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Matplotlib Articles
Page 60 of 91
How to set same color for markers and lines in a Matplotlib plot loop?
To set the same color for markers and lines in a matplotlib, we can take the following Steps −Initialize m, n and x data points using numpy.Create a new figure or activate an existing figure using figure() method.Clear the figure using clf() method.Add a subplot to the current figure using subplot() method.Get a marker from a iterable marker type.Iterate a range from 1 to n.Plot the lines and markers in the loop using plot() method with the same marker and colors for a line.To display the figure, use show() method.Exampleimport numpy as np import itertools from matplotlib import pyplot as ...
Read MorePlot a rectangle with an edgecolor in Matplotlib
To put edgecolor of a rectangle in matplotlib, we can take the following Steps −Create a new figure or activate an existing figure using figure() method.Add a subplot method to the current axis.Create a rectangle instance using Rectangle() class with an edgecolor and linewidth of the edge.Add a rectangle path on the plot.To place the text in the rectangle, we can use text() method.Scale x and y axes using xlim() and ylim() methods.To display the figure, use show() method.Exampleimport matplotlib from matplotlib import pyplot as plt, patches plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_subplot(111) ...
Read MoreCentering x-tick labels between tick marks in Matplotlib
To place labels between two ticks, we can take the following steps−Load some sample data, r.Create a copy of the array, cast to a specified type.Create a figure and a set of subplots using subplots() method.Plot date and r sample data.Set the locator of the major/minor ticker using set_major_locator() and set_minor_locator() methods.Set the locator of the major/minor formatter using set_major_locator() and set_minor_formatter() methods.Now, place the ticklabel at the center.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.cbook as cbook import matplotlib.dates as dates import matplotlib.ticker as ticker import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = ...
Read MoreHow to change a table's fontsize with matplotlib.pyplot?
To change a table's fontsize with matplotlib, we can use set_fontsize() method.StepsCreate a figure and a set of subplots, nrows=1 and ncols=1.Create random data using numpy.Create columns value.Make the axis tight and off.Initialize a variable fontsize to change the font size.Set the font size of the table using set_font_size() 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 fig, axs = plt.subplots(1, 1) data = np.random.random((10, 3)) columns = ("Column I", "Column II", "Column III") axs.axis('tight') axs.axis('off') the_table = axs.table(cellText=data, colLabels=columns, loc='center') the_table.auto_set_font_size(False) the_table.set_fontsize(10) plt.show()Output
Read MoreChanging Matplotlib subplot size/position after axes creation
To change subplot size or position after axes creation, we can take the following steps−Create a new figure or activate an existing figure using figure() method.Add an '~.axes.Axes' to the figure as part of a subplot arrangement using add_subplot() method.A grid layout to place subplots within a figure using GridSpec() class.Set the position of the grid specs.Set the subplotspec instance.Add an '~.axes.Axes' to the figure as part of a subplot arrangement using add_subplot() method, with gridspec instance.Adjust the padding between and around the subplots.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt from matplotlib import gridspec as ...
Read MoreHow to rotate Matplotlib annotation to match a line?
To rotate matplotlib annotation to match a line, we can take the following steps−Create a new figure or activate an existing figure using figure() method.Add an '~.axes.Axes' to the figure as part of a subplot arrangement using add_subplot() method.Initialize the variables, m (slope) and c (intercept).Create x and y data points using numpy.Calculate theta to make text rotation.Plot the line using plot() method with x and y.Place text on the line using text() 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 fig = plt.figure() ax ...
Read MoreHow do I close all the open pyplot windows (Matplotlib)?
plt.figure().close(): Close a figure window.close() by itself closes the current figureclose(h), where h is a Figure instance, closes that figureclose(num) closes the figure with number=numclose(name), where name is a string, closes the figure with that labelclose('all') closes all the figure windowsExamplefrom matplotlib import pyplot as plt fig = plt.figure() ax = fig.add_subplot() plt.show() plt.close()OutputNow, swap the statements "plt.show()" and "plt.close()" in the code. You wouldn't get to see any plot as the output because the plot would already have been closed.
Read MorePlotting a horizontal line on multiple subplots in Python using pyplot
To plot a horizontal line on multiple subplots in Python, we can use subplots to get multiple axes and axhline() method to draw a horizontal line.StepsCreate a figure and a set of subplots. Here, we will create 3 subplots.Use axhline() method to draw horizontal lines on each axis.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt fig, (ax1, ax2, ax3) = plt.subplots(3) plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True ax1.axhline(y=0.5, xmin=0, xmax=3, c="black", linewidth=2, zorder=0) ax2.axhline(y=0.5, xmin=0, xmax=3, c="red", linewidth=3, zorder=0) ax3.axhline(y=0.5, xmin=0, xmax=3, c="yellow", linewidth=4, zorder=0) plt.show()Output
Read MoreOverlay an image segmentation with Numpy and Matplotlib
To overlay an image segmentation with numpy, we can take the following Steps −Make a masked array of 10×10 dimension.Update the masked array with 1 for some region.Make image data using numpy.Mask an array where a condition is met, to get the masked data.Create a new figure or activate an existing figure using figure() mrthod.Use imshow() method to display data as an image, i.e., on a 2D regular raster.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True mask = np.zeros((10, 10)) mask[3:-3, 3:-3] = 1 im ...
Read MoreHow to limit the number of groups shown in a Seaborn countplot using Matplotlib?
To limit the number of groups shown in a Seaborn countplot, we can use a variable group_count, used in countplot() method arguments.StepsCreate a figure and two sets of subplots.Create a data frame using Pandas, with two keys.Initalize a variable group_count to limit the group count in countplot() method.Use countplot() method to show the counts of observations in each categorical bin using bars.Adjust the padding between and around the subplots.Exampleimport pandas as pd import numpy as np import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True f, axes = plt.subplots(1, 2) df = ...
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