Programming Articles - Page 1200 of 3363

To plot a circle in matplotlib, we can take the following steps −Create a new figure or activate an existing figure using figure() method.Add a subplot arrangement to the current axis.Create a true circle at a center using Circle class.Add a patch to the current axis.Set limits of the x and y axes.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_subplot() circle1 = patches.Circle((0.2, 0.2), radius=0.5, color='green') ax.add_patch(circle1) ax.axis('equal') plt.show()OutputRead More

To add apha to an existing matplotlib colormap, we can take the following steps −Create data with a 4×4 dimension array using numpy.Get the colormap using plt.cm.RdBU.Create a new colormap using numpy.Set alpha value to the new colormap.Generate a colormap object using the list of colors.Create a new figure or activate an existing figure using figure() method.Add a subplot to the current figure, nrows=1, ncols=2 at index=1.Create a pseudocolor plot with a non-regular rectangular grid using pcolormesh() method.Create a colorbar for scalar mappable instance.Repeat steps 7 to 9, at index 2.Use tight_layout() to adjust the padding between and around the subplots.To ... Read More

To annotate a heatmap with text in matplotlib, we can take the following steps −Create random data with 4×4 dimension array.Create a pseudocolor plot with a non-regular rectangular grid, using pcolor() method.To put text in the pixels, we can use text() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(4, 4) heatmap = plt.pcolor(data, cmap="PuBuGn_r") for y in range(data.shape[0]):    for x in range(data.shape[1]):       plt.text(x + 0.5, y + 0.5, '%.4f' % data[y, x],          horizontalalignment='center',         ... Read More

To make superscript or subscript text in matplotlib, use LaTeX representation.StepsCreate x and y data points using numpy.Plot x and y data point using plot() method.Put the title with LateX representation using title() method.Use xlabel and ylabel methods to set the label of the axes.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, 10) y = 2**x plt.plot(x, y) plt.title('$Y=2^{X}$') plt.xlabel('$X_{data}$') plt.ylabel('$Y_{data}$') plt.show()Output

To change the spacing of dashes in a dashed line in matplotlib, we can take the following steps −Create data points x and y using numpy.Initialize two variables space and dash_len with value 3.Plot x and y using plot() method, with line style '--', dashes tuple stores the property of dashed line.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, 100) y = np.sin(x) space = 3 dash_len = 3 plt.plot(x, y, c='red', linestyle='--', dashes=(dash_len, space), lw=5) plt.show()OutputRead More

To set the ticks on a fixed position in matplotlib, we can take the following steps −Create a figure and add a set of subplots.To set the ticks on a fixed position, create two lists with some values.Use set_yticks and set_xticks methods to set the ticks on the axes.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() xtick_loc = [0.20, 0.75, 0.30] ytick_loc = [0.12, 0.80, 0.76] ax.set_xticks(xtick_loc) ax.set_yticks(ytick_loc) plt.show()Output

To plot a step function with matplotlib in Python, we can take the following steps −Create data points for x and y.Make a step plot using step() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([1, 3, 4, 5, 7]) y = np.array([1, 9, 16, 25, 49]) plt.step(x, y, 'r*') plt.show()Output

To rotate xtick labels through 90 degrees, we can take the following steps −Make a list (x) of numbers.Add a subplot to the current figure.Set ticks on X-axis.Set xtick labels and use rotate=90 as the arguments in the method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [1, 2, 3, 4] ax1 = plt.subplot() ax1.set_xticks(x) ax1.set_xticklabels(["one", "two", "three", "four"], rotation=90) plt.show()Output

To make a simple 3D line with matplotlib, we can take the following steps −Create a new figure or activate an existing figure.Add axes to the figure as part of a subplot arrangement.Create data points for theta, z, r, x and y using numpy.Plot x, y and z using plot() method.Place a legend on the figure using 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 fig = plt.figure() ax = fig.add_subplot(projection='3d') theta = np.linspace(-4 * np.pi, 4 * np.pi, 100) z = np.linspace(-2, 2, 100) r ... Read More

To plot multiple histograms on same plot with Seaborn, we can take the following steps −Create two lists (x and y).Create a figure and add a set of two subplots.Iterate a list consisting of x and y.Plot a histogram with histplot() method using the data in the list (step 3).Limit the X-axis range from 0 to 10.To display the figure, use show() method.Exampleimport seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [1, 5, 1, 4, 2] y = [7, 5, 6, 4, 5] fig, ax = plt.subplots() for a in [x, y]: ... Read More