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To plot a single line that continuously changes color, we can take the following steps−Set the figure size and adjust the padding between and around the subplots.Create random x and y data points using numpy.Create a figure and a set of subplots.Iterate the index in the range of 1 to 100.Plot x and y data points with random color in a loop.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np import random plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(1, 10, 100) y = np.sin(x) fig, ax = plt.subplots() for ... Read More
To set the Matplotlib title in bold while using "Times New Roman", we can use fontweight="bold".StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Create x and y data points using numpy.Plot x and y data points using scatter() method.Set the title of the plot using fontname="Times New Roman" and fontweight="bold"To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, font_manager as fm plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x = np.random.rand(100) y = np.random.rand(100) ax.scatter(x, y, ... Read More
To plot a 3D surface from x, y and z scatter data in Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure using figure() method.Add an axes to the figure as part of a subplot arrangement.Create x, y, X, Y and Z data points using numpy.Plot x, y and z data points using plot_surface() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ... Read More
To get the (x, y) values of a line that is plotted by a contour plot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a 3D contour plot using contour() method.Get the contour plot collections and get the paths.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True m = [[3, 2, 1, 0], [2, 4, 1, 0], [2, 4, 1, 3], [4, 3, 1, 3]] cs = plt.contour([3, 4, 2, 1], [5, 1, 2, 3], m) p1 = cs.collections[0].get_paths() for item in p1: print(item.vertices) plt.show()Output
To animate a time-ordered sequence of Matplotlib plots, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure.Add an axes to the figure as part of a subplot arrangement.Return the first recurrence after the given datetime instance using after() method.Write an animate() method to animate. Display the data as an image, i.e., on a 2D regular raster.To display the figure, use show() method.Exampleimport numpy as np import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ... Read More
To fill the area under a curve in Matplotlib python on log scale, we can take the following steps−Set the figure size and adjust the padding between and around the subplots.Create x, y1 and y2 data points using numpy.Plot x, y1 and y2 data points using plot() method.Fill the area between the two curves.Set the scale of the axes.Place a legend on 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 = np.linspace(-1, 1, 100) y1 = np.sin(x) y2 = np.cos(x) ... Read More
To force errorbars to render last with matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure using figure() method.Get the current axis using gca() method.Plot the list of linesPlot y versus x as lines and/or markers with attached errorbars.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = plt.gca() [ax.plot(np.random.rand(10)) for j in range(10)] ax.errorbar(range(10), np.random.rand(10), yerr=.3 * np.random.rand(10)) plt.show()OutputRead More
Matplotlib provides a number of colormaps, and others can be added using :func:'~matplotlib.cm.register_cmap'. This function documents the built-in colormaps, and will also return a list of all registered colormaps, if called.Examplefrom matplotlib import pyplot as plt cmaps = plt.colormaps() print("Possible color maps are: ") for item in cmaps: print(item)OutputAccent Accent_r Blues ... ... ... viridis_r winter winter_r
To plot multiple time-series data frames into a single plot using Pandas, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a Pandas data frame with time series.Set the time series index for plot.Plot rupees and dollor on the plot.To display the figure, use show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt, dates plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame(dict(date=list(pd.date_range("2021-01-01", periods=10)), rupees=np.linspace(1, 10, 10), dollar=np.linspace(10, 20, 10))) df.set_index(pd.to_datetime(df.date), drop=True).plot() df = df.set_index(pd.to_datetime(df.date), drop=True) df.rupees.plot(grid=True, label="rupees", legend=True) df.dollar.plot(secondary_y=True, ... Read More
To make several legend keys to the same entry in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Plot line1 and line2 using plot() method.Use legend() method to place a legend over the plot with numpoints=1To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib.legend_handler import HandlerTuple plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True p1, = plt.plot([1, 2.5, 3], 'r-d') p2, = plt.plot([3, 2, 1], 'k-o') l = plt.legend([(p1, p2)], ['Two keys'], numpoints=1, handler_map={tuple: andlerTuple(ndivide=None)}) plt.show()OutputRead More