To create a Swarm Plot with Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a Pandas dataframe, i.e., a two-dimensional, size-mutable, potentially heterogeneous tabular data.Initialize the plotter, swarmplot.To plot the boxplot, use boxplot() method.To display the figure, use show() method.Exampleimport seaborn as sns import matplotlib.pyplot as plt import pandas as pd import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = pd.DataFrame({"Box1": np.arange(10),                      "Box2": np.arange(10)}) ax = sns.swarmplot(x="Box1", y="Box2", data=data, zorder=0) ... Read More

To display the matrix value and colormap in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Initialize max and min values for matrix.Plot the values of a 2D matrix or array as color-coded image.Iterate each cell of the color-code image and place value at the center.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, ax = plt.subplots() min_val, max_val = 0, 5 matrix = np.random.randint(0, 5, size=(max_val, ... Read More

To annotate a range of the X-axis in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create xx and yy data points using numpy.Create a figure and a set of subplots.Plot xx and yy data points using plot() method.Set ylim of the axis.Use annotate method to place arrow heads and range tag name.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 xx = np.linspace(0, 10) yy = np.sin(xx) fig, ax = plt.subplots(1, 1) ... Read More

To add annotated text in Matplotlib for several points, 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.To set the label for each scattered point, make a list of labels.Plot xpoints, ypoints using scatter() method. For color, use xpoints.Iterate zipped labels, xpoints and ypoints.Use annotate() method with bold LaTeX representation in a for loop.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 xpoints = np.linspace(1, 10, 10) ... Read More

To plot a line in Matplotlib with an interval at each data point, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make an array of means and standard deviations.Plot means using plot() method.Fill the area between means+stds and means-stds, alpha=0.7 and color='yellow'.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 means = np.array([3, 5, 1, 8, 4, 6]) stds = np.array([1.3, 2.6, 0.78, 3.01, 2.32, 2.9]) plt.plot(means, color='red', lw=7) plt.fill_between(range(6), means - stds, means ... Read More

To plot CSV data using Matplotlib and Pandas in Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a list of headers of the .CSV file.Read the CSV file with headers.Set the index and plot the dataframe.To display the figure, use show() method.Exampleimport pandas as pd import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True headers = ['Name', 'Age', 'Marks'] df = pd.read_csv('student.csv', names=headers) df.set_index('Name').plot() plt.show()Output

To create a line chart using matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make lists of years and population growth.Plot years and population on the line using plot() method.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 years = [1901, 1911, 1921, 1931, 1941, 1951,         1961, 1971, 1981, 1991, 2001, 2011] population = [237.4, 238.4, 252.09, 251.31, 278.98, 318.66, 361.09, 439.23, 548.16, 683.33, 846.42, 1028.74] plt.plot(years, population, color='red', marker='o') plt.show()Output

To use different markers for different points in a Pylab (Pyplot) scatter plot, we can use the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of sample data.Create x and y random data points.Make a list of markers.Zip the x, y and markers.Iterate the zipper objects and plot the data points with different markers.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 N = 10 x = np.random.rand(N) y = np.random.rand(N) ... Read More

To slice an image into Red, Green and Blue channels with misc.imread, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Read an image from a file into an array.Make lists of colormaps and titles.Create a figure and a set of subplots.Zip the axes, images, titles and colormaps.Iterate zipped objs and set the title of each channel image.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 image = plt.imread('bird.png') titles = ['With red channel', 'With green channel', 'With blue channel'] cmaps ... Read More

To add a second X-axis at the bottom of the first one in Matplotlib, we can take the followingStepsSet the figure size and adjust the padding between and around the subplots.Get the current axis (ax1) using gca() method.Create a twin axis (ax2) sharing the Y-axis.Set X-axis ticks at AxisSet X-axis labels at Axis 1 andTo display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ax1 = plt.gca() ax2 = ax1.twiny() ax2.set_xticks([1, 2, 3, 4, 5]) ax1.set_xlabel("X-axis 1") ax2.set_xlabel("X-axis 2") plt.show()Output