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Page 1948 of 2109
Plot a black-and-white binary map in Matplotlib
To plot black-and-white binary map in matplotlib, we can create and add two subplots to the current figure using subplot() method, where nrows=1 and ncols=2. To display the data as a binary map, we can use greys colormap in imshow() method.StepsCreate data using numpyAdd two sublots, nrows=1 and ncols=2. Consider index 1.To show colored image, use imshow() method.Add title to the colored map.Add a second subplot at index 2.To show the binary map, use show() method with Greys colormap.To adjust the padding between and around the subplots, use tight_layout() method.To display the figure, use show() method.Exampleimport numpy as np from ...
Read MoreHow do you create a legend for a contour plot in Matplotlib?
To create a legend for a contour plot in matplotlib, we can take the following steps−Create x, y and z data points to plot the contour function.To create a 3D filled contour plot, we can use contourf() method with x, y, z and different levels.Make a list of rectangle with the returned contour signature collection and set face colorNow, place the legend in the plot using proxy (of step 3) and different labels.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, y = np.meshgrid(np.arange(10), np.arange(10)) ...
Read MoreOverlapping Y-axis tick label and X-axis tick label in Matplotlib
To reduce the chances of overlapping between x and y tick labels in matplotlib, we can take the following steps −Create x and y data points using numpy.Add a subplot to the current figure at index 1 (nrows=1 and ncols=2).Set x and y margins to 0.Plot x and y data points and add a title to this subplot, i.e., "Overlapping".Add a subplot to the current figure at index 2 (nrows=1 and ncols=2).Set x and y margins to 0.Plot x and y data points and add a title to this subplot, i.e., "Non Overlapping".The objective of MaxNLocator and prune ="lower" is that the smallest tick will be removed.To display the figure, ...
Read MoreStyling a part of label in legend in Matplotlib
To style a part of label in legend, we can take the following steps −Create data point for x using numpy.Plot a sine curve using np.sin(x) with a text label.Plot a cosine curve using np.cos(x) with a text label.To place the legend on the plot, use 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 x = np.linspace(-1, 1, 10) plt.plot(x, np.sin(x), label="This is $\it{a\ sine\ curve}$") plt.plot(x, np.cos(x), label="This is $\bf{a\ cosine\ curve}$") plt.legend(loc='lower right') plt.show()Output
Read MoreHow do I plot only a table in Matplotlib?
To plot only a table, we can take the following steps−Create fig and axs, using subplots. Create a figure and a set of subplots.Create random data for 10 rows and 3 columns.Create a tuple for columns name.axis('tight') − Set the limits, just large enough to show all the data, then disable further autoscaling.axis('off') − Turn off axis lines and labels. Same as ''False''.To add a table on the axis, use table() instance, with column text, column labels, columns, and location=center.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"] ...
Read MoreHow to change the order of plots in Pandas hist command?
To change order of plots in Pandas hist commad, we can take the following steps −Make a data frame using Pandas.Plot a histogram with the data frame.Plot the data frame in different order.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import pandas as pd plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'a': [1, 1, 1, 1, 3], 'b': [1, 1, 2, 1, 3], 'c': [2, 2, 2, 1, 3], }) df.hist() df[['c']].hist() df[['a']].hist() df[['b']].hist() plt.show()Output
Read MoreHow to add vertical lines to a distribution plot (sns.distplot) in Matplotlib?
To add vertical lines to a distribution plot, we can take the following steps−Create a list of numbers.Create an axis using sns.displot().Get x and y data of the axis ax.Plot a vertical line on the plot.Remove the line at the 0th index.To display the figure, use show() method.Exampleimport seaborn as sns, numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [5, 6, 7, 2, 3, 4, 1, 8, 2] ax = sns.distplot(x, kde=True) x = ax.lines[0].get_xdata() y = ax.lines[0].get_ydata() plt.axvline(x[np.argmax(y)], color='red') ax.lines[0].remove() plt.show()Output
Read MoreHow to draw a log-normalized imshow plot with a colorbar representing the raw data in Matplotlib?
To draw a log-normalized imshow() plot with a colorbar representing the raw data in matplotlib, we can take the following steps −Create a 2D array using numpy.Display the data as an image, i.e., on a 2D regular raster, using imshow() methodCreate a colorbar for a ScalarMappable instance, *mappable*, using imshow() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, cm from matplotlib import colors plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(4, 4) im = plt.imshow(data, cmap=cm.rainbow, norm=colors.LogNorm()) plt.colorbar(im) plt.show()Output
Read MoreAdjusting gridlines and ticks in Matplotlib imshow
To adjust gridlines and ticks in matplotlib imshow(), we can take the following steps−Create data, a 2D array, using numpy.Using imshow() method, display data as an image.Set xticks and yticks using set_xticks and set_yticks method.To set the xticklabels and yticklabels, use set_xticklabels and set_yticklabels method.Lay out a grid in current line style. Supply the list of x an y positions using grid() 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 data = np.random.rand(9, 9) plt.imshow(data, interpolation="nearest") ax = plt.gca() ax.set_xticks(np.arange(-.5, 9, 1)) ax.set_yticks(np.arange(-.5, 9, 1)) ...
Read MoreMatplotlib Plot Lines with Colors through Colormap
To plot lines with colors through colormap, we can take the following steps−Create x and y data points using numpyPlot x and y data points using plot() method.Count n finds, number of color lines has to be plotted.Iterate in a range (n) and plot the lines.Limit the x ticks range.Use show() method to display the figure.Exampleimport numpy as np import matplotlib.pylab as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(0, 2 * np.pi, 64) y = np.exp(x) plt.plot(x, y) n = 20 colors = plt.cm.rainbow(np.linspace(0, 1, n)) for i in range(n): plt.plot(x, i * y, color=colors[i]) plt.xlim(4, ...
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