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Articles by Rishikesh Kumar Rishi
Page 46 of 102
Calculate the curl of a vector field in Python and plot it with Matplotlib
To calculate the curl of a vector field in Python and plot in with Matplotlib, we can use quiver() method and calculate the corresponding data.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 a 3D axes to the figure as part of a subplot arrangement.Create x, y and z data points using numpy meshgrid.Create u, v and w data curl vector positions.Use quiver() method to get vectors.Turn off the axes.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] ...
Read MoreHow can you clear a Matplotlib textbox that was previously drawn?
To clear a Matplotlib textbox that was previously drawn, 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.Plot x and y using plot() method.Place characters token on the plot.To clear the text, use text.remove(), where text is a returned artist.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, ax = plt.subplots() x = np.linspace(-10, 10, 100) y = np.sin(x) ax.plot(x, y) text = fig.text(0.5, 0.96, ...
Read MoreHow to assign specific colors to specific cells in a Matplotlib table?
To assign specific colors to specific cells in a Matplotlib table, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a tuple for columns attribute.Make a list of lists, i.e., list of records.Make a list of lists, i.e., color of each cell.Create a figure and a set of subplots.Add a table to an axes ax.Turn off the axes.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 columns = ('name', 'age', 'marks', 'salary') cell_text = [["John", "23", "98", "234"], ["James", ...
Read MoreHow to plot with different scales in Matplotlib?
To plot with different scales in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create t, data1 and data2 data points using numpyCreate a figure and a set of subplots, ax1.Initialize a color variable.Set x and y labels of axis 1.Plot t and data1 using plot() method.Set label colors using tick_params() method.Create a twin Axes sharing the X-axis, ax2.Perform steps 4, 6, 7 with a different dataset on axis 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"] ...
Read MoreHorizontal stacked bar chart in Matplotlib
To plot stacked bar chart in Matplotlib, we can use barh() methodsStepsSet the figure size and adjust the padding between and around the subplots.Create a list of years, issues_addressed and issues_pending, in accordance with years.Plot horizontal bars with years and issues_addressed data.To make stacked horizontal bars, use barh() method with years, issues_pending and issues_addressed dataPlace the legend on the plot.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 year = [2014, 2015, 2016, 2017, 2018, 2019] issues_addressed = [10, 14, 0, 10, 15, 15] issues_pending = [5, 10, 50, ...
Read MoreHow to plot true/false or active/deactive data in Matplotlib?
To plot true/false or active/deactive data in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create data using numpy with True or False.Create a new figure or activate an existing figure using figure() method.Add an '~.axes.Axes' to the figure as part of a subplot arrangement.Use imshow() method to display 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 data = np.random.random((20, 20)) > 0.5 fig = ...
Read MoreHow to view all colormaps available in Matplotlib?
To view all colormaps available 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.Add an '~.axes.Axes' to the figure as part of a subplot arrangementMake an axis that is divider on the existing axes.Create random data using numpy.Display the data as an image, i.e., on a 2D regular raster.Create a colorbar for a ScalarMappable instance, im.Set a title for the current figure.Animate the image with all colormaps available in matplotlib.Make an animation by repeatedly calling a function.To display the figure, ...
Read MoreMatplotlib colorbar background and label placement
To have colorbar background and label placement, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data using numpy.Plot the contours.With scalar mappable instance, make the colorbar.Set ticklabels for colorbar with background and label placementTo 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 data = np.linspace(0, 10, num=16).reshape(4, 4) cf = plt.contourf(data, levels=(0, 2.5, 5, 7.5, 10)) cb = plt.colorbar(cf) cb.set_ticklabels([1, 2, 3, 4, 5]) plt.show()Output
Read MoreHow to plot arbitrary markers on a Pandas data series using Matplotlib?
To plot arbitrary markers on a Pandas data series, we can use pyplot.plot() with markers.StepsSet the figure size and adjust the padding between and around the subplots.Make a Pandas data series with axis labels (including timeseries).Plot the series index using plot() method with linestyle="dotted".Use tick_params() method to rotate overlapping labels.To display the figure, use show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ts = pd.Series(np.random.randn(10), index=pd.date_range('2021-04-10', periods=10)) plt.plot(ts.index, ts, '*', ls='dotted', color='red') plt.tick_params(rotation=45) plt.show()Output
Read MoreHow to plot scatter masked points and add a line demarking masked regions in Matplotlib?
To plot scattered masked points and add a line to demark the masked regions, we can take the following steps.StepsSet the figure size and adjust the padding between and around the subplots.Create N, r0, x, y, area, c, r, area1and area2 data points using numpy.Plot x and y data points using scatter() method.To demark the maked regions, plot the curve using plot() 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 N = 100 r0 = 0.6 x = 0.9 * np.random.rand(N) y = 0.9 * ...
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