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Articles by Rishikesh Kumar Rishi
Page 49 of 102
How to set different opacity of edgecolor and facecolor of a patch in Matplotlib?
To set different opacity of edge and face color, we can use a color tuple and the 4th index of the tuple could set the opacity value of the colors.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots using subplots() method.Set different values for edge and face color opacity.Add a rectangel patch using add_patch() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True figure, ax = plt.subplots() edge_color_opacity = 1 # 0
Read MoreDraw a parametrized curve using pyplot.plot() in Matplotlib
To draw a parametrized curve using pyplot.plot(), we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of samples.Create t, r, x and y data points using numpy.Create a figure and a set of subplots.Use plot() method to plot x and y data points.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 N = 400 t = np.linspace(0, 2 * np.pi, N) r = 0.5 + np.cos(t) x, y = r * ...
Read MoreHow do I plot a spectrogram the same way that pylab's specgram() does? (Matplotlib)
To plot a spectrogram the same way that pylab's specgram() does, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create t, s1, s2, nse, x, NEFT and Fs data points using numpy.Create a new figure or activate an existing figure using subplots() method with nrows=2.Plot t and x data points using plot() method.Lay out a grid in current line style.Set the X-axis margins.Plot a spectrogram using specgram() method.Lay out a grid in current line style with dotted linestyle and some other properties.To display the figure, use show() method.Exampleimport matplotlib.pyplot as ...
Read MoreHow to save an array as a grayscale image with Matplotlib/Numpy?
To save an array as a grayscale image with Matplotlib/numpy, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data with 5☓5 dimension.Set the colormap to "gray".Plot the data using imshow() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True arr = np.random.rand(5, 5) plt.gray() plt.imshow(arr) plt.show()Output
Read MoreHow to plot categorical variables in Matplotlib?
To plot categorical variables in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a dictionary with some details.Extract the keys and values from the dictionary (Step 2).Create a figure and a set of subplots.Plot bar, scatter and plot with names and values data.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = {'apple': 10, 'orange': 15, 'lemon': 5} names = list(data.keys()) values = list(data.values()) fig, axs = plt.subplots(1, 3) axs[0].bar(names, values) axs[1].scatter(names, values) axs[2].plot(names, values) ...
Read MorePlot curves in fivethirtyeight stylesheet in Matplotlib
To use fivethirtyeight stylesheet, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.To use fivethirtyeight, we can use plt.style.use() method.Create x data points using numpy.Create a figure and a set of subplots using subplots() method.Plot three curves using plot() method.Set the title of the plot.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.style.use('fivethirtyeight') x = np.linspace(0, 10) fig, ax = plt.subplots() ax.plot(x, np.sin(x) + x + np.random.randn(50)) ax.plot(x, np.sin(x) + 0.5 * x + ...
Read MoreAdding a line to a scatter plot using Python's Matplotlib
To add a line to a scatter plot using Python's Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, n, for number of data points.Plot x and y data points using scatter() method.Plot a line using plot() method.Limt the X-axis using xlim() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True n = 100 x = np.random.rand(n) y = np.random.rand(n) plt.scatter(x, y, c=x) plt.plot([0.1, 0.4, 0.3, 0.2]) plt.xlim(0, 1) ...
Read MoreHow to disable the keyboard shortcuts in Matplotlib?
To disable the keyboard shortcuts in Matplotlib, we can use remove('s') method.StepsSet the figure size and adjust the padding between and around the subplots.To disable the shortcut "s" to save the figure, use remove("s") method.Initialize a variable n for number of data points.Create x and y data points using numpyPlot x and y data points using plot() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.rcParams['keymap.save'].remove('s') n = 10 x = np.random.rand(n) y = np.random.rand(n) plt.plot(x, y) plt.show()Output
Read MoreHow to label and change the scale of a Seaborn kdeplot's axes? (Matplotlib)
To label and change the scale of a Seaborn kdeplot's axes, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points using numpy.Plot Kernel Density Estimate (KDE) using kdeplot() method.Set Y-axis tscale and label.To display the figure, use show() method.Exampleimport numpy as np import seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.randn(10) k = sns.kdeplot(x=data, shade=True) plt.yticks(k.get_yticks(), k.get_yticks()) plt.ylabel('Y', fontsize=7) plt.show()Output
Read MoreHow to update the plot title with Matplotlib using animation?
To update the plot title with Matplotlib using animation, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure using figure() method.Create x and y data points using numpy.Get the current axis.Add text to the axes using text() method.Add an animate method that can be used to make an animation by repeatedly calling a function.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, animation plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ...
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