axes[1].legend()
axes[1].tick_params(axis='x', rotation=45)

# Set common x-axis label for both subplots
fig.text(0.5, 0.04, "Date", ha='center')

plt.tight_layout()
plt.show()

fig, axes = plt.subplots(2, 1) # Create a 2x1 grid (2 rows, 1 column) # Plot on each subplot individually axes[0].plot(df['Date'], df['Temperature'], label='Temperature') axes[0].set_title("Temperature Over Time") axes[0].set_ylabel("Temperature (°C)") axes[0].legend() axes[0].tick_params(axis='x', rotation=45) axes[1].plot(df['Date'], df['Humidity'], label='Humidity', color='orange') axes[1].set_title("Humidity Over Time") axes[1].set_ylabel("Humidity (%)") axes[1].legend() axes[1].tick_params(axis='x', rotation=45) # Set common x-axis label for both subplots fig.text(0.5, 0.04, "Date", ha='center') plt.tight_layout() plt.show()

Explanation:

• Multiple Subplots: plt.subplots(2, 1) creates a 2x1 grid of subplots (two rows, one column).
• Axes Array: axes is an array (or nested list) where each element represents an individual subplot, allowing you to customize each one separately.
• Unpacking: axes[0] and axes[1] represent individual subplots. This setup is essential when dealing with multiple plots.

Sharing Axes and Labels Across Subplots¶

When working with multiple subplots, you can share the x-axis or y-axis to avoid redundant labels and create a cleaner look.

In [14]:

fig, axes = plt.subplots(2, 1, sharex=True)  # Share the x-axis between subplots

# Plot data
axes[0].plot(df['Date'], df['Temperature'], label='Temperature', color='blue')
axes[0].set_ylabel("Temperature (°C)")
axes[0].legend(loc='upper right')

axes[1].plot(df['Date'], df['Humidity'], label='Humidity', color='green')
axes[1].set_ylabel("Humidity (%)")
axes[1].legend(loc='upper right')
axes[1].tick_params(axis='x', rotation=45)

# Set x-axis label only once since it's shared
fig.suptitle("Temperature and Humidity Over Time")  # Common title for the figure

plt.tight_layout()
plt.show()

fig, axes = plt.subplots(2, 1, sharex=True) # Share the x-axis between subplots # Plot data axes[0].plot(df['Date'], df['Temperature'], label='Temperature', color='blue') axes[0].set_ylabel("Temperature (°C)") axes[0].legend(loc='upper right') axes[1].plot(df['Date'], df['Humidity'], label='Humidity', color='green') axes[1].set_ylabel("Humidity (%)") axes[1].legend(loc='upper right') axes[1].tick_params(axis='x', rotation=45) # Set x-axis label only once since it's shared fig.suptitle("Temperature and Humidity Over Time") # Common title for the figure plt.tight_layout() plt.show()

Explanation:

• Share Axes: The sharex=True parameter shares the x-axis, so dates appear only on the bottom subplot.
• Single X-Axis Label: Using fig.text() to set a single x-axis label avoids repetition.
• Common Title: fig.suptitle() adds a title for the entire figure, helpful for grouped plots.

Creating Separate Figures¶

In cases where you want to separate visualizations into different figures, you can create multiple figure objects using plt.figure().

In [19]:

fig1 = plt.figure()
ax1 = fig1.add_subplot(111)

ax1.plot(df['Date'], df['Temperature'], label='Teperature')
ax1.set_xlabel("Date")
ax1.set_ylabel("Temperature (C)")
ax1.set_title("Temperature Over Time")
ax1.legend()
ax1.tick_params(axis='x', rotation=45)

fig2 = plt.figure()
ax2 = fig2.add_subplot(111)

ax2.plot(df['Date'], df['Humidity'], label='Humidity', color='orange')
ax2.set_xlabel("Date")
ax2.set_ylabel("Humidity (%)")
ax2.set_title("Humidity Over Time")
ax2.legend()
ax2.tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

fig1 = plt.figure() ax1 = fig1.add_subplot(111) ax1.plot(df['Date'], df['Temperature'], label='Teperature') ax1.set_xlabel("Date") ax1.set_ylabel("Temperature (C)") ax1.set_title("Temperature Over Time") ax1.legend() ax1.tick_params(axis='x', rotation=45) fig2 = plt.figure() ax2 = fig2.add_subplot(111) ax2.plot(df['Date'], df['Humidity'], label='Humidity', color='orange') ax2.set_xlabel("Date") ax2.set_ylabel("Humidity (%)") ax2.set_title("Humidity Over Time") ax2.legend() ax2.tick_params(axis='x', rotation=45) plt.tight_layout() plt.show()

Explanation:

• Multiple Figures: Each plt.figure() call creates a new figure. Use this when you need separate windows or want to save figures individually.
• Independent Control: Each figure has its own ax and configuration, allowing independent customization.

Saving Multiple Figures¶

Saving figures is essential for reports or presentations. Each figure can be saved independently using the figure.savefig() method.

In [ ]:

# Saving the first figure
fig1, ax1 = plt.subplots()
ax1.plot(df['Date'], df['Temperature'], color='blue')
ax1.set_title("Temperature Over Time")
fig1.savefig("temperature_plot.png")  # Save figure

# Saving the second figure
fig2, ax2 = plt.subplots()
ax2.plot(df['Date'], df['Humidity'], color='green')
ax2.set_title("Humidity Over Time")
fig2.savefig("humidity_plot.png")  # Save figure

plt.show()

# Saving the first figure fig1, ax1 = plt.subplots() ax1.plot(df['Date'], df['Temperature'], color='blue') ax1.set_title("Temperature Over Time") fig1.savefig("temperature_plot.png") # Save figure # Saving the second figure fig2, ax2 = plt.subplots() ax2.plot(df['Date'], df['Humidity'], color='green') ax2.set_title("Humidity Over Time") fig2.savefig("humidity_plot.png") # Save figure plt.show()

Explanation:

• figure.savefig(): Saves the figure to a file. This is useful for automating the process of exporting visualizations, especially when analyzing complex data.
• File Format: You can specify formats like .png, .jpg, or .pdf.

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