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To disable the minor ticks of a log plot in matplotlib, we can use minorticks_off() method.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Add a subplot to the current figure, at index 1.Plot x and y data points with color=red.Make x-scale as log class by name.Set the title of the current plot.Add a subplot to the current figure, at index 2.Plot x and y data points with color=green.Make x-scale as log class by name.Turn off the minor ticks of the plot.Set the title of the plot as index 2.To ... Read More
To plot distance arrows in technical drawing in matplotlib, we can use annotate() method with arrow properties.StepsSet the figure size and adjust the padding between and around the subplots.Add a horizontal line across the axis using axhline() method, i.e., y=3.5.Add a horizontal line across the axis using axhline() method, i.e., y=2.5.Use annotate() method to draw an arrow line to show the distance and in the very next statement, use annotate() method again to display the distance between two horizontal lines.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.axhline(3.5) plt.axhline(2.5) ... Read More
To set the same axis limits for all subplots in matplotlib we can use subplot() method to create 4 subplots where nrows=2, ncols=2 having share of x and y axes.StepsSet the figure size and adjust the padding between and around the subplots.Add a subplot to the current figure at index 1.Set the x and y axes view limit using set_xlim() and set_ylim() methods.Plot a line on axis 1 (step 2).Add a subplot to the current figure at index 2 with the same limit (step 3).Plot a line on axis 2.Add a subplot to the current figure at index 3 with ... Read More
To plot scatter points on a 3D projection with varying marker size, we can take the following stepsSet the figure size and adjust the padding between and around the subplots.Create xs, ys and zs data points using numpyInitialize a variable 's' for varying size of marker.Create a figure or activate an existing figure using figure() method.Add an axes to the current figure as a subplot arrangement using subplots() method.Plot the xs, ys, and zs data points using scatter() 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"] = ... Read More
To logscale plots with zero values in matplotlib, we can use xscale() and yscale() methods with "symlog" class by name.StepsSet the figure size and adjust the padding between and around the subplots.Plot two lists containing zero values using plot() method.Use yscale() method with "symlog" class by name.Use xscale() method with "symlog" class by name.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.plot([0, 1, 2, 0, 3], [1, 0, 2, 3, 5], marker='o', linestyle='-') plt.yscale('symlog') plt.xscale('symlog') plt.show()Output
To create a legend for a 3D bar in matplotlib, we can plot 3D bars and place a legend using legend() method.StepsSet the figure size and adjust the padding between and around the subplots.Create a new figure or activate an esxisting figure using figure() method.Add an axes to the figure as part of a subplot arrangement.Create a list of data x3, y3, z3, dx, dy and dz using numpy.Plot a 3D bar using bar3d() method.Create a rectangle axis for legend placement.Use legend() method to place the legend for bars.To display the figure, use show() method.Exampleimport numpy as np from matplotlib ... Read More
To plot a dashed line on a Seaborn lineplot, we can use linestyle="dashed" in the argument of lineplot().StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Use lineplot() method with x and y data points in the argument and linestyle="dashed".To display the figure, use show() method.Exampleimport seaborn as sns import numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.random.rand(10) y = np.random.rand(10) ax = sns.lineplot(x=x, y=y, linestyle="dashed") plt.show()OutputRead More
To skip weekends in a financial graph in matplotlib, we can iterate the time in dataframe and skip the plot if weekday is 5 or 6.StepsSet the figure size and adjust the padding between and around the subplots.Create a dataframe with keys time.Iterate zipped index and time of a date frame.If iterated timestamp is having weekday 5 or 6, don't plot them.Other than 5 or 6 weekday, plot the points.Set the current tick locations of Y-axis.Lay out a plot with grid lines.To display the figure, use show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, ... Read More
We can use annotate() method to place annotation outside the drawing.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create a figure and a set of subplots using subplots() method.Use scatter() method to plot x and y data points using star marker and copper color map.To place annotation outside the drawing, use xy coordinates tuple accordingly.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.random.rand(100) y = np.random.rand(100) fig, ax = plt.subplots() ax.scatter(x, y, ... Read More
To plot a histogram for discrete values with matplotlib, we can use hist() method.StepsSet the figure size and adjust the padding between and around the subplots.Make a list of discrete values.Use hist() method to plot data with bins=length of data and edgecolor=black.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = [1, 4, 2, 3, 5, 9, 6, 7] plt.hist(data, bins=len(data), edgecolor='black') plt.show()Output