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Matplotlib Articles
Page 70 of 91
What is the difference between plt.close() and plt.clf() in Matplotlib?
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 MoreHow to plot data from multiple two-column text files with legends in Matplotlib?
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 MoreHide Matplotlib descriptions in Jupyter notebook
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]: []
Read MorePlot multiple columns of Pandas DataFrame using Seaborn
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
Read MoreHow to display Matplotlib Y-axis range using absolute values rather than offset values?
To display Y-axis range using absolute values rather than offset values, we can take the following steps −Create x_data and y_data data points in the range of 100 to 1000.Create a figure and a set of subplots using subplots() method.Plot x_data and y_data using plot() method.If a parameter is not set, the corresponding property of the formatter is left unchanged using ticklabel_format() method with useOffset=False.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 x_date = range(100, 1000, 100) y_data = range(100, 1000, 100) fig, ax = plt.subplots() ax.plot(x_date, y_data) ax.ticklabel_format(useOffset=False) plt.show()Output
Read MoreDrawing lines between two plots in Matplotlib
To draw lines between two plots in matplotlib, we can take the following steps −Create a new figure or activate an existing figure.Add two axes (ax1 and ax2) to the figure as part of a subplot arrangement.Create random data x and y using numpy.Plot x and y data points on both the axes (ax1 and ax2) with color=red and marker=diamond.Initialize two variables, i and j to get the diffirent data points on the subplot.Make xy and mn tuple for positions to add a patch on the subplots.Add a patch that connects two points (possibly in different axes), con1 and con2.Add artists for con1 ...
Read MoreHow 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 ...
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