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Lesson 14 - Creating Subplots

Multi-Panel Figures

You have created single plots—one chart per figure. Now you will learn to create subplots—multiple charts in a single figure arranged in a grid.

By the end of this lesson, you will be able to:

  • Use plt.subplots() to create figure and axes objects
  • Arrange plots in grids (rows × columns)
  • Access individual subplot axes
  • Share x or y axes across subplots
  • Adjust spacing between subplots
  • Create professional multi-panel figures
  • Build comparison dashboards

Subplots are essential for comparing data side-by-side and creating comprehensive visualizations.

Understanding Figure and Axes

The Object-Oriented Approach

Before (simple plotting):

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [1, 4, 9])
plt.show()

Now (object-oriented):

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])
plt.show()

What changed:

  • fig → the entire figure (canvas)
  • ax → the axes (the plotting area)
  • Plot on ax instead of using plt

Concept diagram:

┌─────────────────────────── Figure ────────────────────────────┐
│                                                                 │
│  Title of the Figure                                           │
│                                                                 │
│  ┌─────────────────────── Axes ───────────────────────────┐   │
│  │                                                          │   │
│  │  Y-axis                                                  │   │
│  │    ^                                                     │   │
│  │    │  Plot area (data visualization)                    │   │
│  │    │                                                     │   │
│  │    └──────────────> X-axis                              │   │
│  │                                                          │   │
│  └──────────────────────────────────────────────────────────┘   │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Creating Simple Subplots

1 Row, 2 Columns

import pandas as pd
import matplotlib.pyplot as plt

bikes = pd.read_csv('day.csv')
bikes['dteday'] = pd.to_datetime(bikes['dteday'])

# Create 1 row, 2 columns of subplots
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Left plot: Total rentals
axes[0].plot(bikes['dteday'], bikes['cnt'], color='steelblue', linewidth=1)
axes[0].set_xlabel('Date')
axes[0].set_ylabel('Total Rentals')
axes[0].set_title('Total Daily Rentals')
axes[0].grid(True, alpha=0.3)

# Right plot: Casual vs Registered
axes[1].plot(bikes['dteday'], bikes['casual'], label='Casual', linewidth=1)
axes[1].plot(bikes['dteday'], bikes['registered'], label='Registered', linewidth=1)
axes[1].set_xlabel('Date')
axes[1].set_ylabel('Rentals')
axes[1].set_title('Casual vs Registered Users')
axes[1].legend()
axes[1].grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

Key points:

  • plt.subplots(1, 2) → 1 row, 2 columns
  • axes[0] → left plot
  • axes[1] → right plot
  • plt.tight_layout() → adjust spacing automatically

2 Rows, 1 Column

import pandas as pd
import matplotlib.pyplot as plt

bikes = pd.read_csv('day.csv')
bikes['dteday'] = pd.to_datetime(bikes['dteday'])

# Create 2 rows, 1 column
fig, axes = plt.subplots(2, 1, figsize=(12, 8))

# Top plot: Rentals
axes[0].plot(bikes['dteday'], bikes['cnt'], color='darkgreen', linewidth=1)
axes[0].set_ylabel('Total Rentals')
axes[0].set_title('Daily Bike Rentals')
axes[0].grid(True, alpha=0.3)

# Bottom plot: Temperature
axes[1].plot(bikes['dteday'], bikes['temp'], color='red', linewidth=1)
axes[1].set_xlabel('Date')
axes[1].set_ylabel('Normalized Temperature')
axes[1].set_title('Daily Temperature')
axes[1].grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

Observation: Vertical stacking good for comparing time series with same x-axis.

2x2 Grid Layout

Four-Panel Dashboard

import pandas as pd
import matplotlib.pyplot as plt

bikes = pd.read_csv('day.csv')
bikes['dteday'] = pd.to_datetime(bikes['dteday'])

# Create 2x2 grid
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Top-left: Rentals over time
axes[0, 0].plot(bikes['dteday'], bikes['cnt'], color='steelblue', linewidth=1)
axes[0, 0].set_ylabel('Total Rentals')
axes[0, 0].set_title('Daily Rentals Over Time')
axes[0, 0].grid(True, alpha=0.3)

# Top-right: Rental distribution
axes[0, 1].hist(bikes['cnt'], bins=30, color='coral', edgecolor='black', alpha=0.7)
axes[0, 1].set_xlabel('Daily Rentals')
axes[0, 1].set_ylabel('Frequency')
axes[0, 1].set_title('Rental Distribution')
axes[0, 1].grid(True, alpha=0.3, axis='y')

# Bottom-left: Temperature vs Rentals
axes[1, 0].scatter(bikes['temp'], bikes['cnt'], alpha=0.5, s=30, color='green')
axes[1, 0].set_xlabel('Normalized Temperature')
axes[1, 0].set_ylabel('Total Rentals')
axes[1, 0].set_title('Temperature vs Rentals')
axes[1, 0].grid(True, alpha=0.3)

# Bottom-right: Seasonal averages
season_avg = bikes.groupby('season')['cnt'].mean()
season_names = ['Spring', 'Summer', 'Fall', 'Winter']
axes[1, 1].bar(season_names, season_avg.values, color='orange', edgecolor='black')
axes[1, 1].set_ylabel('Average Daily Rentals')
axes[1, 1].set_title('Average Rentals by Season')
axes[1, 1].grid(True, alpha=0.3, axis='y')

plt.tight_layout()
plt.show()

Accessing 2D grid:

  • axes[row, col]
  • axes[0, 0] → top-left
  • axes[0, 1] → top-right
  • axes[1, 0] → bottom-left
  • axes[1, 1] → bottom-right

Sharing Axes

Shared X-Axis

import pandas as pd
import matplotlib.pyplot as plt

bikes_hour = pd.read_csv('hour.csv')

# Calculate hourly averages
hourly_casual = bikes_hour.groupby('hr')['casual'].mean()
hourly_registered = bikes_hour.groupby('hr')['registered'].mean()

# Create subplots with shared x-axis
fig, axes = plt.subplots(2, 1, figsize=(12, 8), sharex=True)

# Top: Casual users
axes[0].plot(hourly_casual.index, hourly_casual.values,
             color='skyblue', linewidth=2, marker='o', markersize=4)
axes[0].set_ylabel('Average Casual Rentals')
axes[0].set_title('Hourly Patterns: Casual Users')
axes[0].grid(True, alpha=0.3)

# Bottom: Registered users
axes[1].plot(hourly_registered.index, hourly_registered.values,
             color='coral', linewidth=2, marker='o', markersize=4)
axes[1].set_xlabel('Hour of Day')
axes[1].set_ylabel('Average Registered Rentals')
axes[1].set_title('Hourly Patterns: Registered Users')
axes[1].grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

Benefits of sharex=True:

  • Both plots use same x-axis range
  • X-axis labels only on bottom plot
  • Aligned for easy comparison
  • Zooming one plot zooms both

Shared Y-Axis

import pandas as pd
import matplotlib.pyplot as plt

bikes = pd.read_csv('day.csv')

# Weekend vs Workingday
weekend_rentals = bikes[bikes['workingday'] == 0]['cnt']
workday_rentals = bikes[bikes['workingday'] == 1]['cnt']

# Create subplots with shared y-axis
fig, axes = plt.subplots(1, 2, figsize=(14, 5), sharey=True)

# Left: Weekend distribution
axes[0].hist(weekend_rentals, bins=30, color='lightblue', edgecolor='black', alpha=0.7)
axes[0].set_xlabel('Daily Rentals')
axes[0].set_ylabel('Frequency')
axes[0].set_title('Rental Distribution: Weekends')
axes[0].grid(True, alpha=0.3, axis='y')

# Right: Workday distribution
axes[1].hist(workday_rentals, bins=30, color='lightgreen', edgecolor='black', alpha=0.7)
axes[1].set_xlabel('Daily Rentals')
axes[1].set_title('Rental Distribution: Workdays')
axes[1].grid(True, alpha=0.3, axis='y')

plt.tight_layout()
plt.show()

Benefits of sharey=True:

  • Same y-axis scale for both plots
  • Y-axis labels only on left plot
  • Direct comparison of distributions

Flattening Axes for Loops

Using axes.flat

import pandas as pd
import matplotlib.pyplot as plt

bikes = pd.read_csv('day.csv')

# Variables to plot
variables = ['temp', 'atemp', 'hum', 'windspeed']
titles = ['Temperature', 'Feels-Like Temp', 'Humidity', 'Wind Speed']
colors = ['red', 'orange', 'blue', 'green']

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Flatten 2D array to 1D for easy iteration
for ax, var, title, color in zip(axes.flat, variables, titles, colors):
    ax.scatter(bikes[var], bikes['cnt'], alpha=0.5, s=30, color=color)
    ax.set_xlabel(title)
    ax.set_ylabel('Daily Rentals')
    ax.set_title(f'{title} vs Rentals')
    ax.grid(True, alpha=0.3)

    # Add correlation
    corr = bikes[var].corr(bikes['cnt'])
    ax.text(0.05, 0.95, f'r = {corr:.3f}',
            transform=ax.transAxes, fontsize=11, verticalalignment='top',
            bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))

plt.tight_layout()
plt.show()

axes.flat advantage: Iterate over 2D grid as if it were 1D list.

Figure-Level Titles

Super Title

import pandas as pd
import matplotlib.pyplot as plt

bikes = pd.read_csv('day.csv')

fig, axes = plt.subplots(1, 3, figsize=(16, 5))

# Plot by season
for season, ax in zip([1, 2, 3], axes):
    season_data = bikes[bikes['season'] == season]
    ax.scatter(season_data['temp'], season_data['cnt'], alpha=0.6, s=30)
    ax.set_xlabel('Temperature')
    ax.set_ylabel('Daily Rentals')
    season_name = ['Spring', 'Summer', 'Fall'][season - 1]
    ax.set_title(season_name)
    ax.grid(True, alpha=0.3)

# Add figure-level title
fig.suptitle('Temperature vs Rentals by Season', fontsize=16, fontweight='bold')
plt.tight_layout()
plt.show()

fig.suptitle() adds a title above all subplots.

Custom Spacing

Adjusting Layout

import pandas as pd
import matplotlib.pyplot as plt

bikes_hour = pd.read_csv('hour.csv')

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Create plots
hourly_pattern = bikes_hour.groupby('hr')['cnt'].mean()

axes[0, 0].plot(hourly_pattern.index, hourly_pattern.values, linewidth=2)
axes[0, 0].set_title('Hourly Pattern')

axes[0, 1].hist(bikes_hour['cnt'], bins=40, edgecolor='black', alpha=0.7)
axes[0, 1].set_title('Rental Distribution')

axes[1, 0].scatter(bikes_hour['temp'], bikes_hour['cnt'], alpha=0.3, s=5)
axes[1, 0].set_title('Temperature vs Rentals')

axes[1, 1].scatter(bikes_hour['hum'], bikes_hour['cnt'], alpha=0.3, s=5)
axes[1, 1].set_title('Humidity vs Rentals')

# Adjust spacing manually
plt.subplots_adjust(hspace=0.3, wspace=0.3)
plt.show()

Parameters:

  • hspace: vertical spacing (height)
  • wspace: horizontal spacing (width)
  • Values are fractions of subplot size

Alternative: plt.tight_layout() does this automatically!

Practical Example: Comprehensive Analysis

Weather Analysis Dashboard

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

bikes = pd.read_csv('day.csv')
bikes['dteday'] = pd.to_datetime(bikes['dteday'])

# Create comprehensive figure
fig = plt.figure(figsize=(16, 12))

# Grid: 3 rows, 2 columns
ax1 = plt.subplot(3, 2, 1)
ax2 = plt.subplot(3, 2, 2)
ax3 = plt.subplot(3, 2, 3)
ax4 = plt.subplot(3, 2, 4)
ax5 = plt.subplot(3, 2, 5)
ax6 = plt.subplot(3, 2, 6)

# Plot 1: Rentals over time
ax1.plot(bikes['dteday'], bikes['cnt'], color='steelblue', linewidth=1)
ax1.set_ylabel('Daily Rentals')
ax1.set_title('Daily Rentals Over Time')
ax1.grid(True, alpha=0.3)

# Plot 2: Rental distribution
ax2.hist(bikes['cnt'], bins=30, color='coral', edgecolor='black', alpha=0.7)
ax2.set_xlabel('Daily Rentals')
ax2.set_ylabel('Frequency')
ax2.set_title('Rental Distribution')
ax2.grid(True, alpha=0.3, axis='y')

# Plot 3: Temperature vs Rentals
ax3.scatter(bikes['temp'], bikes['cnt'], alpha=0.5, s=30, color='red')
corr_temp = bikes['temp'].corr(bikes['cnt'])
ax3.set_xlabel('Normalized Temperature')
ax3.set_ylabel('Daily Rentals')
ax3.set_title(f'Temperature vs Rentals (r = {corr_temp:.3f})')
ax3.grid(True, alpha=0.3)

# Plot 4: Humidity vs Rentals
ax4.scatter(bikes['hum'], bikes['cnt'], alpha=0.5, s=30, color='blue')
corr_hum = bikes['hum'].corr(bikes['cnt'])
ax4.set_xlabel('Humidity')
ax4.set_ylabel('Daily Rentals')
ax4.set_title(f'Humidity vs Rentals (r = {corr_hum:.3f})')
ax4.grid(True, alpha=0.3)

# Plot 5: Seasonal averages
season_avg = bikes.groupby('season')['cnt'].mean()
season_names = ['Spring', 'Summer', 'Fall', 'Winter']
ax5.bar(season_names, season_avg.values, color='orange', edgecolor='black')
ax5.set_ylabel('Average Daily Rentals')
ax5.set_title('Average Rentals by Season')
ax5.grid(True, alpha=0.3, axis='y')

# Plot 6: Weather condition impact
weather_avg = bikes.groupby('weathersit')['cnt'].mean()
weather_names = ['Clear', 'Mist', 'Light Rain']
ax6.barh(weather_names, weather_avg.values, color='green', edgecolor='black')
ax6.set_xlabel('Average Daily Rentals')
ax6.set_title('Weather Condition Impact')
ax6.grid(True, alpha=0.3, axis='x')

fig.suptitle('Bike Rental Analysis Dashboard', fontsize=18, fontweight='bold')
plt.tight_layout()
plt.show()

Comparison: Subplots vs GridSpec

Using plt.subplots()

import matplotlib.pyplot as plt

# Simple regular grid
fig, axes = plt.subplots(2, 2, figsize=(10, 8))

Best for: Regular grids where all subplots have same size.

Using GridSpec (Preview)

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

# Complex irregular layout
fig = plt.figure(figsize=(12, 8))
gs = GridSpec(3, 3, figure=fig)

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

Best for: Irregular layouts, different subplot sizes. (You will learn this in next lesson!)

Summary

You learned to create multi-panel figures with subplots:

  • plt.subplots(nrows, ncols) creates figure and axes
  • fig is the entire canvas, axes are the plotting areas
  • Access subplots: axes[i] for 1D, axes[row, col] for 2D grids
  • Plot on axes: ax.plot(), ax.scatter(), ax.hist(), etc.
  • Share axes: sharex=True, sharey=True for aligned comparisons
  • Iterate: Use axes.flat to loop over 2D grids
  • Spacing: plt.tight_layout() or plt.subplots_adjust()
  • Figure title: fig.suptitle() for overall title
  • Use cases: Comparing data, multi-variable analysis, dashboards

Next Steps: In the next lesson, you will learn advanced grid layouts with GridSpec for complex dashboard designs.

Practice: Create a 2×2 subplot showing: (1) hourly rental pattern, (2) temperature distribution, (3) humidity vs rentals scatter, (4) weather condition bar chart.

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Lesson 15 - Grid Charts