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To handle an asymptote/discontinuity with matplotlib, we can take the following steps −Create x and y data points using numpy.Turn off the axes plot.Plot the line with x and y data points.Add a horizontal line across the axis, x=0.Add a vertical line across the axis, y=0.Place legend for the curve y=1/x.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, 100) y = 1 / x plt.axis('off') plt.plot(x, y, label='y=1/x') plt.axhline(y=0, c='red') plt.axvline(x=0, c='red') plt.legend(loc='upper left') plt.show()OutputRead More
To set the properties of a plot, we can get the current axis of the plot. After that, we can perform several set_* methods to set the properties of the plot.StepsCreate a figure and a set of subplots using subplots() method with figsize=(5, 5).Create x and y data points using numpy.Plot x and y using plot() method.Set the title and labels (for X and Y axis) using set_xlabel() and set_ylabel() methods.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 fig, ax = plt.subplots() x = np.linspace(-1, 1, 10) y = ... Read More
To show multiple images in one figure in matplotlib, we can take the following steps −Create random data using numpy.Add a subplot to the current figure, nrows=1, ncols=4 and at index=1.Display data as an image, i.e., on a 2D regular raster, using imshow() method with cmap="Blues_r".Add a subplot to the current figure, nrows=1, ncols=4 and at index=2.Display data as an image, i.e., on a 2D regular raster, using imshow() method with cmap="Accent_r".Add a subplot to the current figure, nrows=1, ncols=4 and at index=3.Display data as an image, i.e., on a 2D regular raster, using imshow() method with cmap="terrain_r".Add a subplot to the current figure, nrows=1, ... Read More
To remove gaps between bars, we can change the align value to center in the argument of bar() method.StepsCreate a dictionary called data with two keys, milk and water.Get the list of keys and values in the dictionay.Using subplots() method, create a figure and add a set of two subplots.On axis 2, use bar method to plot bars without gaps. Set the width attribute as 1.0. Set the title using set_title() method.Use tight_layout() to adjust the padding between and around the subplots.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 data = {'milk': 12, 'water': ... Read More
To set theta=0 on a matplotlib polar plot, we can take the following steps −Create random theta in the range of 0 to 100; convert them into radian.Using set_theta_zero_location() method, we can set the location of theta to 0.Plot theta_in_rad and data_r using plot() method.Set the title of the plot using title() method.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import random plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True theta_in_rad = [float(i) * np.pi / 180.0 for i in range(0, 100, 10)] data_r = random.sample(range(70, 90), 10) ax = plt.subplot(111, polar=True) ax.set_theta_zero_location("W") ax.plot(theta_in_rad, data_r, color='r', linewidth=3) ax.set_title("Example", ... Read More
plt.figure() - Create a new figure or activate an existing figure.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 figure number numclose(name), where name is a string, closes the figure with that labelclose('all') closes all the figure windowsplt.figure().clear() - It is the same as clf.plt.cla() - Clear the current axes.plt.clf() - Clear the current figure.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 = np.linspace(1, 2, 10) plt.plot(x, y, c='red') plt.title("First Plot") plt.show() ... Read More
To add a varaible to Python plt.title(), we can take the following steps −Create data points for x and y using numpy and num (is a variable) to calculate y and set this in title.Plot x and y data points using plot() method with red color.Set the title of the curve with variable num.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) num = 2 y = num ** x plt.plot(x, y, c='red') plt.title(f"y=%d$^x$" % num) plt.show()OutputRead More
To plot data from multiple two-column text files with legends in matplotlib, we can take the following steps −Import genfromtxt from pylab. It has several options to read data from a text file and plot the data.Read two text files, test.txt and test1.txt (having two columns of data), using genfromtxt and store the data in two variables, firstfiledata and secondfiledata.Plot the data using plot() method. label will be displayed as the legend.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt; from pylab import genfromtxt; plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True firstfiledata = genfromtxt("test.txt"); secondfiledata = genfromtxt("test1.txt"); plt.plot(firstfiledata[:, 0], firstfiledata[:, 1], label="test.txt ... Read More
To hide matplotlib descriptions of an instance while calling plot() method, we can take the following steps −Open Ipython instance.import numpy as npfrom matplotlib, import pyplot as pltCreate points for x, i.e., np.linspace(1, 10, 1000)Now, plot the line using plot() method.To hide the instance, use plt.plot(x); i.e., (with semi-colon)Or, use _ = plt.plot(x)ExampleIn [1]: import numpy as np In [2]: from matplotlib import pyplot as plt In [3]: x = np.linspace(1, 10, 1000) In [4]: plt.plot(x) Out[4]: [] In [5]: plt.plot(x); In [6]: _ = plt.plot(x) In [7]:OutputOut[4]: []
To plot multiple columns of Pandas DataFrame using Seaborn, we can take the following steps −Make a dataframe using Pandas.Plot a bar using Seaborn's barplot() method.Rotate the xticks label by 45 angle.To display the figure, use show() method.Exampleimport pandas import matplotlib.pylab as plt import seaborn as sns import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pandas.DataFrame({"X-Axis": [np.random.randint(10) for i in range(10)], "YAxis": [i for i in range(10)]}) bar_plot = sns.barplot(x='X-Axis', y='Y-Axis', data=df) plt.xticks(rotation=45) plt.show()Output