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To plot two graphs side-by-side in Seaborn, we can take the following steps −To create two graphs, we can use nrows=1, ncols=2 with figure size (7, 7).Create a data frame with keys, col1 and col2, using Pandas.Use countplot() to show the counts of observations in each categorical bin using bars.Adjust the padding between and around the subplots.To display the figure, use show() method.Exampleimport pandas as pd import numpy as np import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True f, axes = plt.subplots(1, 2) df = pd.DataFrame(dict(col1=np.linspace(1, 10, 5), col2=np.linspace(1, 10, 5))) sns.countplot(df.col1, x='col1', ... Read More
To show matplotlib graphs as full screen, we can use full_screen_toggle() method.StepsCreate a figure or activate an existing figure using figure() method.Plot a line using two lists.Return the figure manager of the current figure.To toggle full screen image, use full_screen_toggle() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True plt.figure() plt.plot([1, 2], [1, 2]) manager = plt.get_current_fig_manager() manager.full_screen_toggle() plt.show()Output
To make a 4D plot, we can create x, y, z and c standard data points. Create a new figure or activate an existing figure.StepsUse figure() method to create a figure or activate an existing figure.Add a figure as part of a subplot arrangement.Create x, y, z and c data points using numpy.Create a scatter plot using scatter method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_subplot(111, projection='3d') x = np.random.standard_normal(100) y = np.random.standard_normal(100) z = np.random.standard_normal(100) c = np.random.standard_normal(100) img = ax.scatter(x, ... Read More
To plot a very simple bar chart from an input text file, we can take the following steps −Make an empty list for bar names and heights.Read a text file and iterate each line.Append names and heights into lists.Plot the bar using lists (Step 1).To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True bar_names = [] bar_heights = [] for line in open("test_data.txt", "r"): bar_name, bar_height = line.split() bar_names.append(bar_name) bar_heights.append(bar_height) plt.bar(bar_names, bar_heights) plt.show()"test_data.txt" contains the following data −Javed 75 Raju 65 Kiran 55 Rishi 95OutputRead More
To make two histograms having same bin width, we can compute the histogram of a set of data.StepsCreate random data, a, and normal distribution, b.Initialize a variable, bins, for the same bin width.Plot a and bins using hist() method.Plot b and bins using hist() 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 a = np.random.random(100) * 0.5 b = 1 - np.random.normal(size=100) * 0.1 bins = 10 bins = np.histogram(np.hstack((a, b)), bins=bins)[1] plt.hist(a, bins, edgecolor='black') plt.hist(b, bins, edgecolor='black') plt.show()OutputRead More
To increase/reduce the fontsize of x and y tick labels in matplotlib, we can initialize the fontsize variable to reduce or increase font size.StepsCreate a list of numbers (x) that can be used to tick the axes.Get the axis using subplot() that helps to add a subplot to the current figure.Set ticks on x and y axes using set_xticks and set_yticks methods respectively and list x (from step 1).Set tick labels with label lists (["one", "two", "three", "four"]) using set_xticklabels() and set_yticklabels() with fontsize variable.To add space between axes and tick labels, we can use tick_params() method with pad argument that helps to ... Read More
To plot a rectangle inside 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 to the current axis.Make a rectangle and a circle instance using Rectangle() and Circle() class.Add a patch on the axes.Scale x and y axes using xlim() and ylim() methods.To display the figure, use show() method.Exampleimport matplotlib from 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(111) rect1 = patches.Rectangle((-2, -2), 4, 2, color='yellow') circle1 = matplotlib.patches.Circle((0, 0), radius=3, color='red') ax.add_patch(circle1) ax.add_patch(rect1) plt.xlim([-5, 5]) plt.ylim([-5, 5]) plt.axis('equal') plt.show()OutputRead More
set_xlim − Set the X-axis view limits.set_xbound − Set the lower and upper numerical bounds of the X-axis.To set the xlim and xbound, we can take the following steps −Using subplots(2), we can create a figure and a set of subplots. Here, we are creating 2 subplots.Create x and y data points using numpy.Use axis 1 to plot x and y data points using plot() method.Set x limit using set_xlim() method.Use axis 2 to plot x and y data points using plot() method.Sex xbound using set_xbound() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] ... Read More
To animate pcolormesh in matplotlib, we can take the following steps −Create a figure and a set of subplots.Create x, y and t data points using numpy.Create X3, Y3 and T3, return coordinate matrices from coordinate vectors using meshgrid.Create a pseudocolor plot with a non-regular rectangular grid using pcolormesh() method.Make a colorbar with colormesh axis.Animate pcolormesh using Animation() class method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, animation plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x = np.linspace(-3, 3, 91) t = np.linspace(0, 25, 30) y = np.linspace(-3, 3, 91) X3, Y3, T3 = ... Read More
To add a colorbar for hist2d plot, we can pass a scalar mappable object to colorbar() method's argument.StepsCreate x and y data points using numpy.Create a figure and a set of subplots using subplots() method.Make a 2D histogram plot using hist2d() method.Create a colorbar for a hist2d scalar mappable instance.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, colors plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.random.randn(100) y = np.random.randn(100) + 5 fig, ax = plt.subplots() hh = ax.hist2d(x, y, bins=40, norm=colors.LogNorm()) fig.colorbar(hh[3], ax=ax) plt.show()OutputRead More