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* * Matplotlib Reference](#) `figure()` is used to create a new Figure (canvas) or activate an existing Figure. Figure is the top-level container for all plotting, managing the entire chart's size, DPI, and all subplots. ## Function Definition matplotlib.pyplot.figure(num=None, figsize=None, dpi=None, *, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class 'matplotlib.figure.Figure'>, clear=False, layout=None, **kwargs) ## Parameter Description | Parameter | Type | Description | | --- | --- | --- | | num | int or str | Figure identifier. Integer for number, string for label. Creates new if doesn't exist, activates existing if present | | figsize | tuple (w, h) | Figure size (inches), e.g. (8, 6). Default determined by rcParams | | dpi | float | Resolution (dots per inch), e.g. 100. Affects pixel output size | | facecolor | color | Figure background color | | edgecolor | color | Figure border color | | frameon | bool | Whether to display Figure background frame, default True | | clear | bool | If num already exists, whether to clear before use | | layout | str or LayoutEngine | Recommended layout engines: 'constrained', 'compressed', 'tight', 'none' | > In daily use, `figure()` is rarely called directly; `subplots()` is more commonly used to create Figure and Axes at once. Using figure() alone is mainly for multi-Figure scenarios or custom canvas properties. * * * ## Usage Examples ### Example 1: Creating a Figure with Custom Size ## Example import matplotlib.pyplot as plt import numpy as np # Create a Figure with specified size and high DPI fig = plt.figure(figsize=(10,4), dpi=100, facecolor='#f8f9fa', layout='constrained') # Manually add subplots ax1 = fig.add_subplot(1,2,1) ax2 = fig.add_subplot(1,2,2) x = np.linspace(0,10,100) ax1.plot(x, np.sin(x),'steelblue', linewidth=2) ax1.set_title('Plot 1') ax2.plot(x, np.cos(x),'coral', linewidth=2) ax2.set_title('Plot 2') plt.show() ### Example 2: Multiple Figure Management ## Example import matplotlib.pyplot as plt import numpy as np x = np.linspace(0,10,100) # Create Figure #1 fig1 = plt.figure(1, figsize=(6,4)) ax1 = fig1.subplots() ax1.plot(x, np.sin(x),'blue') ax1.set_title('Figure 1: sin(x)') # Create Figure #2 fig2 = plt.figure(2, figsize=(6,4)) ax2 = fig2.subplots() ax2.plot(x, np.cos(x),'red') ax2.set_title('Figure 2: cos(x)') # Activate Figure #1 and continue operations plt.figure(1) plt.plot(x, np.sin(2*x),'--', alpha=0.5) # Save specific Figure fig1.savefig('tutorial_fig1.png', dpi=150) fig2.savefig('tutorial_fig2.png', dpi=150) print("tutorial: two figures saved") plt.show() ### Example 3: Automatic Layout with layout Parameter ## Example import matplotlib.pyplot as plt import numpy as np # Compare layout settings # Without layout (labels may overlap) fig1 = plt.figure(1, figsize=(8,4)) for i in range(3): ax = fig1.add_subplot(1,3, i+1) ax.plot(np.random.randn(50).cumsum()) ax.set_title(f'Plot {i+1}') ax.set_xlabel('A very long x label') # Using layout='constrained' (automatically handles overlap) fig2 = plt.figure(2, figsize=(8,4), layout='constrained') for i in range(3): ax = fig2.add_subplot(1,3, i+1) ax.plot(np.random.randn(50).cumsum()) ax.set_title(f'Plot {i+1}') ax.set_xlabel('A very long x label') plt.figure(1) plt.suptitle('Without layout', y=1.02) plt.figure(2) plt.suptitle('With layout="constrained"', y=1.02) plt.show() print("tutorial: compare layout settings") * * * ## Frequently Asked Questions ### What is the unit of measurement for figsize? Inches. Actual pixel size = figsize × dpi. For example, `figsize=(8,6), dpi=100` generates an 800×600 pixel image. ### Which layout option is best? `'constrained'` is the recommended option for most scenarios, automatically handling subplot and label spacing. `'compressed'` further compresses margins, suitable for scenarios requiring maximized data area. `'none'` does not use automatic layout (default), allowing manual adjustment via subplots_adjust(). [ Matplotlib Reference](#)