Axes Shape Behavior

Understanding how plt.subplots returns different shaped arrays is crucial for writing robust plotting code.

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Single Axes

With no arguments or (1, 1), a single Axes object is returned (not an array):

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
print(type(ax))  # <class 'matplotlib.axes._subplots.AxesSubplot'>

fig, ax = plt.subplots(1, 1)
print(type(ax))  # <class 'matplotlib.axes._subplots.AxesSubplot'>

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1D Arrays (1×N or N×1)

When creating a single row or column, the result is a 1D array:

import matplotlib.pyplot as plt
import numpy as np

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

# 1 row, 2 columns -> shape (2,), not (1, 2)
fig, axes = plt.subplots(1, 2)
axes[0].plot(x, np.sin(x))
axes[1].plot(x, np.cos(x))
plt.show()

print(type(axes))   # numpy.ndarray
print(axes.shape)   # (2,)
print(axes.dtype)   # object

# 2 rows, 1 column -> shape (2,), not (2, 1)
fig, axes = plt.subplots(2, 1)
axes[0].plot(x, np.sin(x))
axes[1].plot(x, np.cos(x))
plt.show()

print(axes.shape)   # (2,)

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2D Arrays (N×M)

Only when both dimensions are greater than 1:

import matplotlib.pyplot as plt
import numpy as np

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

fig, axes = plt.subplots(2, 2)

axes[0, 0].plot(x, np.sin(x))
axes[0, 1].plot(x, np.cos(x))
axes[1, 0].plot(x, np.sinh(x))
axes[1, 1].plot(x, np.cosh(x))

plt.show()

print(type(axes))   # numpy.ndarray
print(axes.shape)   # (2, 2)
print(axes.dtype)   # object

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The squeeze Parameter

Use squeeze=False to always get a 2D array:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0.001, 1, 100)

# Without squeeze=False
fig, axs = plt.subplots(1, 3, figsize=(12, 3))
print(axs.shape)  # (3,)
axs[0].plot(x, x**2)
axs[1].plot(x, np.sin(x))
axs[2].plot(x, np.exp(x))

# With squeeze=False
fig, axs = plt.subplots(1, 3, figsize=(12, 3), squeeze=False)
print(axs.shape)  # (1, 3)
axs[0, 0].plot(x, x**2)
axs[0, 1].plot(x, np.sin(x))
axs[0, 2].plot(x, np.exp(x))

plt.tight_layout()
plt.show()

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Why Use squeeze=False?

Consistent array access is useful when:

1. Looping over subplots:

   fig, axes = plt.subplots(1, 3, squeeze=False)
   for i in range(1):
       for j in range(3):
           axes[i, j].plot([1, 2, 3])
   

2. Writing generic functions:

   def setup_grid(nrows, ncols):
       fig, axes = plt.subplots(nrows, ncols, squeeze=False)
       # Always use axes[i, j] regardless of dimensions
       return fig, axes
   

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Shape Summary Table

Subplots	Returns	Shape	Access
plt.subplots()	Axes	N/A	ax
plt.subplots(1, 3)	1D array	(3,)	axes[j]
plt.subplots(3, 1)	1D array	(3,)	axes[i]
plt.subplots(2, 3)	2D array	(2, 3)	axes[i, j]
plt.subplots(1, 3, squeeze=False)	2D array	(1, 3)	axes[0, j]

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Practical Implications

Handle different cases in code:

import matplotlib.pyplot as plt
import numpy as np

def plot_functions(funcs, figsize=(12, 3)):
    n = len(funcs)
    fig, axes = plt.subplots(1, n, figsize=figsize, squeeze=False)

    x = np.linspace(0, 2*np.pi, 100)

    for j, func in enumerate(funcs):
        axes[0, j].plot(x, func(x))

    plt.tight_layout()
    return fig, axes

# Works for any number of functions
plot_functions([np.sin, np.cos, np.tan])
plt.show()

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Key Takeaways

• Single axes returns an Axes object, not an array
• 1×N or N×1 returns a 1D array with shape (N,)
• N×M (both > 1) returns a 2D array with shape (N, M)
• Use squeeze=False for consistent 2D array access
• Handle shape variations when writing generic plotting functions