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Python Articles
Page 556 of 852
How to redefine a color for a specific value in a Matplotlib colormap?
To redefine a color for a specific value in matplotlib colormap, we can take the following steps −Get a colormap instance, defaulting to rc values if *name* is None using get_cmap() method, with gray colormap.Set the color for low out-of-range values when "norm.clip = False" using set_under() method.Using imshow() method, display data an image, i.e., on a 2D regular raster.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, cm plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True cmap = cm.get_cmap('gray') cmap.set_under('red') plt.imshow(np.arange(25).reshape(5, 5), interpolation='none', cmap=cmap, vmin=.001) plt.show()Output
Read MoreAnimate a rotating 3D graph in Matplotlib
To make a rotating 3D graph in matplotlib, we can use Animation class for calling a function repeatedly.StepsInitialize variables for number of mesh grids, frequency per second to call a function, frame numbers.Create x, y, and z array for a curve.Make a function to make z array using lambda function.To pass a function into the animation class, make a user-defined function to remove the previous plot and plot a surface using x, y, and zarray.Create a new figure or activate an existing figure.Add a subplot arrangement using subplots() method.Set the Z-axis limit using set_zlim() method.Call the animation class to animate the surface plot.To display ...
Read MoreHow to plot two Seaborn lmplots side-by-side (Matplotlib)?
To plot two graphs side-by-side in Seaborn, we can take the following steps −To create two graphs, we can use nrows=1, ncols=2 with figure size (7, 7).Create a data frame with keys, col1 and col2, using Pandas.Use countplot() to show the counts of observations in each categorical bin using bars.Adjust the padding between and around the subplots.To display the figure, use show() method.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 = pd.DataFrame(dict(col1=np.linspace(1, 10, 5), col2=np.linspace(1, 10, 5))) sns.countplot(df.col1, x='col1', ...
Read MoreShow Matplotlib graphs to image as fullscreen
To show matplotlib graphs as full screen, we can use full_screen_toggle() method.StepsCreate a figure or activate an existing figure using figure() method.Plot a line using two lists.Return the figure manager of the current figure.To toggle full screen image, use full_screen_toggle() method.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.figure() plt.plot([1, 2], [1, 2]) manager = plt.get_current_fig_manager() manager.full_screen_toggle() plt.show()Output
Read MoreHow to make a 4D plot with Matplotlib using arbitrary data?
To make a 4D plot, we can create x, y, z and c standard data points. Create a new figure or activate an existing figure.StepsUse figure() method to create a figure or activate an existing figure.Add a figure as part of a subplot arrangement.Create x, y, z and c data points using numpy.Create a scatter plot using scatter method.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 fig = plt.figure() ax = fig.add_subplot(111, projection='3d') x = np.random.standard_normal(100) y = np.random.standard_normal(100) z = np.random.standard_normal(100) c = np.random.standard_normal(100) img = ax.scatter(x, ...
Read MoreHow to plot a very simple bar chart (Python, Matplotlib) using input *.txt file?
To plot a very simple bar chart from an input text file, we can take the following steps −Make an empty list for bar names and heights.Read a text file and iterate each line.Append names and heights into lists.Plot the bar using lists (Step 1).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 bar_names = [] bar_heights = [] for line in open("test_data.txt", "r"): bar_name, bar_height = line.split() bar_names.append(bar_name) bar_heights.append(bar_height) plt.bar(bar_names, bar_heights) plt.show()"test_data.txt" contains the following data −Javed 75 Raju 65 Kiran 55 Rishi 95Output
Read MoreHow to make two histograms have the same bin width in Matplotlib?
To make two histograms having same bin width, we can compute the histogram of a set of data.StepsCreate random data, a, and normal distribution, b.Initialize a variable, bins, for the same bin width.Plot a and bins using hist() method.Plot b and bins using hist() 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 a = np.random.random(100) * 0.5 b = 1 - np.random.normal(size=100) * 0.1 bins = 10 bins = np.histogram(np.hstack((a, b)), bins=bins)[1] plt.hist(a, bins, edgecolor='black') plt.hist(b, bins, edgecolor='black') plt.show()Output
Read MoreHow to plot a rectangle inside a circle in Matplotlib?
To plot a rectangle inside a circle in matplotlib, we can take the following steps −Create a new figure or activate an existing figure using figure method.Add a subplot to the current axis.Make a rectangle and a circle instance using Rectangle() and Circle() class.Add a patch on the axes.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) rect1 = patches.Rectangle((-2, -2), 4, 2, color='yellow') circle1 = matplotlib.patches.Circle((0, 0), radius=3, color='red') ax.add_patch(circle1) ax.add_patch(rect1) plt.xlim([-5, 5]) plt.ylim([-5, 5]) plt.axis('equal') plt.show()Output
Read MoreWhat is the difference betweent set_xlim and set_xbound in Matplotlib?
set_xlim − Set the X-axis view limits.set_xbound − Set the lower and upper numerical bounds of the X-axis.To set the xlim and xbound, we can take the following steps −Using subplots(2), we can create a figure and a set of subplots. Here, we are creating 2 subplots.Create x and y data points using numpy.Use axis 1 to plot x and y data points using plot() method.Set x limit using set_xlim() method.Use axis 2 to plot x and y data points using plot() method.Sex xbound using set_xbound() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] ...
Read MoreHow to animate a pcolormesh in Matplotlib?
To animate pcolormesh in matplotlib, we can take the following steps −Create a figure and a set of subplots.Create x, y and t data points using numpy.Create X3, Y3 and T3, return coordinate matrices from coordinate vectors using meshgrid.Create a pseudocolor plot with a non-regular rectangular grid using pcolormesh() method.Make a colorbar with colormesh axis.Animate pcolormesh using Animation() class method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, animation plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x = np.linspace(-3, 3, 91) t = np.linspace(0, 25, 30) y = np.linspace(-3, 3, 91) X3, Y3, T3 = ...
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