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Data Visualization Articles
Page 39 of 68
How to Zoom with Axes3D in Matplotlib?
To zoom with Axes3D, 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.Get 3D axes object using Axes3D(fig) method.Plot x, y and z data points using scatter() method.To display the figure, use show() method.Examplefrom mpl_toolkits.mplot3d import Axes3D from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = Axes3D(fig) x = [2, 4, 6, 3, 1] y = [1, 6, 8, 1, 3] z = [3, 4, 10, 3, 1] ...
Read MoreHow to get alternating colours in a dashed line using Matplotlib?
To get alternating colors in a dashed line using Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplotsGet the current axis.Create x and y data points using numpy.Plot x and y data points with "-" and "--" linestyle.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ax = plt.gca() x = np.linspace(-10, 10, 100) y = np.sin(x) ax.plot(x, y, '-', color='red', linewidth=5) ax.plot(x, y, '--', color='yellow', linewidth=5) plt.show()Output
Read MoreHow to plot a layered image in Matplotlib in Python?
To plot a layered image in Matplotlib in Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create dx, dy, x, y and extent data using numpy.Create a new figure or activate an existing figure using figure() method.Create data1 and data2 to display the data as an image, i.e., on a 2D regular raster.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 dx, dy = 0.05, 0.05 x = np.arange(-3.0, 3.0, dx) y = np.arange(-3.0, 3.0, ...
Read MoreHow to save a plot in Seaborn with Python (Matplotlib)?
To save a plot in Seaborn, we can use the savefig() method.StepsSet the figure size and adjust the padding between and around the subplots.Make a two-dimensional, size-mutable, potentially heterogeneous tabular data.Plot pairwise relationships in a dataset.Save the plot into a file using savefig() method.To display the figure, use show() method.Exampleimport seaborn as sns import pandas as pd import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame(np.random.random((5, 5)), columns=["a", "b", "c", "d", "e"]) sns_pp = sns.pairplot(df) sns_pp.savefig("sns-heatmap.png")OutputWhen we execute the code, it will create the following plot and save it ...
Read MoreHow to remove grid lines from an image in Python Matplotlib?
To remove grid lines from an image, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Load an image from a file.Convert the image from one color space to another.To remove grid lines, use ax.grid(False).Display the 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 cv2 plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True img = cv2.imread('bird.jpg') img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) plt.grid(False) plt.imshow(img) plt.show()Output
Read MoreHow to customize X-axis ticks in Matplotlib?
To customize X-axis ticks in Matplotlib, we can change the ticks length and width.StepsSet the figure size and adjust the padding between and around the subplots.Create lists for height, bars and y_pos data points.Make a bar plot using bar() method.To customize X-axis ticks, we can use tick_params() method, with color=red, direction=outward, length=7, and width=2.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 height = [3, 12, 5, 18, 45] bars = ('A', 'B', 'C', 'D', 'E') y_pos = np.arange(len(bars)) plt.bar(y_pos, height, color='yellow') plt.tick_params(axis='x', colors='red', direction='out', ...
Read MoreHow to reverse the colormap of an image to scalar values in Matplotib?
To reverse the colormap of an image, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points using x and y.Get the blue color map using get_cmap() method.Add a subplot to the current figure at index 1.Plot x and y data points using scatter() method.Create a colorbar for a scalar mappable instance.Plot x and y data points using scatter() method, with reversed colormap.Set the title of both the axes.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"] ...
Read MoreHow to plot single data with two Y-axes (two units) in Matplotlib?
To plot single data with two Y-Axes (Two units) in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create speed and acceleration data points using numpy.Add a subplot to the current figure.Plot speed data points using plot() method.Create a twin Axes sharing the X-axis.Plot acceleration data point using plot() method.Place a legend on the figure.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 speed = np.array([3, 1, 2, 0, 5]) acceleration = np.array([6, 5, 7, ...
Read MoreHow to embed an interactive Matplotlib plot on a webpage?
To show a plot on a webpage such that the plot could be interactive, we can take the following steps −Install Bokeh and import figure, show, and output_file.Configure the default output state to generate the output saved to a file when:func:'show' is called.Create a new Figure for plotting.Render the images loaded from the given URLs.Immediately display a Bokeh object or application.Examplefrom bokeh.plotting import figure, show, output_file output_file('image.html') p = figure(x_range=(0, 1), y_range=(0, 1)) p.image_url(url=['bird.jpg'], x=0, y=1, w=0.8, h=0.6) show(p)OutputWhen we execute the code, it will show the following image on your default browser.You can move the image around ...
Read MoreCreate a legend with Pandas and Matplotlib.pyplot
To create a legend with Pandas and matplotib.pyplot(), we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a two-dimensional, size-mutable, potentially heterogeneous tabular data.Plot the dataframe instance with bar class by name and legend is True.To display the figure, use show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() df = pd.DataFrame({'Numbers': [3, 4, 1, 7, 8, 5], 'Frequency': [2, 4, 1, 4, 3, 2]}) df.plot(ax=ax, kind='bar', legend=True) plt.show()Output
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