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Articles by Rishikesh Kumar Rishi
Page 89 of 102
How to add group labels for bar charts in Matplotlib?
To make grouped labels for bar charts, we can take the following steps −Create lists for 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() method.Set a title for the axes using set_title() method.Get or set the current tick locations and ...
Read MoreHow to rotate xticklabels in Matplotlib so that the spacing between each xticklabel is equal?
To rotate xticklabels in matplotlib to make equal spacing between two xticklabels, we can take the following steps −Make a list of numbers from 1 to 4.Using subplot(), sdd a subplot to the current figure.Add xticks and yticks on the current subplot (using step 1).Set xtick labels by passing a list and to make label rotation (= 45).To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [1, 2, 3, 4] ax1 = plt.subplot() ax1.set_xticks(x) ax1.set_yticks(x) ax1.set_xticklabels(["one", "two", "three", "four"], rotation=45) plt.show()Output
Read MoreColorplot of 2D array in Matplotlib
To plot a colorplot of a 2D array, we can take the following steps −Create data (i.e., 2D array) using numpy.For colorplot, use imshow() method, with input data (Step 1) and colormap is "PuBuGn".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(4, 4) plt.imshow(data, cmap='PuBuGn') plt.show()Output
Read MoreAdding caption below X-axis for a scatter plot using Matplotlib
To add caption below X-axis for a scatter plot, we can use text() method for the current figure.StepsCreate x and y data points using numpy.Create a new figure or activate an existing figure using figure() method.Plot the scatter points with x and y data points.To add caption to the figure, use text() method.Adjust the padding between and around the subplots.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.random.rand(10) y = np.random.rand(10) fig = plt.figure() plt.scatter(x, y, c=y) fig.text(.5, .0001, "Scatter Plot", ha='center') plt.tight_layout() plt.show()Output
Read MoreHow to plot a confusion matrix with string axis rather than integer in Python?
To plot a confusion matrix with string axis rather than integer in Python, we can take the following steps−Make a list for labels.Create a confusion matrix. Use confusion_matrix() to calculate accuracy of classification.3. Add an '~.axes.Axes' to the figure as part of a subplot arrangement.Plot the values of a 2D matrix or array as a color-coded image.Using colorbar() method, create a colorbar for a ScalarMappable instance, *mappable*6. Set x and y ticklabels using set_xticklabels and set_yticklabels methods.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt from sklearn.metrics import confusion_matrix plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True labels ...
Read MoreSet variable point size in Matplotlib
To set the variable point size in matplotlib, we can take the following steps−Initialize the coordinates of the point.Make a variable to store the point size.Plot the point using scatter method, with marker=o, color=red, s=point_size.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 xy = (3, 4) point_size = 100 plt.scatter(x=xy[0], y=xy[1], marker='o', c='red', s=point_size) plt.show()Output
Read MoreHow to draw a rectangle over a specific region in a Matplotlib graph?
To draw a rectangle over a specific region in a matplotlib graph, we can take the following steps −Using subplots() method, create a figure and a set of subplots, where nrows=1.Using rectangle, we can create a rectangle, defined via an anchor point and its width and height. Where, edgecolor=orange, linewidth=7, and facecolor=green.To plot a diagram over the axis, we can create a line using plot() method, where line color is red.Add a rectangle patch on the diagram, using add_patch() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True figure, ax = plt.subplots(1) ...
Read MoreHow to create a draggable legend in Matplotlib?
To create a draggable legend in matplotlib, we can take the following steps −Create two lines, line1 and line2, using plot() method.Place the legend for plot line1 and line2 with ordered lables at location 1, using legend() method.To create a draggable legend, use set_draggable() method, where state=True. If state=False, then we can't drag the legend.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 line1, = plt.plot([1, 2, 3]) line2, = plt.plot([3, 2, 1]) leg = plt.legend([line2, line1], ["line 2", "line 1"], loc=1) leg.set_draggable(state=True) plt.show()OutputOn the output window, you can drag the legend around with ...
Read MoreAutomatically Rescale ylim and xlim in Matplotlib
To rescale ylim and xlim automatically, we can take the following steps −To plot a line, use plot() method and data range from 0 to 10.To scale the xlim and ylim automatically, we can make the variable scale_factore=6.Use scale_factor (from Step 2) to rescale the xlim and ylim, using xlim() and ylim() methods, respectively.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.plot(range(0, 10)) scale_factor = 6 xmin, xmax = plt.xlim() ymin, ymax = plt.ylim() plt.xlim(xmin * scale_factor, xmax * scale_factor) plt.ylim(ymin * scale_factor, ymax * scale_factor) plt.show()Output
Read MoreHow do I plot Shapely polygons and objects using Matplotlib?
To plot shapely polygons and objects using matplotlib, the steps are as follows −Create a polygon object using (x, y) data points.Get x and y, the exterior data, and the array using polygon.exterior.xy.Plot x and y data points using plot() method with red color.Examplefrom shapely.geometry import Polygon import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True polygon1 = Polygon([(0, 5), (1, 1), (3, 0), (4, 6), ]) x, y = polygon1.exterior.xy plt.plot(x, y, c="red") plt.show()Output
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