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Matplotlib Articles
Page 73 of 91
How to improve the label placement for Matplotlib scatter chart?
To imporove the label placement for matplotlib scatter chart, we can first plot the scatter points and annotate those points with labels.StepsCreate points for x and y using numpy.Create labels using xpoints.Use scatter() method to scatter points.Iterate the labels, xpoints and ypoints and annotate the plot with label, x and y with different properties.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 xpoints = np.linspace(1, 10, 10) ypoints = np.random.rand(10) labels = ["%.2f" % i for i in xpoints] plt.scatter(xpoints, ypoints, c=xpoints) for label, x, y in zip(labels, xpoints, ypoints): ...
Read MoreHow to plot bar graphs with same X coordinates side by side in Matplotlib?
To plot bar graphs with same X coordinates (G1, G2, G3, G4 and G5), side by side in matplotlib, we can take the following steps −Create the following lists – labels, men_means and women_means with different data elements.Return evenly spaced values within a given interval, using numpy.arrange() method.Set the width variable, i.e., width=0.35.Create fig and ax variables using subplots method, where default nrows and ncols are 1.The bars are positioned at *x* with the given *align*\ment. Their dimensions are given by *height* and *width*. The vertical baseline is *bottom* (default 0), so create rect1 and rect2 using plt.bar() method.Set the Y-axis label using plt.ylabel() ...
Read MorePlot scatter points using plot method in Matplotlib
To plot scatter points using plot method in matplotlib, we can take the following steps−Create random data points (x1 and x2) using numpy.Plot x1 data points using plot() method with marker size 20 and green color.Plot x2 data points using plot() method with marker size 10 and red color.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x1 = np.random.randn(20) x2 = np.random.randn(20) plt.plot(x1, 'go', markersize=20) plt.plot(x2, 'ro', ms=10) plt.show()Output
Read MoreSave figure as file from iPython notebook using Matplotlib
To save a figure as a file from iPython, we can take the following steps−Create a new figure or activate an existing figure.Add an axes to the figure using add_axes() method.Plot the given list.Save the plot using savefig() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_axes([1, 1, 1, 1]) plt.plot([1, 2]) plt.savefig('test.png', bbox_inches='tight')OutputWhen we execute the code, it will save the following plot as "test.png".
Read MoreHow to plot 2D math vectors with Matplotlib?
To plot 2D math vectors with matplotlib, we can take the following steps−Create vector cordinates using numpy array.Get x, y, u and v data points.Create a new figure or activate an existing figure using figure method.Get the current axis using gca() method.Set x an y limits of the axes.To redraw the current figure, use draw() method.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True soa = np.array([[0, 0, 3, 2], [0, 0, 4, 5], [0, 0, 9, 9]]) X, Y, U, V = zip(*soa) plt.figure() ax = plt.gca() ...
Read MoreWrapping long Y labels in Matplotlib tight layout using setp
To wrap long Y label in matplotlib tight layput using setp, we can take the following steps−Create a list of a long strings.Create a tuple of 3 values.Create a figure and add a set of subplots.Limit the Y-axis ticks using ylim() method.Make a horizontal bar plot, using barh() method.Use yticks() method to ticks the yticks.Use setp() method to set a property on an artist object.Use tight_layout() method to adjust the padding between and around subplots.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True labels = ( ...
Read MoreHow to get a list of all the fonts currently available for Matplotlib?
To get a list of all the fonts currently available for matplotlib, we can use the font_manager.findSystemFonts() method.StepsPrint a statement.Use font_manager.findSystemFonts() method to get a list of fonts availabe.Examplefrom matplotlib import font_manager print("List of all fonts currently available in the matplotlib:") print(*font_manager.findSystemFonts(fontpaths=None, fontext='ttf'), sep="")Output/usr/share/fonts/truetype/Nakula/nakula.ttf /usr/share/fonts/truetype/ubuntu/Ubuntu-L.ttf /usr/share/fonts/truetype/tlwg/Loma-BoldOblique.ttf ................................................................. ............................................................................ ................................................................................. ........ /usr/share/fonts/truetype/lohit-malayalam/Lohit-Malayalam.ttf /usr/share/fonts/truetype/tlwg/TlwgTypist-Oblique.ttf /usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf
Read MoreTop label for Matplotlib colorbars
To place a top label for colorbars, we can use colorbar's axis to set the title.StepsCreate random data using numpy.Use imshow() method to represent data into an image, with colormap "PuBuGn" and interpolation= "nearest".Create a colorbar for a scalar mappable instance, imSet the title on the ax (of colorbar) using set_title() 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.randn(4, 4) im = plt.imshow(data, interpolation='nearest', cmap="PuBuGn") clb = plt.colorbar(im) clb.ax.set_title('Color Bar Title') plt.show()Output
Read MoreDarken or lighten a color in Matplotlib
To darken and lighten the color, we can chage the alpha value in the argument of plot() method.Greater the aplha value, darker will be the color.StepsCreate data points for xs and ys using numpy.Plot two lines with different value of alpha, to replicate darker and lighter color of the linesPlace legend of the plot using 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 xs = np.linspace(-2, 2, 100) ys = np.sin(xs) plt.plot(xs, ys, c='red', lw=10, label="Darken") plt.plot(xs+.75, ys+.75, c='red', lw=10, alpha=0.3, label="Lighten") plt.legend(loc='upper left') ...
Read MoreHow do I change the range of the X-axis with datetimes in Matplotlib?
To change the range of the X-axis with datetimes in matplotlib, we can take the following steps −Create a list of x and y, where x stores the datetime and y stores the number.Using subplots method, create a figure and add a set of subplots.Plot x and y data points using plots() method, wehere markerface color is green, marker edge color is red, and marker size is 7.Since date ticklabels often overlap, so it is useful to rorate them and right-align them using autofmt_xdate() method.To change the range of X-axis with datetimes, use set_xlim() with range of datetimes.To change the range of Y-axis, use set_ylim() method.To ...
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