Creating Axes¶

There are multiple ways to create Axes objects in Matplotlib, each suited for different use cases.

plt.subplots¶

The most common method for creating figures with axes:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(-2np.pi, 2np.pi, 100) y = np.sin(x)

fig, ax = plt.subplots() ax.plot(x, y) plt.show() ```

With multiple subplots:

python fig, axes = plt.subplots(2, 2, figsize=(8, 6))

plt.axes¶

Create axes on the current figure:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(-2np.pi, 2np.pi, 100) y = np.exp(x)

fig = plt.figure() ax = plt.axes() ax.plot(x, y) plt.show() ```

With scale options:

python fig = plt.figure() ax = plt.axes(yscale='log') # Logarithmic y-axis ax.plot(x, y) plt.show()

fig.add_axes¶

For precise positioning using normalized coordinates:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(0, 3, 50) y1 = x ** 2 y2 = np.sqrt(x)

fig = plt.figure()

[left, bottom, width, height] - values from 0 to 1¶

ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) ax.plot(x, y1)

plt.show() ```

fig.add_subplot¶

Add subplots one at a time:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(0, 1, 50)

fig = plt.figure(figsize=(12, 6))

for s in range(12): ax = fig.add_subplot(3, 4, s + 1) ax.plot(x ** (s + 1)) ax.set_title(f"x^{s+1}")

plt.tight_layout() plt.show() ```

plt.subplot2grid¶

Create subplots with varying sizes:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(0, 3, 50) y1 = x ** 2 y2 = np.sqrt(x)

fig = plt.figure()

ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3) ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=2) ax3 = plt.subplot2grid((3, 3), (1, 2), rowspan=2) ax4 = plt.subplot2grid((3, 3), (2, 0)) ax5 = plt.subplot2grid((3, 3), (2, 1))

ax1.plot(x, y1) ax2.plot(y1, x) ax3.plot(x, y1) ax4.plot(x, y2) ax5.plot(y2, x)

fig.tight_layout() plt.show() ```

Parameters:

• First tuple: grid shape (nrows, ncols)
• Second tuple: starting position (row, col)
• colspan: columns to span
• rowspan: rows to span

GridSpec¶

For maximum control over subplot layout:

```python import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import numpy as np

x = np.linspace(0, 3, 50) y1 = x ** 2 y2 = np.sqrt(x)

fig = plt.figure() gs = gridspec.GridSpec(2, 3, height_ratios=[2, 1], width_ratios=[1, 2, 1])

for i, g in enumerate(gs): ax = fig.add_subplot(g) if i % 2 == 0: ax.plot(x, y1) else: ax.plot(x, y2)

fig.tight_layout() plt.show() ```

Inset Axes¶

Create axes within axes for detail views:

```python import matplotlib.pyplot as plt import numpy as np

x = np.linspace(0, 3, 50) y1 = x ** 2 y2 = np.sqrt(x)

fig = plt.figure()

ax0 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # Main axes ax1 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # Inset axes

ax0.plot(x, y1) ax1.plot(y1, x)

plt.show() ```

Pandas Integration¶

Pandas can plot directly to a specified axes:

```python import matplotlib.pyplot as plt import yfinance as yf

ticker = 'AAPL' df = yf.Ticker(ticker).history(start='2020-01-01', end='2020-12-31')

fig, axes = plt.subplots(2, 1, figsize=(12, 6), sharex=True)

df.Close.plot(ax=axes[0]) df.Volume.plot(ax=axes[1])

plt.show() ```

Comparison Summary¶

Key Takeaways¶

• plt.subplots is the workhorse for most plots
• Use fig.add_axes for precise control
• subplot2grid and GridSpec handle complex layouts
• Inset axes are created with multiple add_axes calls
• Pandas plotting integrates via the ax parameter

Exercises¶

Exercise 1. Create a figure and add three axes using three different methods: fig.add_subplot(1, 3, 1), fig.add_axes([0.4, 0.15, 0.25, 0.7]) for the second, and plt.subplots for the third. Plot a different function on each.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2 * np.pi, 200)

fig = plt.figure(figsize=(12, 4))

ax1 = fig.add_subplot(1, 3, 1)
ax1.plot(x, np.sin(x))
ax1.set_title('add_subplot')

ax2 = fig.add_axes([0.4, 0.15, 0.25, 0.7])
ax2.plot(x, np.cos(x), color='red')
ax2.set_title('add_axes')

ax3 = fig.add_subplot(1, 3, 3)
ax3.plot(x, np.tan(x), color='green')
ax3.set_ylim(-5, 5)
ax3.set_title('add_subplot again')

plt.show()

Exercise 2. Create an inset axes inside a main plot. Plot y = sin(x) on the main axes over \([0, 4\pi]\), and add a small inset axes using fig.add_axes([0.55, 0.55, 0.3, 0.3]) that zooms into the region \([0, \pi]\).

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 4 * np.pi, 500)

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(x, np.sin(x), color='steelblue')
ax.set_title('sin(x) with Inset Zoom')
ax.set_xlabel('x')
ax.set_ylabel('y')

ax_inset = fig.add_axes([0.55, 0.55, 0.3, 0.3])
x_zoom = np.linspace(0, np.pi, 200)
ax_inset.plot(x_zoom, np.sin(x_zoom), color='red')
ax_inset.set_title('Zoom: [0, π]', fontsize=9)
ax_inset.set_xlim(0, np.pi)

plt.show()

Exercise 3. Use fig.add_subplot to create an irregular layout: one tall subplot on the left spanning 2 rows (fig.add_subplot(1, 2, 1)) and two small subplots stacked on the right (fig.add_subplot(2, 2, 2) and fig.add_subplot(2, 2, 4)). Plot different data in each.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2 * np.pi, 200)

fig = plt.figure(figsize=(10, 6))

ax_left = fig.add_subplot(1, 2, 1)
ax_left.plot(x, np.sin(x), color='blue', linewidth=2)
ax_left.set_title('Tall Left Subplot')

ax_top_right = fig.add_subplot(2, 2, 2)
ax_top_right.bar(['A', 'B', 'C'], [3, 7, 5], color='coral')
ax_top_right.set_title('Top Right')

ax_bottom_right = fig.add_subplot(2, 2, 4)
ax_bottom_right.scatter(np.random.rand(20), np.random.rand(20), color='green')
ax_bottom_right.set_title('Bottom Right')

plt.tight_layout()
plt.show()