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Matplotlib Articles
Page 38 of 91
How to plot categorical variables in Matplotlib?
To plot categorical variables in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a dictionary with some details.Extract the keys and values from the dictionary (Step 2).Create a figure and a set of subplots.Plot bar, scatter and plot with names and values data.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 data = {'apple': 10, 'orange': 15, 'lemon': 5} names = list(data.keys()) values = list(data.values()) fig, axs = plt.subplots(1, 3) axs[0].bar(names, values) axs[1].scatter(names, values) axs[2].plot(names, values) ...
Read MorePlot curves in fivethirtyeight stylesheet in Matplotlib
To use fivethirtyeight stylesheet, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.To use fivethirtyeight, we can use plt.style.use() method.Create x data points using numpy.Create a figure and a set of subplots using subplots() method.Plot three curves using plot() method.Set the title of the plot.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 plt.style.use('fivethirtyeight') x = np.linspace(0, 10) fig, ax = plt.subplots() ax.plot(x, np.sin(x) + x + np.random.randn(50)) ax.plot(x, np.sin(x) + 0.5 * x + ...
Read MoreAdding a line to a scatter plot using Python's Matplotlib
To add a line to a scatter plot using Python's Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, n, for number of data points.Plot x and y data points using scatter() method.Plot a line using plot() method.Limt the X-axis using xlim() 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 n = 100 x = np.random.rand(n) y = np.random.rand(n) plt.scatter(x, y, c=x) plt.plot([0.1, 0.4, 0.3, 0.2]) plt.xlim(0, 1) ...
Read MoreHow to disable the keyboard shortcuts in Matplotlib?
To disable the keyboard shortcuts in Matplotlib, we can use remove('s') method.StepsSet the figure size and adjust the padding between and around the subplots.To disable the shortcut "s" to save the figure, use remove("s") method.Initialize a variable n for number of data points.Create x and y data points using numpyPlot x and y 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 plt.rcParams['keymap.save'].remove('s') n = 10 x = np.random.rand(n) y = np.random.rand(n) plt.plot(x, y) plt.show()Output
Read MoreHow to label and change the scale of a Seaborn kdeplot's axes? (Matplotlib)
To label and change the scale of a Seaborn kdeplot's axes, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points using numpy.Plot Kernel Density Estimate (KDE) using kdeplot() method.Set Y-axis tscale and label.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.randn(10) k = sns.kdeplot(x=data, shade=True) plt.yticks(k.get_yticks(), k.get_yticks()) plt.ylabel('Y', fontsize=7) plt.show()Output
Read MoreHow to update the plot title with Matplotlib using animation?
To update the plot title with Matplotlib using animation, 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.Create x and y data points using numpy.Get the current axis.Add text to the axes using text() method.Add an animate method that can be used to make an animation by repeatedly calling a function.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, animation plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ...
Read MoreColouring the edges by weight in networkx (Matplotlib)
To color the edges by weight in networkx, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a graph with edges, name, or graph attributes.Add nodes to the current graph.Add edges to the current graph's nodes.Iterate the given graph's edges and set some weight to them.Draw current graphs with weights for edge color.To display the figure, use show() method.Exampleimport random as rd import matplotlib.pylab as plt import networkx as nx plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True G = nx.DiGraph() G.add_nodes_from([1, 2, 3, 4]) G.add_edges_from([(1, 2), (2, 3), ...
Read MorePlotting animated quivers in Python using Matplotlib
To animate quivers in Python, 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 u and v data points using numpy.Create a figure and a set of subplots.Plot a 2D field of arrows using quiver() method.To animate the quiver, we can change the u and v values, in animate() method. Update the u and v values and the color of the vectors.To display the figure, use show() method.Exampleimport numpy as np import random as rd from matplotlib import pyplot as plt, animation ...
Read MoreHow to make markers on lines smaller in Matplotlib?
To make markers on lines smaller in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points, x.Plot x data points using plot() method, with linewidth =0.5 and color="black".To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.random.rand(20) plt.plot(x, '*-', color='black', markersize=10, lw=0.5) plt.show()Output
Read MoreHow can I make Matplotlib.pyplot stop forcing the style of my markers?
To make matplotlib.pyplot stop forcing the style of markers, 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.Plot x and y data points using plot() method, with "r*" marker with markersize=10.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.random.rand(20) y = np.random.rand(20) plt.plot(x, y, 'r*', markersize=10) plt.show()Output
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