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To decrease the density of x-ticks in Seaborn, we can use set_visible=False for odd positions.StepsSet the figure size and adjust the padding between and around the subplots.Create a dataframe with X-axis and Y-axis keys.Show the point estimates and confidence intervals with bars, using barplot() method.Iterate bar_plot.get_xticklabels() method. If index is even, then make them visible; else, not visible.To display the figure, use show() method.Exampleimport pandas import matplotlib.pylab as plt import seaborn as sns plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pandas.DataFrame({"X-Axis": [i for i in range(10)], "Y-Axis": [i for i in range(10)]}) bar_plot = sns.barplot(x='X-Axis', y='Y-Axis', data=df) for ... Read More
To remove the space between subplots in matplotlib, we can use GridSpec(3, 3) class and add axes as a subplot arrangement.StepsSet the figure size and adjust the padding between and around the subplots.Add a grid layout to place subplots within a figure.Update the subplot parameters of the gridIterate in the range of dimension of grid specs.Add a subplot to the current figure.Set the aspect ratios.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import matplotlib.gridspec as gridspec plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True gs1 = gridspec.GridSpec(3, 3) gs1.update(wspace=0.5, hspace=0.1) for i in range(9): ax1 = plt.subplot(gs1[i]) ax1.set_aspect('equal') plt.show()OutputRead More
A simple call to the imread method loads our image as a multi-dimensional NumPy array (one for each Red, Green, and Blue component, respectively) and imshow displays our image on the screen. Whereas, cv2 represents RGB images as multi-dimensional NumPy arrays, but in reverse order.StepsSet the figure size and adjust the padding between and around the subplots.Initialize the filename.Add a subplot to the current figure using nrows=1, ncols=2, and index=1.Read the image using cv2.Off the axes and show the figure in the next statement.Add a subplot to the current figure using nrows=1, ncols=2, and index=2.Read the image using plt.Off the ... Read More
To make matplotlib scatterplots transparent as a group, we can change the alpha value in the scatter() method argument with a different group value.StepsSet the figure size and adjust the padding between and around the subplots.Make a method to return a grouped x and y points.Get group 1 and group 2 data points.Plot group1, x and y points using scatter() method with color=green and alpha=0.5.Plot group2, x and y points using scatter() method with color=red and alpha=0.5.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 def ... Read More
To align rows in a matplotlib legend with 2 columns, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Using plot() method, plot lines with the labels line1, line2 and line3.Place a legend on the figure with two columns. Use ncol=2.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.plot([1, 2, 3], label="line1") plt.plot([3, 2, 1], label="line2") plt.plot([2, 3, 1], label="line3") plt.legend(ncol=2, loc="upper right") plt.show()Output
To map values to a colors tuple(red, green and blue) in matplotlib, we can take the following steps −Create a list of values from 1.00 to 2.00, count=10.Get linearly normalized data into the vmin and vmax interval.Get an object to map the scalar data to rgba.Iterate the values to map the color values.Print the values against the mapped red, green, and blue values.Exampleimport numpy as np from matplotlib import cm, colors values = np.linspace(1.0, 2.0, 10) norm = colors.Normalize(vmin=1.0, vmax=2.0, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap=cm.Greys_r) for value in values: print("%.2f" % value, "=", "red:%.2f" % mapper.to_rgba(value)[0], ... Read More
To draw a network graph with networkx and matplotlib, plt.show() −Set the figure size and adjust the padding between and around the subplots.Make an object for a dataframe with the keys, from and to.Get a graph containing an edgelist.Draw a graph (Step 3) using draw() method with some node properties.To display the figure, use show() method.Exampleimport pandas as pd import networkx as nx from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'from': ['A', 'B', 'C', 'A'], 'to': ['D', 'A', 'E', 'C']}) G = nx.from_pandas_edgelist(df, 'from', 'to') nx.draw(G, with_labels=True, node_size=100, alpha=1, linewidths=10) plt.show()OutputRead More
To draw R-style (default is regular style) axis ticks that point outward from the axes in matplotlib, we can use rcParams["xticks.direction"]="out" for X-axis.StepsSet the figure size and adjust the padding between and around the subplots.Set outwaord tick points using plt.rcParams.Initialize a variable for the number of data points.Create x and y data points using numpy.Plot 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['ytick.direction'] = 'out' # in plt.rcParams['xtick.direction'] = 'out' # in n = 10 x = ... Read More
To make width of title box span the entire plot in matplotlib, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot x and y data points using plot() method, with color=black and linewidth=7.Get the current axes using gca() method.Set the title of of the plot.Return the bbox patch using get_bbox_patch() methodTo 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(-2, 2, 100) y = np.sin(x) plt.plot(x, y, c='black', ... Read More
To convert or scale the axis values and redefine the tick frequency in matplotlib, we can make a list of xticks and xtick_labels using xticks() method. Place the axis scale and redefine the tick frequency.StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable, n, for the number of data points.Create x and y data points using numpy.Plot x and y data points using plot() method.Make lists of ticks and tick labels.Use xticks() method to place axis scale and redefine tick frequency.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot ... Read More