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Page 1940 of 2109
How to display only a left and bottom box border in Matplotlib?
To display or hide box border in matplotlib, we can use spines (value could be right, left, top or bottom) and set_visible() method to set the visibility to True or False.StepsCreate x and y data points using numpy.Create a figure and add a set of subplots using subplots() method.Plot x and y data points using plot() method, where linewidth=7 and color=red.Set visibility as True for left and bottom, and False for top and right.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(-2, 2, 10) y ...
Read MoreHow to increase plt.title font size in Matplotlib?
To increase plt.title font size, we can initialize a variable fontsize and can use it in the title() method's argument.StepsCreate x and y data points using numpy.Use subtitle() method to place the title at the center.Plot the data points, x and y.Set the title with a specified fontsize.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) y = x ** 2 fontsize = 12 plt.suptitle("Quadratic Equation", fontsize=fontsize) plt.plot(x, y) plt.title("y=x$^{2}$", fontdict={'fontsize': fontsize}) plt.show()Output
Read MoreHow do I configure the behavior of the Qt4Agg backend in Matplotlib?
To configure the behaviour of the backend, we can use matplotlib.rcParams['backend'] with a new backend name.StepsUse get_backend() method to get the backend name.Override the existing backend name using matplotlib.rcParams.Use get_backend() method to get the configured backend name.Exampleimport matplotlib backend = matplotlib.get_backend() print("The current backend name is: ", backend) matplotlib.rcParams['backend'] = 'TkAgg' backend = matplotlib.get_backend() print("Configured backend name is: ", backend)OutputThe current backend name is: GTK3Agg Configured backend name is: TkAgg
Read MoreHow to change the strength of antialiasing in Matplotlib?
We can change the strength of antialiasing by using True or False flag in the argument of plot() method.StepsCreate x data points and colors list with different colors.Defining a method that accepts antialiased flag and axis.We can iterate in the range of 5, to print 5 different colors of curves from x data points (Step 1).Create a new figure or activate an existing figure.Add an axis to the figure as part of a subplot arrangement, at index 1.Plot a line with antialiased flag set as False and ax1 (axis 1) and set the title of the figure.Add an axis to the figure ...
Read MoreWhat is the necessity of plt.figure() in Matplotlib?
Using plt.figure(), we can create multiple figures and to close them all explicitly, call plt.close(). If you are creating many figures, make sure you explicitly call pyplot.close on the figures you are not using, because this will enable pyplot to properly clean up the memory.Using subplots(), we can create a figure and set of subplots.Here we creating two figures, fig1 and fig2. fig1 is 8×8 in size, whereas fig2 has the default figsize. There are 4×4=16 subplots added in fig2.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig2, ax_lst = plt.subplots(4, 4) plt.show()Output
Read MoreHow to adjust transparency (alpha) in Seaborn pairplot using Matplotlib?
To adjust transparency, i.e., aplha in Seaborn pairplot, we can change the value of alpha.StepsCreate a dataframe using Pandas with two keys, col1 and col2.Initialize the variable, alpha, for transparency.Use pairplot() method to plot pairwise relationships in a dataset. Use df (from step 1), kind="scatter", and set the plot size, edgecolor, facecolor, linewidth and alpha vaues in the arguments.To display the figure, use show() method.Exampleimport pandas as pd import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({"col1": [1, 3, 5, 7, 1], "col2": [1, 5, 7, 9, 1]}) alpha = 0.75 ...
Read MoreHow do I let my Matplotlib plot go beyond the axes?
To let my matplotlib plot go beyond the axes, we can turn off the flag clip_on in the argument of plot() method.StepsCreate xs and ys data points using numpy.Limit the X and Y axis range in the plot to let the line go beyond this limit, using xlim() and ylim() method.Plot the xs and ys data points using plot() method, where marker is a diamond shape, color is orange and clip_on=False (to go beyond the plot).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 xs = np.arange(10) ys ...
Read MoreHow to avoid overlapping of labels & autopct in a Matplotlib pie chart?
To avoid overlapping of labels and autopct in a matplotlib pie chart, we can follow label as a legend, using legend() method.StepsInitialize a variable n=20 to get a number of sections in a pie chart.Create slices and activities using numpy.Create random colors using hexadecimal alphabets, in the range of 20.Use pie() method to plot a pie chart with slices, colors, and slices data points as a label.Make a list of labels (those are overlapped using autopct).Use legend() method to avoid overlapping of labels and autopct.To display the figure, use show() method.Exampleimport random import numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = ...
Read MoreFixing color in scatter plots in Matplotlib
To fix colors in scatter plots in matplotlib, we can take the following steps −Create xs and ys random data points using numpy.Create a set of colors using hexadecimal alpabets, equal to the length of ys.Plot the lists, xs and ys, using scatter() method, with the list of colors.To display the figure, use show() method.Exampleimport random import numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True xs = np.random.rand(100) ys = np.random.rand(100) colors = ["#" + ''.join([random.choice('0123456789ABCDEF') for j in range(6)]) for i in range(len(xs))] plt.scatter(xs, ys, c=colors) plt.show()Output
Read MoreSetting Transparency Based on Pixel Values in Matplotlib
To set transparency based on pixel values in matplotlib, get masked data wherever data is less than certain values. Lesser value will result in full overlapping between two images.StepsCreate data1 and data2 using numpy.Get the masked data using numpy's masked_where() method.Using subplots() method, create a figure and a set of subplots (fig and ax).Display the data (data1 and masked data) as an image, i.e., on a 2D regular raster, using imshow() method, with different colormaps, jet and gray.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import matplotlib.cm as cm plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data1 = np.random.rand(50, 50) data2 = ...
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