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To turn off the left or bottom axis tick marks in matplotlib, we can use length=0 for the axes.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot the x and y data points using plot() method.Use tick_params() method with length=0.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 x = np.linspace(-2, 2, 10) y = np.sin(x) plt.plot(x, y) plt.tick_params(axis='both', which='both', length=0) plt.show()OutputRead More
To set the spacing between grouped bar plots in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a dictionary for bar details to be plotted.Make a Pandas dataframe using dictionary, d.Plot the bar using dictionary, d, with align="center".To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import pandas as pd plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True d = {"Name": ["John", "Jacks", "James", "Joe"], "Age": [23, 12, 30, 26], "Marks": [98, 85, 70, 77]} df = pd.DataFrame(d) df.set_index('Name').plot(kind="bar", align='center', width=0.1) plt.tick_params(rotation=45) plt.show()OutputRead More
To show two different colored colormaps in the same imshow matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a 2D matrix of 5×5 dimension.Get masked matrix, data1 and data2, with positive and negative values.Create a figure and a set of subplots.Display the data as an image, i.e., on a 2D regular raster, with data1 and data2.To make two different colorbars, use colorbar method.Set the colorbar for both the images.Set the label of the colorbars.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np ... Read More
To make custom grid lines in Seaborn heatmap, we can use linewidths and linecolor values in the heatmap() method.StepsSet the figure size and adjust the padding between and around the subplots.Create a Pandas dataframe with 5 columns.Use heatmap() method to plot the rectangular data as a color-encoded matrix.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=["col1", "col2", "col3", "col4", "col5"]) sns.heatmap(df, linewidths=4, linecolor='green') plt.show()OutputRead More
To plot scatter points in a 3D figure with a colorbar in matplotlib, we can use the scatter() and colorbar() methods.StepsSet the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure using figure() method.Add an axis as a subplot arrangement.Create xs, ys and zs data points using numpy.Use scatter() method to create a scatter plot.Use colorbar() method with scatter scalar mappable instance for colorbar.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 fig = plt.figure() ax ... Read More
To plot the line color of a 3D parametric curve in Matplotlib, 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.Add an axis as a subplot arrangement.To make a parametric curve, initialize theta, z, r, x and y variables.Plot x, y and z data points using scatter() method.Set the title of the plot.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 fig = plt.figure() ax = fig.add_subplot(projection='3d') ... Read More
To show mean in a box plot, we can use showmeans=True in the argument of boxplot() method.StepsSet the figure size and adjust the padding between and around the subplots.Create a random dataset.Create a new figure or activate an existing figure using figure() method.Add an axes to the current figure as a subplot arrangement.Make a box and whisker plot using boxplot() method.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 data = np.random.rand(100, 5) fig = plt.figure() ax = fig.add_subplot(111) bp = ax.boxplot(data, 'd', showmeans=True) plt.show()OutputRead More
To shade points in a scatter based on colormap, we can use copper colormap in scatter() method.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y random 100 data points using numpy.Plot scatter points x and y with color=x and colormap=copper.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, cm plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.random.rand(100) y = np.random.rand(100) plt.scatter(x, y, c=x, cmap='copper') plt.show()Output
To use custome png or jpg i.e an image as a marker in a plot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a paths list to store the directories of images.Make a list (x and y) of points.Using subplots() method, create a figure and a set of subplots.To plot images instead of points, iterate zipped x, y and paths.Instantiate AnnotationBbox() with image and (x, y) points.Put xticks and yticks on both the axes.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib.offsetbox import OffsetImage, AnnotationBbox ... Read More
To plot a 3D continuous line in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create z data points using x and y data points.Create a new figure or activate an existing figure using figure() method.Add an axes as a subplot arrangement with 3D projection.Plot x, y and z 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 x = np.linspace(-4 * np.pi, 4 ... Read More