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Matplotlib Articles
Page 78 of 91
Equivalent to matlab's imagesc in Matplotlib
To make equivalent imagesc, we can use extent [left, right, bottom, top].StepsCreate random data using numpy.Display the data as an image, i.e., on a 2D regular raster, with data and extent [−1, 1, −1, 1] arguments.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(4, 4) plt.imshow(data, extent=[-1, 1, -1, 1]) plt.show()Output
Read MoreShading an area between two points in a Matplotlib plot
To shade an area between two points in matplotlib, we can take the following steps−Create x and y data points using numpy.Plot x and y data points, with color=red and linewidth=2.To shade an area parallel to X-axis, initialize two variables, y1 and y2.To add horizontal span across the axes, use axhspan() method with y1, y2, green as shade color, and alpha for transprency of the shade.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(0, 20, 500) y = np.cos(3*x) + np.sin(2*x) plt.plot(x, y, c='red', lw=2) ...
Read MoreCreating a graph with date and time in axis labels with Matplotlib
To create a graph with date and time in axis labels, we can take the following steps−Create a figure and add a set of subplots.Create x and y data points using numpy.Set date formatter for X-axis.Plot x and y using plot() method.Set the ticks of X-axis.Set the date-time tick labels for X-axis, with some rotation.Make the plot tight layout using plt.tight_layout() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, dates import datetime import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x = np.array([datetime.datetime(2021, 1, 1, i, 0) for i ...
Read MoreHow to plot 1D data at a given Y-value with PyLab using Matplotlib?
To plot 1D data at a given Y-value with pyplot, we can take the following steps−Initialize y value.Create x and y data points using numpy. zeros_like helps to return an array of zeros with the same shape and type as a given array and add y-value for y data points.Plot x and y with linestyle=dotted, color=red, and linewidth=5.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True y_value = 1 x = np.arange(10) y = np.zeros_like(x) + y_value plt.plot(x, y, ls='dotted', c='red', lw=5) plt.show()Output
Read MoreMatplotlib savefig with a legend outside the plot
To save a file with legend outside the plot, we can take the following steps −Create x data points using numpy.Plot y=sin(x) curve using plot() method, with color=red, marker="v" and label y=sin(x).Plot y=cos(x), curve using plot() method, with color=green, marker="x" and label y=cos(x).To place the legend outside the plot, use bbox_to_anchor(.45, 1.15) and location="upper center".To save the figure, use savefig() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-2, 2, 100) plt.plot(x, np.sin(x), c="red", marker="v", label="y=sin(x)") plt.plot(x, np.cos(x), c="green", marker="x", label="y=cos(x)") plt.legend(bbox_to_anchor=(.45, 1.15), loc="upper center") plt.savefig("legend_outside.png")OutputWhen we execute this code, it will ...
Read MoreHow to change the scale of an existing table in Matplotlib?
To change scale of a table, we can use the scale() method. Steps −Create a figure and a set of subplots, nrows=1 and ncols=1.Create a random data using numpy.Make columns value.Make the axis tight and off.Initialize a variable fontsize to change the fontsize.To set the fontsize of the table and to scale the table, we can use 1.5 and 1.5.To display the figure, use the 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 fig, axs = plt.subplots(1, 1) data = np.random.random((10, 3)) columns = ("Column I", "Column II", "Column III") axs.axis('tight') ...
Read MorePlot a black-and-white binary map in Matplotlib
To plot black-and-white binary map in matplotlib, we can create and add two subplots to the current figure using subplot() method, where nrows=1 and ncols=2. To display the data as a binary map, we can use greys colormap in imshow() method.StepsCreate data using numpyAdd two sublots, nrows=1 and ncols=2. Consider index 1.To show colored image, use imshow() method.Add title to the colored map.Add a second subplot at index 2.To show the binary map, use show() method with Greys colormap.To adjust the padding between and around the subplots, use tight_layout() method.To display the figure, use show() method.Exampleimport numpy as np from ...
Read MoreHow do you create a legend for a contour plot in Matplotlib?
To create a legend for a contour plot in matplotlib, we can take the following steps−Create x, y and z data points to plot the contour function.To create a 3D filled contour plot, we can use contourf() method with x, y, z and different levels.Make a list of rectangle with the returned contour signature collection and set face colorNow, place the legend in the plot using proxy (of step 3) and different labels.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x, y = np.meshgrid(np.arange(10), np.arange(10)) ...
Read MoreOverlapping Y-axis tick label and X-axis tick label in Matplotlib
To reduce the chances of overlapping between x and y tick labels in matplotlib, we can take the following steps −Create x and y data points using numpy.Add a subplot to the current figure at index 1 (nrows=1 and ncols=2).Set x and y margins to 0.Plot x and y data points and add a title to this subplot, i.e., "Overlapping".Add a subplot to the current figure at index 2 (nrows=1 and ncols=2).Set x and y margins to 0.Plot x and y data points and add a title to this subplot, i.e., "Non Overlapping".The objective of MaxNLocator and prune ="lower" is that the smallest tick will be removed.To display the figure, ...
Read MoreStyling a part of label in legend in Matplotlib
To style a part of label in legend, we can take the following steps −Create data point for x using numpy.Plot a sine curve using np.sin(x) with a text label.Plot a cosine curve using np.cos(x) with a text label.To place the legend on the plot, use legend() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-1, 1, 10) plt.plot(x, np.sin(x), label="This is $\it{a\ sine\ curve}$") plt.plot(x, np.cos(x), label="This is $\bf{a\ cosine\ curve}$") plt.legend(loc='lower right') plt.show()Output
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