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Matplotlib Articles
Page 43 of 91
How to change the legend fontname in Matplotlib?
To change the legend fontname in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x data points using numpy.Plot x, sin(x) and cos(x) using plot() method.Use legend() method to place the legend.Iterate legend.get_texts() and update the legend fontname.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-5, 5, 100) plt.plot(x, np.sin(x), label="$y=sin(x)$") plt.plot(x, np.cos(x), label="$y=cos(x)$") legend = plt.legend(loc='upper right') i = 1 for t in legend.get_texts(): ...
Read MoreAdding units to heatmap annotation in Seaborn
To add units to a heatmap annotation in Seaborn, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a 5×5 dimension matrix using numpy.Plot rectangular data as a color-encoded matrix.Annotate heatmap value with %age unit.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import seaborn as sns import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(5, 5) ax = sns.heatmap(data, annot=True, fmt='.1f', square=1, linewidth=1.) for t in ax.texts: t.set_text(t.get_text() + " %") plt.show()Output
Read MoreHow to color a Matplotlib scatterplot using a continuous value?
To color a matplotlib scatterplot using continuous value, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and z random data points using numpy.Create a figure and a set of subplots.Create a scatter plot.Draw a colorbar in an existing axes, with scatter points scalar mappable instance.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x, y, z = np.random.rand(3, 50) f, ax = plt.subplots() points = ax.scatter(x, y, c=z, s=50, cmap="plasma") f.colorbar(points) ...
Read MoreText alignment in a Matplotlib legend
To make text alignment in a matplotlib legend, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x data points using numpy.Plot x, sin(x) and cos(x) using plot() method.Place legend using legend() method and initialize a method.Iterate the legend.get_texts() method to set the horizontal alignment.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-5, 5, 100) plt.plot(x, np.sin(x), label="$y=sin(x)$") plt.plot(x, np.cos(x), label="$y=cos(x)$") legend = plt.legend(loc='upper right') for t in ...
Read MorePlot Matplotlib 3D plot_surface with contour plot projection
To plot 3d plot_surface with contour plot projection, we can use plot_surface() and contourf() methods.StepsSet the figure size and adjust the padding between and around the subplots.Create x, y, X, Y and Z data points using numpy.Create a new figure or activate an existing figure using figure() method.Add an '~.axes.Axes' to the figure as part of a subplot arrangement, with 3D projection.Use plot_surface() method to create a surface plot.Create a 3D filled contour plotm using contourf() method.Trurn off the axes.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] ...
Read MoreHow to add Matplotlib Colorbar Ticks?
To add ticks to the colorbar, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and z data points using numpy.Use imshow() method to display the data as an image, i.e., on a 2D regular raster.Create ticks using numpy in the range of min and max of z.Create a colorbar for a ScalarMappable instance, *mappable*, with ticks=ticks.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x, y = np.mgrid[-1:1:100j, -1:1:100j] z = (x + ...
Read MoreHow to change the font properties of a Matplotlib colorbar label?
To change the font properties of a matplotlib colorbar label, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and z data points using numpy.Use imshow() method to display the data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance, *mappable*.Using colorbar axes, set the font properties such that the label is bold.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x, y = np.mgrid[-1:1:100j, -1:1:100j] z = ...
Read MoreHow can I draw a scatter trend line using Matplotlib?
To draw a scatter trend line using matplotlib, we can use polyfit() and poly1d() methods to get the trend line points.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create a figure and a set of subplots.Plot x and y data points using numpy.Find the trend line data points using polyfit() and poly1d() method.Plot x and p(x) data points using plot() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.random.rand(100) y ...
Read MoreHow to plot hexbin histogram in Matplotlib?
To plot a hexbin histogram in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create a figure and a set of subplots.Plot x and y using hexbin() method.Set the title of the plot.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = 2 * np.random.randn(5000) y = x + np.random.randn(5000) fig, ax = plt.subplots() _ = ax.hexbin(x[::10], y[::10], gridsize=20, cmap='plasma') ax.set_title('Hexbin Histogram') ...
Read MoreHow to make joint bivariate distributions in Matplotlib?
To make joint bivariate distributions in matplotlib, we can use the scatter method.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create a figure and a set of subplots.Plot x and y using scatter() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = 2 * np.random.randn(5000) y = x + np.random.randn(5000) fig, ax = plt.subplots() _ = ax.scatter(x, y, alpha=0.08, cmap="copper", c=x) plt.show()Output
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