Matplotlib Functions
Matplotlib is the most widely used data visualization library in Python, supporting the creation of static, animated, and interactive charts.
This document comprehensively organizes the functions and methods of all public interfaces in Matplotlib, making it easy to quickly look up corresponding features and usage.
Two Major Programming Interfaces
Matplotlib provides two usage approaches, suitable for different scenarios.
| Feature | Axes Interface (Explicit/Object-Oriented) | pyplot Interface (Implicit/Functional) |
|---|---|---|
| Usage | First create Figure and Axes objects, then call object methods. | Directly call pyplot module functions, implicitly operating on the current figure. |
| Applicable Scenarios | Complex charts, multiple subplots, fine-grained control required. | Quick plotting, interactive exploration, simple charts. |
| Code Example | fig, ax = plt.subplots(); ax.plot(x, y) | plt.plot(x, y); plt.title("Title") |
| Recommendation Level | Recommended(Clearer, More Controllable) | Suitable for simple scenarios and rapid prototyping. |
The Axes interface is the officially recommended programming approach. The code logic is clearer and less error-prone when handling multiple subplots and complex charts. For quick reference, the following lists both pyplot functions and the corresponding Axes methods.
pyplot Module - Full Function List
pyplot is Matplotlib's top-level interface, providing a MATLAB-like plotting experience. The following lists all functions by official category.
1. Figure and Axes Management
Functions for creating and managing Figure (canvas) and Axes (coordinate system/subplot).
| Function | Description |
|---|---|
| figure() | Create a new Figure or activate an existing Figure. |
| subplots() | RecommendedCreate a Figure and a set of Axes subplots. |
| subplot() | Add a single subplot to the current Figure (by row and column index). |
| subplot2grid() | Create a subplot at a specified position in a grid layout. |
| subplot_mosaic() | Create complex non-uniform layout using label strings. |
| axes() | Add an Axes to the current Figure. |
| gca() | Get the current Axes object. |
| gcf() | Get the current Figure object. |
| sca() | Set the current Axes. |
| cla() | Clear the current Axes. |
| clf() | Clear the current Figure. |
| close() | Close the Figure window. |
| delaxes() | Remove the specified Axes from the Figure. |
| fignum_exists() | Check whether a Figure with the specified number exists. |
| get_figlabels() | Return the list of labels of all Figures. |
| get_fignums() | Return the list of numbers of all Figures. |
| twinx() | Create a twin y-axis sharing the x-axis. |
| twiny() | Create a twin x-axis sharing the y-axis. |
2. Basic Plotting
The most commonly used chart type plotting functions.
| Function | Description |
|---|---|
| plot() | Plot a line chart (most common). |
| scatter() | Plot a scatter plot, supporting size/color/transparency mapping. |
| bar() | Plot a vertical bar chart. |
| barh() | Plot a horizontal bar chart. |
| bar_label() | Add value labels to the bars of a bar chart. |
| grouped_bar() | Plot a grouped bar chart. |
| pie() | Plot a pie chart. |
| pie_label() | Add labels to a pie chart. |
| stem() | Plot a stem plot (matchstick plot). |
| eventplot() | Plot an event plot (multiple horizontal lines marking event positions). |
| step() | Plot a step chart. |
| fill() | Draw filled polygons. |
| fill_between() | Fill the area between two horizontal curves. |
| fill_betweenx() | Fill the area between two vertical curves. |
| stackplot() | Plot a stacked area chart. |
| broken_barh() | Plot horizontal broken bar chart (Gantt chart style). |
| vlines() | Draw a vertical reference line. |
| hlines() | Draw a horizontal reference line. |
| errorbar() | Plot a line chart with error bars. |
| loglog() | Log-log line chart. |
| semilogx() | Line chart with logarithmic x-axis. |
| semilogy() | Line chart with logarithmic y-axis. |
| polar() | Plot in polar coordinates. |
3. Span Lines (Spans)
Draw horizontal and vertical reference lines and shaded regions.
| Function | Description |
|---|---|
| axhline() | Add a horizontal line spanning the Axes. |
| axhspan() | Add a horizontal shaded band spanning the Axes. |
| axvline() | Add a vertical line spanning the Axes. |
| axvspan() | Add a vertical shaded band spanning the Axes. |
| axline() | Add an infinite line passing through two points. |
4. Spectral Analysis
Functions for signal processing and spectral visualization.
| Function | Description |
|---|---|
| acorr() | Plot an autocorrelation plot. |
| xcorr() | Plot cross-correlation |
| angle_spectrum() | Plot angle spectrum |
| magnitude_spectrum() | Plot magnitude spectrum |
| phase_spectrum() | Plot phase spectrum |
| psd() | Plot power spectral density |
| csd() | Plot cross-spectral density |
| cohere() | Plot coherence |
| specgram() | Plot spectrogram (time-frequency plot) |
5. Statistical Charts
Functions for plotting statistical distributions.
| Function | Description |
|---|---|
| boxplot() | Plot boxplot |
| violinplot() | Plot violin plot |
| ecdf() | Plot empirical cumulative distribution function (ECDF) |
6. Binning and Histograms
| Function | Description |
|---|---|
| hist() | Plot 1D histogram |
| hist2d() | Plot 2D histogram |
| hexbin() | Plot hexagonal binning plot |
| stairs() | Plot step histogram (new version, replacing hist's step mode) |
7. Contour Lines
| Function | Description |
|---|---|
| contour() | Plot contour lines |
| contourf() | Plot filled contours |
| clabel() | Add labels to contours |
8. 2D Arrays and Images
Visualization functions for displaying 2D data, matrices, and images.
| Function | Description |
|---|---|
| imshow() | Display an image or 2D array (heatmap) |
| matshow() | Display a matrix as an image in a new Figure |
| pcolor() | Plot pseudocolor mesh (creates PolyCollection) |
| pcolormesh() | Plot pseudocolor mesh (creates QuadMesh, better performance) |
| spy() | Plot the pattern of nonzero elements of a sparse matrix |
| figimage() | Place an image at the Figure level (not Axes) |
9. Unstructured Triangular Grids
| Function | Description |
|---|---|
| triplot() | Plot an unstructured triangular grid |
| tripcolor() | Plot pseudocolor plot on triangular grid |
| tricontour() | Plot contour lines on triangular grid |
| tricontourf() | Plot filled contours on triangular grid |
10. Text and Annotations
| Function | Description |
|---|---|
| text() | Add text at specified coordinates in Axes |
| figtext() | Add text at specified position in Figure |
| annotate() | Add annotation with arrow |
| arrow() | Add arrow |
| legend() | Add legend in Axes |
| figlegend() | Add legend at Figure level |
| table() | Add table in Axes |
11. Vector Fields
| Function | Description |
|---|---|
| quiver() | Plot vector field (quiver plot) |
| quiverkey() | Add legend scale to vector field |
| barbs() | Plot barb plot (representing wind speed and direction in meteorology) |
| streamplot() | Plot streamlines |
12. Axis Configuration
Functions for setting axis limits, ticks, labels, and scales.
| Function | Description |
|---|---|
| title() | Set Axes title |
| suptitle() | Set Figure suptitle |
| xlabel() | Set x-axis label |
| ylabel() | Set y-axis label |
| xlim() | Get or set x-axis limits |
| ylim() | Get or set y-axis limits |
| xscale() | Set x-axis scale (linear/log/symlog/logit...) |
| yscale() | Set y-axis scale (linear/log/symlog/logit...) |
| xticks() | Get or set x-axis tick positions and labels |
| yticks() | Get or set y-axis tick positions and labels |
| tick_params() | Adjust tick appearance (direction, color, size, label rotation, etc.) |
| ticklabel_format() | Set tick label format (scientific notation, etc.) |
| locator_params() | Control tick locator parameters |
| minorticks_on() | Show minor ticks |
| minorticks_off() | Hide minor ticks |
| rgrids() | Get or set radial gridlines of polar plot |
| thetagrids() | Get or set angular gridlines of polar plot |
| grid() | Turn gridlines on or off |
| axis() | Convenience function to get or set certain axis properties |
| box() | Turn Axes border lines on or off |
| autoscale() | Automatically scale axes to fit data |
13. Layout
Functions for controlling subplot arrangement and spacing.
| Function | Description |
|---|---|
| subplots_adjust() | Manually adjust subplot spacing (left/right/top/bottom/wspace/hspace) |
| tight_layout() | Automatically adjust subplot parameters for a tight layout |
| margins() | Set or get data margins of axes |
| subplot_tool() | Launch interactive subplot adjustment tool window |
14. Colormap
Functions related to colormaps and colorbars.
| Function | Description |
|---|---|
| colorbar() | Add colorbar |
| clim() | Set colormap data range |
| get_cmap() | Get the colormap object with the specified name |
| set_cmap() | Set default colormap |
| gci() | Get the current color-mappable image object |
| sci() | Set the current color-mappable image object |
| imread() | Read image from file into array |
| imsave() | Save array as image file |
| colormaps | Colormap registry object |
| color_sequences | Color sequence registry object |
15. Configuration
Functions for managing Matplotlib global configuration parameters.
| Function | Description |
|---|---|
| rc() | Set rc parameters (can be set in batch) |
| rc_context() | Context manager for temporarily setting rc parameters |
| rcdefaults() | Restore all rc parameters to default values |
16. Output and Interaction
Functions for controlling plot display, saving, and interaction modes.
| Function | Description |
|---|---|
| show() | Display all open Figures |
| savefig() | Save current Figure to file |
| draw() | Force re-render of current Figure |
| draw_if_interactive() | Render Figure if in interactive mode |
| pause() | Pause for the specified number of seconds (processing events during the pause) |
| ion() | Turn on interactive mode |
| ioff() | Turn off interactive mode |
| isinteractive() | Return whether currently in interactive mode |
| install_repl_displayhook() | Install REPL display hook (to display Figures automatically) |
| uninstall_repl_displayhook() | Uninstall REPL display hook |
| switch_backend() | Switch backend |
17. Other Utility Functions
| Function | Description |
|---|---|
| connect() | Bind event callback function |
| disconnect() | Unbind event callback function |
| ginput() | Get coordinate points via mouse clicks |
| waitforbuttonpress() | Wait for mouse or keyboard press, return event type |
| findobj() | Find Artist objects matching the specified conditions |
| get() | Get the property value of an Artist object |
| getp() | Get the properties of an Artist object (alias of get) |
| setp() | Set the properties of an Artist object |
| get_current_fig_manager() | Get the window manager of the current Figure |
| new_figure_manager() | Create a new Figure manager for the specified Figure |
| set_loglevel() | Set the Matplotlib log level |
| xkcd() | Switch to xkcd (hand-drawn comic) style |
Axes Object - Full Method List
Axes (Axes object) represents a subplot within a Figure, containing data, ticks, labels, titles, etc. The following lists all methods completely according to the official classification.
1. Basic Plotting Methods
| Method | Description |
|---|---|
| Axes.plot() | Line plot |
| Axes.scatter() | Scatter plot |
| Axes.bar() | Vertical bar chart |
| Axes.barh() | Horizontal bar chart |
| Axes.bar_label() | Add value labels on bars |
| Axes.grouped_bar() | Grouped bar chart |
| Axes.pie() | Pie chart |
| Axes.pie_label() | Pie chart labels |
| Axes.stem() | Stem plot |
| Axes.eventplot() | Event plot |
| Axes.step() | Step plot |
| Axes.fill() | Fill polygons |
| Axes.fill_between() | Horizontal fill region |
| Axes.fill_betweenx() | Vertical fill region |
| Axes.stackplot() | Stacked area plot |
| Axes.broken_barh() | Horizontal broken bar chart |
| Axes.vlines() | Vertical reference line |
| Axes.hlines() | Horizontal reference line |
| Axes.errorbar() | Line plot with error bars |
| Axes.loglog() | Log-log scale |
| Axes.semilogx() | x-axis log scale |
| Axes.semilogy() | y-axis log scale |
2. Span Line Methods
| Method | Description |
|---|---|
| Axes.axhline() | Horizontal reference line |
| Axes.axvline() | Vertical reference line |
| Axes.axhspan() | Horizontal shaded band |
| Axes.axvspan() | Vertical shaded band |
| Axes.axline() | Infinite line through two points |
3. Spectral Analysis Methods
| Method | Description |
|---|---|
| Axes.acorr() | Autocorrelation plot |
| Axes.xcorr() | Cross-correlation plot |
| Axes.angle_spectrum() | Angle spectrum |
| Axes.magnitude_spectrum() | Magnitude spectrum |
| Axes.phase_spectrum() | Phase spectrum |
| Axes.psd() | Power spectral density |
| Axes.csd() | Cross spectral density |
| Axes.cohere() | Coherence |
| Axes.specgram() | Spectrogram (time-frequency plot) |
4. Statistical Methods
| Method | Description |
|---|---|
| Axes.boxplot() | Box plot |
| Axes.bxp() | Draw a box plot from precomputed statistics |
| Axes.violinplot() | Violin plot |
| Axes.violin() | Draw a violin plot from precomputed statistics |
| Axes.ecdf() | Empirical cumulative distribution function |
5. Binning and Histogram Methods
| Method | Description |
|---|---|
| Axes.hist() | Histogram |
| Axes.hist2d() | 2D histogram |
| Axes.hexbin() | Hexbin plot |
| Axes.stairs() | Stairs histogram |
6. Contour Methods
| Method | Description |
|---|---|
| Axes.contour() | Contour |
| Axes.contourf() | Filled contour |
| Axes.clabel() | Contour labels |
7. 2D Array Methods
| Method | Description |
|---|---|
| Axes.imshow() | Display image/heatmap |
| Axes.matshow() | Matrix image |
| Axes.pcolor() | Pseudocolor mesh (PolyCollection) |
| Axes.pcolorfast() | Fast pseudocolor (drawn with imshow) |
| Axes.pcolormesh() | Pseudocolor mesh (QuadMesh, recommended) |
| Axes.spy() | Sparse matrix pattern |
8. Unstructured Triangular Grid Methods
| Method | Description |
|---|---|
| Axes.triplot() | Triangular grid lines |
| Axes.tripcolor() | Triangular grid pseudocolor |
| Axes.tricontour() | Triangular grid contour |
| Axes.tricontourf() | Triangular grid filled contour |
9. Text and Annotation Methods
| Method | Description |
|---|---|
| Axes.text() | Add text |
| Axes.annotate() | Add annotation with arrow |
| Axes.table() | Add table |
| Axes.arrow() | Add arrow |
| Axes.inset_axes() | Create an inset axes within the Axes |
| Axes.indicate_inset() | Mark the inset axes region on the parent Axes |
| Axes.indicate_inset_zoom() | Mark the zoomed region of the inset axes |
| Axes.secondary_xaxis() | Add a secondary x-axis (display the same data at another location) |
| Axes.secondary_yaxis() | Add a secondary y-axis |
10. Vector Field Methods
| Method | Description |
|---|---|
| Axes.quiver() | Vector field (arrow plot) |
| Axes.quiverkey() | Vector field legend scale bar |
| Axes.barbs() | Wind barb plot |
| Axes.streamplot() | Streamline plot |
11. Clearing Methods
| Method | Description |
|---|---|
| Axes.cla() | Clear all content in the Axes |
| Axes.clear() | Clear the Axes (same as cla()) |
12. Appearance Methods
| Method | Description |
|---|---|
| Axes.axis() | Set axis visibility or range |
| Axes.set_axis_off() | Hide the axes |
| Axes.set_axis_on() | Show the axes |
| Axes.set_frame_on() | Show the Axes frame |
| Axes.get_frame_on() | Get whether the frame is visible |
| Axes.set_axisbelow() | Set whether grid/ticks are below data |
| Axes.get_axisbelow() | Get the grid/tick layer |
| Axes.grid() | Set grid lines |
| Axes.get_facecolor() | Get the Axes background color |
| Axes.set_facecolor() | Set the Axes background color |
13. Property Cycling
| Method | Description |
|---|---|
| Axes.set_prop_cycle() | Set the property cycle for line colors/linestyles, etc. |
14. Axis and Ticks Control
Axis access:
| Method | Description |
|---|---|
| Axes.xaxis / Axes.yaxis | Get the Axis object for the x/y axis (property) |
| Axes.get_xaxis() / get_yaxis() | Get the Axis object for the x/y axis |
Axis range and direction:
| Method | Description |
|---|---|
| Axes.set_xlim() / get_xlim() | Set/get the x-axis range |
| Axes.set_ylim() / get_ylim() | Set/get the y-axis range |
| Axes.set_xbound() / get_xbound() | Set/get the lower and upper bounds of the x-axis |
| Axes.set_ybound() / get_ybound() | Set/get the lower and upper bounds of the y-axis |
| Axes.invert_xaxis() | Invert the x-axis direction |
| Axes.invert_yaxis() | Invert the y-axis direction |
| Axes.xaxis_inverted() / yaxis_inverted() | Query whether the axis is inverted |
| Axes.set_xinverted() / get_xinverted() | Set/get whether the x-axis is inverted |
| Axes.set_yinverted() / get_yinverted() | Set/get whether the y-axis is inverted |
| Axes.update_datalim() | Extend the data limits with new data points |
Axis labels and legend:
| Method | Description |
|---|---|
| Axes.set_xlabel() / get_xlabel() | Set/get the x-axis label |
| Axes.set_ylabel() / get_ylabel() | Set/get the y-axis label |
| Axes.label_outer() | Keep tick labels only on the outermost subplots |
| Axes.set_title() / get_title() | Set/get the Axes title |
| Axes.legend() | Add a legend |
| Axes.get_legend() | Get the current legend object |
| Axes.get_legend_handles_labels() | Get the legend handles and labels |
Axis scale:
| Method | Description |
|---|---|
| Axes.set_xscale() / get_xscale() | Set/get the x-axis scale |
| Axes.set_yscale() / get_yscale() | Set/get the y-axis scale |
Autoscaling and margins:
| Method | Description |
|---|---|
| Axes.autoscale() | Autoscale the view to fit the data |
| Axes.autoscale_view() | Autoscale the view only (without changing margins) |
| Axes.margins() | Set or get data margins |
| Axes.relim() | Recalculate the data limits based on the current Artists |
| Axes.use_sticky_edges() | Use sticky edges |
| Axes.set_xmargin() / get_xmargin() | Set/get the x-axis margin |
| Axes.set_ymargin() / get_ymargin() | Set/get the y-axis margin |
| Axes.set_autoscale_on() / get_autoscale_on() | Set/get whether to autoscale |
| Axes.set_autoscalex_on() / get_autoscalex_on() | Set/get whether the x-axis autoscales |
| Axes.set_autoscaley_on() / get_autoscaley_on() | Set/get whether the y-axis autoscales |
Aspect ratio:
| Method | Description |
|---|---|
| Axes.set_aspect() / get_aspect() | Set/get the axes aspect ratio ('equal'/'auto'/number) |
| Axes.set_box_aspect() / get_box_aspect() | Set/get the Axes box aspect ratio |
| Axes.apply_aspect() | Apply the current aspect ratio setting |
| Axes.set_adjustable() / get_adjustable() | Set/get the adjustable direction ('box'/'datalim') |
Ticks and tick labels:
| Method | Description |
|---|---|
| Axes.set_xticks() / get_xticks() | Set/get the x-axis tick positions |
| Axes.set_yticks() / get_yticks() | Set/get the y-axis tick positions |
| Axes.set_xticklabels() / get_xticklabels() | Set/get the x-axis tick labels |
| Axes.set_yticklabels() / get_yticklabels() | Set/get the y-axis tick labels |
| Axes.get_xmajorticklabels() | Get the x-axis major tick labels |
| Axes.get_xminorticklabels() | Get the x-axis minor tick labels |
| Axes.get_ymajorticklabels() | Get the y-axis major tick labels |
| Axes.get_yminorticklabels() | Get the y-axis minor tick labels |
| Axes.get_xgridlines() | Get the x-axis grid lines |
| Axes.get_ygridlines() | Get the y-axis grid lines |
| Axes.get_xticklines() | Get the x-axis tick lines |
| Axes.get_yticklines() | Get the y-axis tick lines |
| Axes.xaxis_date() | Set the x-axis ticks to date format |
| Axes.yaxis_date() | Set the y-axis ticks to date format |
| Axes.minorticks_on() | Show minor ticks |
| Axes.minorticks_off() | Hide minor ticks |
| Axes.ticklabel_format() | Set the tick label format |
| Axes.tick_params() | Adjust tick appearance parameters |
| Axes.locator_params() | Control tick locator parameters |
15. Units
| Method | Description |
|---|---|
| Axes.convert_xunits() | Convert x values using the unit converter |
| Axes.convert_yunits() | Convert y values using the unit converter |
| Axes.have_units() | Check whether a unit converter is registered |
16. Adding Artists
| Method | Description |
|---|---|
| Axes.add_artist() | Add an arbitrary Artist object |
| Axes.add_child_axes() | Add a child Axes |
| Axes.add_collection() | Add a Collection object |
| Axes.add_container() | Add a Container object |
| Axes.add_image() | Add an AxesImage object |
| Axes.add_line() | Add a Line2D object |
| Axes.add_patch() | Add a Patch object |
| Axes.add_table() | Add a Table object |
17. Twin Axes and Shared Axes
| Method | Description |
|---|---|
| Axes.twinx() | Create a twin y-axis sharing the x-axis |
| Axes.twiny() | Create a twin x-axis sharing the y-axis |
| Axes.sharex() | Share the x-axis with other Axes |
| Axes.sharey() | Share the y-axis with other Axes |
| Axes.get_shared_x_axes() | Get the Grouper object for shared x-axes |
| Axes.get_shared_y_axes() | Get the Grouper object for shared y-axes |
18. Axes Position
| Method | Description |
|---|---|
| Axes.get_position() / set_position() | Get/set the position and size of the Axes in the Figure |
| Axes.get_anchor() / set_anchor() | Get/set the anchor point (fixed position) of the Axes |
| Axes.get_axes_locator() / set_axes_locator() | Get/set the Axes locator callback function |
| Axes.get_subplotspec() / set_subplotspec() | Get/set the SubplotSpec object |
| Axes.reset_position() | Reset the Axes position to its original value |
19. Asynchronous/Events
| Method | Description |
|---|---|
| Axes.stale | Mark whether the Artist needs redrawing (property) |
| Axes.pchanged() | Notify a property change event |
| Axes.add_callback() | Add a property change callback |
| Axes.remove_callback() | Remove a property change callback |
20. Interaction
| Method | Description |
|---|---|
| Axes.can_pan() / can_zoom() | Return whether pan/zoom is enabled |
| Axes.set_navigate() / get_navigate() | Set/get whether the navigation toolbar is active |
| Axes.set_navigate_mode() / get_navigate_mode() | Set/get the navigation mode |
| Axes.start_pan() / drag_pan() / end_pan() | Start/drag/end of pan operation |
| Axes.format_coord() | Format the coordinate string displayed on the toolbar |
| Axes.format_cursor_data() | Format the data value at the cursor |
| Axes.format_xdata() / format_ydata() | Format x/y data values |
| Axes.mouseover() | Determine whether the mouse is over the Axes |
| Axes.in_axes() | Determine whether a point is inside the Axes |
| Axes.contains() / contains_point() | Determine whether an Artist contains a point |
| Axes.get_cursor_data() | Get the data at the cursor position |
| Axes.get_forward_navigation_events() / set_forward_navigation_events() | Get/set navigation event forwarding |
21. Child Element Querying
| Method | Description |
|---|---|
| Axes.get_children() | Get all child Artists |
| Axes.get_images() | Get all image objects |
| Axes.get_lines() | Get all line objects |
| Axes.findobj() | Find child Artists matching the condition |
22. Drawing
| Method | Description |
|---|---|
| Axes.draw() | Render the Axes |
| Axes.draw_artist() | Draw a single Artist (low-level method) |
| Axes.redraw_in_frame() | Redraw within the frame |
| Axes.get_window_extent() | Get the Axes bounds in the display window |
| Axes.get_tightbbox() | Get the tight bounding box of the Axes |
| Axes.get_rasterization_zorder() / set_rasterization_zorder() | Get/set the zorder threshold for rasterization |
23. Projection (Subclasses Should Override)
| Method | Description |
|---|---|
| Axes.name | Projection name (e.g., 'rectilinear', 'polar') |
| Axes.get_xaxis_transform() / get_yaxis_transform() | Get the x/y axis transform |
| Axes.get_data_ratio() | Get the data aspect ratio |
| Axes.get_xaxis_text1_transform() | Bottom label transform of the x-axis |
| Axes.get_xaxis_text2_transform() | Top label transform of the x-axis |
| Axes.get_yaxis_text1_transform() | Left label transform of the y-axis |
| Axes.get_yaxis_text2_transform() | Right label transform of the y-axis |
24. Other Methods
| Method | Description |
|---|---|
| Axes.set() | Batch set properties |
| Axes.zorder | Get/set zorder (property) |
| Axes.get_figure() | Get the parent Figure object |
| Axes.figure | Parent Figure object (property) |
| Axes.remove() | Remove itself from the Figure |
| Axes.has_data() | Check whether there is data |
| Axes.get_default_bbox_extra_artists() | Get artists to additionally include in bounding box calculation |
| Axes.get_transformed_clip_path_and_affine() | Get the transformed clipping path |
| Axes.viewLim | View limits (property) |
| Axes.dataLim | Data limits (property) |
| Axes.spines | Spine dictionary (property) |
Figure Object - Full Method List
Figure is the top-level container of the entire chart, managing all Axes, Artists, and layout.
1. Adding Axes and SubFigure
| Method | Description |
|---|---|
| Figure.subplots() | RecommendedCreate an Axes subplot grid |
| Figure.add_subplot() | Add a single subplot at a given row/column position |
| Figure.add_axes() | Add an Axes at a specified position and size |
| Figure.subplot_mosaic() | Create complex subplot arrangements using label-based layout |
| Figure.add_gridspec() | Add a GridSpec layout object |
| Figure.subfigures() | Create a nested SubFigure |
| Figure.add_subfigure() | Add a single SubFigure |
| Figure.axes | Axes list (property) |
| Figure.get_axes() | Get all Axes |
| Figure.delaxes() | Remove the specified Axes |
2. Saving
| Method | Description |
|---|---|
| Figure.savefig() | Save the Figure to a file |
3. Figure-level Annotations
| Method | Description |
|---|---|
| Figure.suptitle() / get_suptitle() | Set/get the Figure suptitle |
| Figure.supxlabel() / get_supxlabel() | Set/get the Figure-level x label |
| Figure.supylabel() / get_supylabel() | Set/get the Figure-level y label |
| Figure.colorbar() | Add a colorbar |
| Figure.legend() | Add a global legend |
| Figure.text() | Add text at the Figure level |
| Figure.align_labels() | Align axis labels of all subplots |
| Figure.align_xlabels() | Align x-axis labels of all subplots |
| Figure.align_ylabels() | Align y-axis labels of all subplots |
| Figure.align_titles() | Align titles of all subplots |
| Figure.autofmt_xdate() | Automatically rotate date tick labels |
4. Figure Geometry
| Method | Description |
|---|---|
| Figure.set_size_inches() / get_size_inches() | Set/get the Figure size (in inches) |
| Figure.set_figheight() / get_figheight() | Set/get the Figure height |
| Figure.set_figwidth() / get_figwidth() | Set/get the Figure width |
| Figure.dpi | DPI resolution (property) |
| Figure.set_dpi() / get_dpi() | Set/get the DPI |
5. Subplot Layout
| Method | Description |
|---|---|
| Figure.subplots_adjust() | Manually adjust subplot spacing |
| Figure.set_layout_engine() | Set the layout engine ('constrained'/'compressed'/'none') |
| Figure.get_layout_engine() | Get the current layout engine |
| Figure.tight_layout() | Automatic tight layout (deprecated) |
| Figure.set_tight_layout() / get_tight_layout() | Set/get tight_layout (deprecated) |
| Figure.set_constrained_layout() / get_constrained_layout() | Set/get constrained_layout (deprecated) |
| Figure.set_constrained_layout_pads() / get_constrained_layout_pads() | Set/get constrained_layout margins (deprecated) |
6. Interaction
| Method | Description |
|---|---|
| Figure.ginput() | Get coordinate points via mouse clicks |
| Figure.waitforbuttonpress() | Wait for mouse or keyboard press |
| Figure.pick() | Trigger a pick event |
| Figure.add_axobserver() | Add an Axes observer |
7. Appearance Modification
| Method | Description |
|---|---|
| Figure.set_frameon() / get_frameon() | Set/get whether the Figure background is visible |
| Figure.set_linewidth() / get_linewidth() | Set/get the Figure frame line width |
| Figure.set_facecolor() / get_facecolor() | Set/get the Figure background color |
| Figure.set_edgecolor() / get_edgecolor() | Set/get the Figure frame color |
8. Adding and Getting Artists
| Method | Description |
|---|---|
| Figure.add_artist() | Add an arbitrary Artist |
| Figure.figimage() | Place an image at the Figure level |
| Figure.get_children() | Get all child Artists |
9. State Management
| Method | Description |
|---|---|
| Figure.clear() | Clear the Figure |
| Figure.gca() | Get the current Axes |
| Figure.sca() | Set the current Axes |
| Figure.show() | Show the Figure |
| Figure.draw() | Render the Figure |
| Figure.draw_artist() | Draw a single Artist |
| Figure.draw_without_rendering() | Compute layout without rendering (to obtain size information) |
| Figure.set_canvas() | Set the canvas |
| Figure.get_tightbbox() | Get the tight bounding box of the Figure |
| Figure.get_window_extent() | Get the Figure bounds in the display window |
10. Helper Functions
| Function | Description |
|---|---|
| figaspect() | Calculate the Figure size based on the specified aspect ratio |
SubFigure Object - Method List
SubFigure is a logical child Figure nested inside a parent Figure, with methods similar to Figure.
Adding Axes
| Method | Description |
|---|---|
| SubFigure.subplots() | Create a subplot grid |
| SubFigure.add_subplot() | Add a single subplot |
| SubFigure.add_axes() | Add an Axes at a specified position |
| SubFigure.subplot_mosaic() | Label-based layout subplots |
| SubFigure.add_gridspec() | Add a GridSpec |
| SubFigure.subfigures() | Create a deeper nested SubFigure |
| SubFigure.add_subfigure() | Add a single SubFigure |
| SubFigure.delaxes() | Remove Axes |
Annotations
| Method | Description |
|---|---|
| SubFigure.suptitle() / get_suptitle() | SubFigure suptitle |
| SubFigure.supxlabel() / get_supxlabel() | SubFigure x label |
| SubFigure.supylabel() / get_supylabel() | SubFigure y label |
| SubFigure.colorbar() | Colorbar |
| SubFigure.legend() | Legend |
| SubFigure.text() | Text |
| SubFigure.align_labels() | Align labels |
| SubFigure.align_xlabels() / align_ylabels() | Align x/y labels |
| SubFigure.align_titles() | Align titles |
Artist and Appearance
| Method | Description |
|---|---|
| SubFigure.add_artist() | Add an Artist |
| SubFigure.get_children() | Get child Artists |
| SubFigure.set_frameon() / get_frameon() | Set/get the frame |
| SubFigure.set_linewidth() / get_linewidth() | Set/get the line width |
| SubFigure.set_facecolor() / get_facecolor() | Set/get the background color |
| SubFigure.set_edgecolor() / get_edgecolor() | Set/get the frame color |
| SubFigure.set_dpi() / get_dpi() | Set/get the DPI |
Style Configuration Quick Reference
Built-in Style Sheets
Byplt.style.use('name')switching styles.
| Style name | Description |
|---|---|
| default | Default style, clean and neutral |
| ggplot | Mimics R ggplot2 style, gray background with white grid |
| seaborn-v0_8 | Modern style similar to the seaborn library |
| seaborn-v0_8-bright | seaborn bright palette |
| seaborn-v0_8-colorblind | seaborn colorblind-friendly palette |
| seaborn-v0_8-dark | seaborn dark style |
| seaborn-v0_8-dark-palette | seaborn deep palette |
| seaborn-v0_8-darkgrid | seaborn darkgrid style |
| seaborn-v0_8-deep | seaborn dark palette |
| seaborn-v0_8-muted | seaborn muted palette |
| seaborn-v0_8-notebook | seaborn notebook style |
| seaborn-v0_8-paper | seaborn paper style |
| seaborn-v0_8-pastel | seaborn pastel palette |
| seaborn-v0_8-poster | seaborn poster style |
| seaborn-v0_8-talk | seaborn talk style |
| seaborn-v0_8-ticks | seaborn ticks style |
| seaborn-v0_8-white | seaborn white style |
| seaborn-v0_8-whitegrid | seaborn whitegrid style |
| fivethirtyeight | Mimics FiveThirtyEight data journalism style |
| dark_background | Dark background, suitable for presentations and nighttime use |
| bmh | Bayesian Methods for Hackers style |
| grayscale | Grayscale style, suitable for black-and-white printing |
| classic | Matplotlib v1.x classic style |
| fast | Simplified style, faster rendering |
| Solarize_Light2 | Solarized light theme |
| tableau-colorblind10 | Tableau colorblind-friendly palette |
Common rcParams Configurations
| Parameters | Description | Example values |
|---|---|---|
| figure.figsize | Default figure size (inches) | [8, 6] |
| figure.dpi | Default resolution | 100 |
| figure.facecolor | Figure background color | 'white' |
| figure.edgecolor | Figure edge color | 'white' |
| font.size | Global font size | 12 |
| font.family | Font family | 'sans-serif' |
| font.sans-serif | Sans-serif font list | ['DejaVu Sans', ...] |
| axes.titlesize | Title font size | 'large' |
| axes.labelsize | Axis label font size | 'medium' |
| axes.grid | Whether grid is shown by default | False |
| axes.facecolor | Axes background color | 'white' |
| axes.spines.top | Whether to display the top spine | True |
| axes.spines.right | Whether to display the right spine | True |
| lines.linewidth | Default line width | 1.5 |
| lines.markersize | Default marker size | 6 |
| lines.linestyle | Default line style | '-' |
| legend.loc | Legend default location | 'best' |
| legend.fontsize | Legend font size | 'medium' |
| xtick.labelsize | x-axis tick label size | 'medium' |
| ytick.labelsize | y-axis tick label size | 'medium' |
| savefig.dpi | Default resolution for saving images | 'figure' |
| savefig.bbox | Bounding box mode when saving | None |
| image.cmap | Default colormap | 'viridis' |
| image.interpolation | Image interpolation method | 'antialiased' |
Colormap Quick Reference
| Category | Colormap name | Applicable scenarios |
|---|---|---|
| Perceptually uniform (Sequential) | viridis, plasma, inferno, magma, cividis | Continuous data, colorblind-friendly, recommended as top choice |
| Sequential (single-color gradient) | Greys, Purples, Blues, Greens, Oranges, Reds, YlOrBr, YlOrRd, OrRd, PuRd, RdPu, BuPu, GnBu, PuBu, YlGnBu, PuBuGn, BuGn, YlGn | Continuous data from low to high, single hue |
| Diverging | PiYG, PRGn, BrBG, PuOr, RdGy, RdBu, RdYlBu, RdYlGn, Spectral, coolwarm, bwr, seismic | Bidirectional data with a central reference point |
| Cyclic | twilight, twilight_shifted, hsv | Periodic data (e.g., angles, time) |
| Qualitative | Pastel1, Pastel2, Paired, Accent, Dark2, Set1, Set2, Set3, tab10, tab20, tab20b, tab20c | Discrete categorical data |
| Miscellaneous | flag, prism, ocean, gist_earth, terrain, gist_stern, gnuplot, gnuplot2, CMRmap, cubehelix, brg, gist_rainbow, rainbow, jet, turbo, nipy_spectral, gist_ncar | Special-purpose or visual effects |
'viridis'It is the default colormap since Matplotlib 2.0, with excellent perceptual uniformity and colorblind-friendly characteristics, and is the first choice for most scenarios. Avoid using'jet'and'rainbow', which have perceptual distortion issues.
Common Examples
Example 1: Basic Line Chart and Scatter Plot
Use the explicit Axes interface to create charts with multiple curves and annotations.
Example
import matplotlib.pyplot as plt
import numpy as np
# Generate data: 100 equally spaced points between 0 and 10
x = np.linspace(0, 10, 100)
y1 = np.sin(x) # Sine function
y2 = np.cos(x) # Cosine function
# Create Figure and Axes (explicit interface, recommended)
fig, ax = plt.subplots(figsize=(8, 4), layout='constrained')
# Plot two lines, set colors, line styles, and labels
ax.plot(x, y1, label='sin(x)', color='blue', linewidth=2)
ax.plot(x, y2, label='cos(x)', color='red', linestyle='--', linewidth=2)
# Add highlighted scatter points at the peak positions
peak_idx_sin = np.argmax(y1)
peak_idx_cos = np.argmax(y2)
ax.scatter(x[peak_idx_sin], y1[peak_idx_sin],
color='blue', s=100, zorder=5)
ax.scatter(x[peak_idx_cos], y2[peak_idx_cos],
color='red', s=100, zorder=5)
# Decorate: title, axis labels, legend, grid
ax.set_title('Sine and Cosine Functions', fontsize=14)
ax.set_xlabel('x (radians)')
ax.set_ylabel('Amplitude')
ax.legend(loc='upper right')
ax.grid(True, alpha=0.3)
plt.show()
print("example: plot displayed successfully")
import numpy as np
# Generate data: 100 equally spaced points between 0 and 10
x = np.linspace(0, 10, 100)
y1 = np.sin(x) # Sine function
y2 = np.cos(x) # Cosine function
# Create Figure and Axes (explicit interface, recommended)
fig, ax = plt.subplots(figsize=(8, 4), layout='constrained')
# Plot two lines, set colors, line styles, and labels
ax.plot(x, y1, label='sin(x)', color='blue', linewidth=2)
ax.plot(x, y2, label='cos(x)', color='red', linestyle='--', linewidth=2)
# Add highlighted scatter points at the peak positions
peak_idx_sin = np.argmax(y1)
peak_idx_cos = np.argmax(y2)
ax.scatter(x[peak_idx_sin], y1[peak_idx_sin],
color='blue', s=100, zorder=5)
ax.scatter(x[peak_idx_cos], y2[peak_idx_cos],
color='red', s=100, zorder=5)
# Decorate: title, axis labels, legend, grid
ax.set_title('Sine and Cosine Functions', fontsize=14)
ax.set_xlabel('x (radians)')
ax.set_ylabel('Amplitude')
ax.legend(loc='upper right')
ax.grid(True, alpha=0.3)
plt.show()
print("example: plot displayed successfully")
Example 2: Multiple Subplot Layout (2x2)
Demonstrate how to create different types of subplots in one Figure.
Example
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 2 * np.pi, 100)
# Create a 2x2 subplot (object-oriented interface)
fig, axes = plt.subplots(2, 2, figsize=(10, 8), layout='constrained')
fig.suptitle('Multi-panel EXAMPLE Demo', fontsize=16)
# Subplot (0,0): line plot
axes[0, 0].plot(x, np.sin(x), color='tab:blue', linewidth=2)
axes[0, 0].set_title('Line Plot')
axes[0, 0].set_ylabel('sin(x)')
# Subplot (0,1): scatter plot
np.random.seed(42)
colors = np.random.rand(50)
sizes = np.random.rand(50) * 200
axes[0, 1].scatter(np.random.rand(50), np.random.rand(50),
c=colors, s=sizes, alpha=0.6, cmap='viridis')
axes[0, 1].set_title('Scatter Plot')
# Subplot (1,0): bar chart (with value labels)
categories = ['A', 'B', 'C', 'D', 'E']
values = [23, 45, 56, 78, 32]
bars = axes[1, 0].bar(categories, values,
color='tab:green', edgecolor='white')
axes[1, 0].bar_label(bars) # Display values on the bars
axes[1, 0].set_title('Bar Chart')
# Subplot (1,1): histogram
data = np.random.randn(1000)
axes[1, 1].hist(data, bins=30, color='tab:orange',
edgecolor='white', alpha=0.8)
axes[1, 1].axvline(x=0, color='red', linestyle='--', linewidth=2)
axes[1, 1].set_title('Histogram')
plt.show()
import numpy as np
x = np.linspace(0, 2 * np.pi, 100)
# Create a 2x2 subplot (object-oriented interface)
fig, axes = plt.subplots(2, 2, figsize=(10, 8), layout='constrained')
fig.suptitle('Multi-panel EXAMPLE Demo', fontsize=16)
# Subplot (0,0): line plot
axes[0, 0].plot(x, np.sin(x), color='tab:blue', linewidth=2)
axes[0, 0].set_title('Line Plot')
axes[0, 0].set_ylabel('sin(x)')
# Subplot (0,1): scatter plot
np.random.seed(42)
colors = np.random.rand(50)
sizes = np.random.rand(50) * 200
axes[0, 1].scatter(np.random.rand(50), np.random.rand(50),
c=colors, s=sizes, alpha=0.6, cmap='viridis')
axes[0, 1].set_title('Scatter Plot')
# Subplot (1,0): bar chart (with value labels)
categories = ['A', 'B', 'C', 'D', 'E']
values = [23, 45, 56, 78, 32]
bars = axes[1, 0].bar(categories, values,
color='tab:green', edgecolor='white')
axes[1, 0].bar_label(bars) # Display values on the bars
axes[1, 0].set_title('Bar Chart')
# Subplot (1,1): histogram
data = np.random.randn(1000)
axes[1, 1].hist(data, bins=30, color='tab:orange',
edgecolor='white', alpha=0.8)
axes[1, 1].axvline(x=0, color='red', linestyle='--', linewidth=2)
axes[1, 1].set_title('Histogram')
plt.show()
Example 3: Heatmap vs Contour Plot Comparison
Demonstrate visualizing 2D data in the same Figure using imshow and contourf.
Example
import matplotlib.pyplot as plt
import numpy as np
# Create 2D grid data
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y) # 2D function values
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5),
layout='constrained')
# Left panel: heatmap (imshow)
im = ax1.imshow(Z, extent=[-3, 3, -3, 3], origin='lower',
cmap='viridis', aspect='auto')
ax1.set_title('Heatmap (imshow)', fontsize=14)
ax1.set_xlabel('X')
ax1.set_ylabel('Y')
fig.colorbar(im, ax=ax1, label='Amplitude', shrink=0.8)
# Right panel: filled contour (contourf)
contour = ax2.contourf(X, Y, Z, levels=15, cmap='RdYlBu')
# Overlay contour boundaries
ax2.contour(X, Y, Z, levels=15, colors='black', linewidths=0.5)
ax2.set_title('Filled Contour (contourf)', fontsize=14)
ax2.set_xlabel('X')
ax2.set_ylabel('Y')
fig.colorbar(contour, ax=ax2, label='Amplitude', shrink=0.8)
plt.show()
import numpy as np
# Create 2D grid data
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y) # 2D function values
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5),
layout='constrained')
# Left panel: heatmap (imshow)
im = ax1.imshow(Z, extent=[-3, 3, -3, 3], origin='lower',
cmap='viridis', aspect='auto')
ax1.set_title('Heatmap (imshow)', fontsize=14)
ax1.set_xlabel('X')
ax1.set_ylabel('Y')
fig.colorbar(im, ax=ax1, label='Amplitude', shrink=0.8)
# Right panel: filled contour (contourf)
contour = ax2.contourf(X, Y, Z, levels=15, cmap='RdYlBu')
# Overlay contour boundaries
ax2.contour(X, Y, Z, levels=15, colors='black', linewidths=0.5)
ax2.set_title('Filled Contour (contourf)', fontsize=14)
ax2.set_xlabel('X')
ax2.set_ylabel('Y')
fig.colorbar(contour, ax=ax2, label='Amplitude', shrink=0.8)
plt.show()
Example 4: Style Switching and Saving Figures
Use style sheets and rcParams to customize appearance, and save high-quality figures.
Example
import matplotlib.pyplot as plt
import numpy as np
# Use the ggplot style, then fine-tune some parameters
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = [8, 6]
plt.rcParams['font.size'] = 12
x = np.linspace(0, 10, 50)
y1 = np.exp(-x/3) * np.sin(2 * x) # Damped sine wave
y2 = np.exp(-x/3) * np.cos(2 * x) # Damped cosine wave
fig, ax = plt.subplots()
ax.plot(x, y1, 'o-', label='Damped sin', markersize=6)
ax.plot(x, y2, 's--', label='Damped cos', markersize=6)
# Annotate the first peak
peak_idx = np.argmax(y1)
ax.annotate(f'Peak: {y1[peak_idx]:.2f}',
xy=(x[peak_idx], y1[peak_idx]),
xytext=(x[peak_idx] + 1.5, y1[peak_idx] + 0.15),
arrowprops=dict(arrowstyle='->', color='gray'),
fontsize=10)
ax.set_title('Damped Oscillation', fontsize=16)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.legend()
ax.grid(True)
# Save as a 300 DPI high-quality PNG
fig.savefig('example_damped_oscillation.png',
dpi=300, bbox_inches='tight')
print("example: figure saved successfully")
plt.show()
import numpy as np
# Use the ggplot style, then fine-tune some parameters
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = [8, 6]
plt.rcParams['font.size'] = 12
x = np.linspace(0, 10, 50)
y1 = np.exp(-x/3) * np.sin(2 * x) # Damped sine wave
y2 = np.exp(-x/3) * np.cos(2 * x) # Damped cosine wave
fig, ax = plt.subplots()
ax.plot(x, y1, 'o-', label='Damped sin', markersize=6)
ax.plot(x, y2, 's--', label='Damped cos', markersize=6)
# Annotate the first peak
peak_idx = np.argmax(y1)
ax.annotate(f'Peak: {y1[peak_idx]:.2f}',
xy=(x[peak_idx], y1[peak_idx]),
xytext=(x[peak_idx] + 1.5, y1[peak_idx] + 0.15),
arrowprops=dict(arrowstyle='->', color='gray'),
fontsize=10)
ax.set_title('Damped Oscillation', fontsize=16)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.legend()
ax.grid(True)
# Save as a 300 DPI high-quality PNG
fig.savefig('example_damped_oscillation.png',
dpi=300, bbox_inches='tight')
print("example: figure saved successfully")
plt.show()
Example 5: Complex Mosaic Layout
Use subplot_mosaic to create non-uniform subplot arrangements.
Example
import matplotlib.pyplot as plt
import numpy as np
# Define layout with a mosaic string:
# 'A' spans the full top row, 'B' and 'C' are at bottom-left and middle-left, 'D' occupies the bottom-right vertically
layout = """
A A A
B C D
"""
fig, axes = plt.subplot_mosaic(layout, figsize=(10, 6),
layout='constrained')
fig.suptitle('Complex Mosaic Layout (EXAMPLE)', fontsize=16)
# Panel A: line plot (spans the top)
x = np.linspace(0, 10, 100)
axes['A'].plot(x, np.sin(x), label='sin(x)')
axes['A'].plot(x, np.cos(x), label='cos(x)')
axes['A'].set_title('Panel A: Line Plot')
axes['A'].legend()
# Panel B: pie chart
sizes = [30, 25, 20, 15, 10]
labels = ['Python', 'Java', 'C++', 'Rust', 'Go']
axes['B'].pie(sizes, labels=labels, autopct='%1.1f%%',
startangle=90)
axes['B'].set_title('Panel B: Pie Chart')
# Panel C: bar chart
axes['C'].bar(['X', 'Y', 'Z'], [10, 25, 15],
color=['#ff6b6b', '#4ecdc4', '#45b7d1'])
axes['C'].set_title('Panel C: Bar Chart')
# Panel D: heatmap
matrix = np.random.rand(5, 5)
im = axes['D'].imshow(matrix, cmap='Blues', aspect='auto')
axes['D'].set_title('Panel D: Heatmap')
fig.colorbar(im, ax=axes['D'])
plt.show()
import numpy as np
# Define layout with a mosaic string:
# 'A' spans the full top row, 'B' and 'C' are at bottom-left and middle-left, 'D' occupies the bottom-right vertically
layout = """
A A A
B C D
"""
fig, axes = plt.subplot_mosaic(layout, figsize=(10, 6),
layout='constrained')
fig.suptitle('Complex Mosaic Layout (EXAMPLE)', fontsize=16)
# Panel A: line plot (spans the top)
x = np.linspace(0, 10, 100)
axes['A'].plot(x, np.sin(x), label='sin(x)')
axes['A'].plot(x, np.cos(x), label='cos(x)')
axes['A'].set_title('Panel A: Line Plot')
axes['A'].legend()
# Panel B: pie chart
sizes = [30, 25, 20, 15, 10]
labels = ['Python', 'Java', 'C++', 'Rust', 'Go']
axes['B'].pie(sizes, labels=labels, autopct='%1.1f%%',
startangle=90)
axes['B'].set_title('Panel B: Pie Chart')
# Panel C: bar chart
axes['C'].bar(['X', 'Y', 'Z'], [10, 25, 15],
color=['#ff6b6b', '#4ecdc4', '#45b7d1'])
axes['C'].set_title('Panel C: Bar Chart')
# Panel D: heatmap
matrix = np.random.rand(5, 5)
im = axes['D'].imshow(matrix, cmap='Blues', aspect='auto')
axes['D'].set_title('Panel D: Heatmap')
fig.colorbar(im, ax=axes['D'])
plt.show()
Example 6: Twin Y-Axes and Inset Subplots
Demonstrate the usage of secondary_axis, twinx, and inset_axes.
Example
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(8, 5), layout='constrained')
x = np.linspace(0, 10, 100)
# Main plot: draw two curves with different dimensions
ax.plot(x, np.sin(x), 'b-', label='sin(x) [amplitude]')
ax.set_xlabel('x (radians)')
ax.set_ylabel('sin(x)', color='blue')
ax.tick_params(axis='y', labelcolor='blue')
# Create twin y-axes (shared x-axis)
ax2 = ax.twinx()
ax2.plot(x, np.exp(x/5), 'r--', label='exp(x/5) [growth]')
ax2.set_ylabel('exp(x/5)', color='red')
ax2.tick_params(axis='y', labelcolor='red')
# Add an inset subplot in ax (zoom into a local region)
axins = ax.inset_axes([0.15, 0.5, 0.3, 0.35])
axins.plot(x, np.sin(x), 'b-')
axins.set_xlim(3, 5)
axins.set_ylim(-1.2, 1.2)
axins.set_title('Zoomed Region')
axins.grid(True, alpha=0.3)
# Mark the inset region on the main plot
ax.indicate_inset_zoom(axins, edgecolor='gray')
ax.set_title('Dual Y-Axes with Inset Zoom', fontsize=14)
plt.show()
import numpy as np
fig, ax = plt.subplots(figsize=(8, 5), layout='constrained')
x = np.linspace(0, 10, 100)
# Main plot: draw two curves with different dimensions
ax.plot(x, np.sin(x), 'b-', label='sin(x) [amplitude]')
ax.set_xlabel('x (radians)')
ax.set_ylabel('sin(x)', color='blue')
ax.tick_params(axis='y', labelcolor='blue')
# Create twin y-axes (shared x-axis)
ax2 = ax.twinx()
ax2.plot(x, np.exp(x/5), 'r--', label='exp(x/5) [growth]')
ax2.set_ylabel('exp(x/5)', color='red')
ax2.tick_params(axis='y', labelcolor='red')
# Add an inset subplot in ax (zoom into a local region)
axins = ax.inset_axes([0.15, 0.5, 0.3, 0.35])
axins.plot(x, np.sin(x), 'b-')
axins.set_xlim(3, 5)
axins.set_ylim(-1.2, 1.2)
axins.set_title('Zoomed Region')
axins.grid(True, alpha=0.3)
# Mark the inset region on the main plot
ax.indicate_inset_zoom(axins, edgecolor='gray')
ax.set_title('Dual Y-Axes with Inset Zoom', fontsize=14)
plt.show()
Common Issues
Charts Not Displaying
Confirm whether you have calledplt.show()。
Under non-interactive backends (such as Agg), it must be called explicitlyshow()。
In Jupyter Notebook, use%matplotlib inlinemagic command to ensure charts are displayed inline
Too Many Ticks or Wrong Order
A common cause is passing a list of strings as data (instead of numeric or datetime values)
Matplotlib treats a list of strings as categorical variables, with one tick per unique value, arranged in order of appearance
Solution: convert the strings to numeric values, such asnp.asarray(data, dtype='float')ornp.asarray(data, dtype='datetime64[s]')。
Chinese Characters Display as Boxes
Matplotlib's default font (DejaVu Sans) does not contain Chinese glyphs; you need to specify a font that supports Chinese
Setplt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', ...]And ensure that the corresponding font is installed on the system
Labels Overlapping or Truncated
It is recommended to use when creating a Figurelayout='constrained'orlayout='compressed'parameter, which automatically handles overlapping of labels, titles, and legends
You can also usefig.tight_layout()orfig.subplots_adjust()manually adjust
Difference Between pcolor and pcolormesh
pcolor()Creates a PolyCollection, rendering is slower but more precise
pcolormesh()Creates a QuadMesh, rendering is faster, and it is the recommended choice for most scenarios
For very large grids,pcolorfast()using imshow to plot is fastest but has the lowest precision
Related Resources
- Matplotlib official website: matplotlib.org
- Official gallery (Gallery): matplotlib.org/stable/gallery/index.html
- Full API reference: matplotlib.org/stable/api/index.html
- Installation guide: matplotlib.org/stable/install/index.html
- Contribution guide: matplotlib.org/stable/devel/index.html