The call to sns.set() imposes the default Seaborn theme to all Matplotlib plots as well as those using Seaborn. Sns.heatmap(flights, annot=True, fmt="d", linewidths=.5, ax=ax) # Draw a heatmap with the numeric values in each cell # Load the example flights dataset and conver to long-formįlights_long = sns.load_dataset("flights")įlights = flights_long.pivot("month", "year", "passengers") ![]() Many other statistical plots are available including boxplots, violin plots, distribution plots, and so forth. Sns.relplot(x="timepoint", y="signal", col="region", This example uses a built-in demo dataset. Seaborn 0.9 or later is needed for the “relationship” plot example below. Seaborn can be used alone if its defaults are satisfactory, or plots can be enhanced with direct calls to Matplotlib functions. Seaborn is a package built upon Matplotlib that is targeted to statistical graphics. In the contour plot example, change contour to contourf and observe the difference. ![]() These were all taken from the Matplotlib gallery. Type into your choice of Spyder’s interpreter pane or a JupyterLab cell the example plotting codes we have seen so far. For example, NumPy has modified its default style, but the older one (shown in some of our illustrations) is available as “classic.” Matplotlib can also be styled to imitate the R package ggplot. Recent versions of Matplotlib can apply style sheets to change the overall appearance of plots. # Add a color bar which maps values to colors. Surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,Īx.t_major_locator(LinearLocator(10))Īx.t_major_formatter(FormatStrFormatter('%.02f')) # This import registers the 3D projection, but is otherwise unused.įrom mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused importįrom matplotlib.ticker import LinearLocator, FormatStrFormatter Surface plots require the mplot3d package and some additional commands to set views and sometimes lighting. Notice how NumPy array operations are used to compute the function values from the meshgrid arrays. The meshgrid function takes two rank-1 arrays and returns two rank-2 arrays, with each point labeled with both x and y values. Higher-Dimensional Plotsįor higher-dimensional plots we can use contour, contourf, surface, and others. These are commonly used with Pandas, and Pandas can access them directly, as we will see. Matplotlib can also make histograms, pie charts, and so forth. Interpolation='nearest', cmap='gray', aspect='auto') ![]() # Displaying the starting points with blue symbols.Īx3.plot(seed_points, seed_points, 'bo')Īx4.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5, Strm = ax3.streamplot(X, Y, U, V, color=U, linewidth=2,Ĭmap='autumn', start_points=seed_points.T)Īx3.set_title('Controlling Starting Points') # Controlling the starting points of the streamlines Strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='autumn')Īx2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw) Gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=)Īx0.streamplot(X, Y, U, V, density=) The following demonstrates streamlines for vector fields, such as fluid flows. Many other options are available for annotations, legends, and so forth. We can place more sophisticated labeling or multiple plots on a graph with subplot import numpy as np This is a scatter plot with points randomly placed according to a normal distribution. Let us write a more sophisticated example. Most of our sample scripts in this section are taken directly from the Matplotlib
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