To insert a scale bar in a map in matplotlib, we can use AnchoredBar() class to instantiate the scalebar object.StepsSet the figure size and adjust the padding between and around the subplots.Create random data using numpy.Use imshow() method to display data as an image, i.e., on a 2D regular raster.Get the current axis using gca() method.Draw a horizontal scale bar with a center-aligned label underneath.Add a scalebar artist to the current axis.Turn off the axes.To display the figure, use show() mthod.Examplefrom matplotlib import pyplot as plt from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] ... Read More

To plot gamma distribution with alpha and beta parameters in Python, we can use gamma.pdf() function.StepsSet the figure size and adjust the padding between and around the subplots.Create x using numpy and y using gamma.pdf() function at x of the given RV.Plot x and y data points using plot() method.Use legend() method to place the legend elements for the plot.To display the figure, use show() method.Exampleimport numpy as np import scipy.stats as stats from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(0, 10, 10) y = stats.gamma.pdf(x, a=5, scale=0.333) plt.plot(x, ... Read More

To change the fontsize of scientific notation in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a list of x and y values.Plot x and y data points using plot() method.To change the font size of scientific notation, we can use style="sci" 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 x = [10000, 20000, 300000, 34, 1, 10000] y = [1, 2, 0, 4, 1, 5] plt.plot(x, y, color='red') plt.ticklabel_format(axis="x", style="sci", scilimits=(0, ... Read More

To plot hatches bars using Pandas, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a dataframe using Pandas with two columns.Add an axes to the current figure as a subplot arrangement.Make a plot with kind="bars" class by name.Make a list of hatches.Get the bars patches using bars.patches.Iterate bars patches and set the hatch of each patch.To display the figure, use show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame(np.random.rand(5, 2), columns=['a', ... Read More

To surface plot/3d, we would require 2D data points, not 1D dataframe.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 '~.axes.Axes' to the figure as part of a subplot arrangement using add_subplot() method.Initialize a variable 'n' for the number of samples.Create x, y and z data points using numpy.Use plot_surface() method to make surface 3d.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 show different colors for points and line in a Seaborn regplot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a Pandas dataframe with key X-axis and Y-axis.Plot numeric independent variables with regression model.To display the figure, use show() method.Exampleimport pandas import matplotlib.pylab as plt import seaborn as sns import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pandas.DataFrame({"X-Axis": [np.random.randint(5) for i in range(10)], "Y-Axis": [np.random.randint(5) for i in range(10)]}) sns.regplot(x='X-Axis', y='Y-Axis', data=df, scatter_kws={"color": "red"}, line_kws={"color": "green"}) plt.show()OutputRead More

To render 3D histograms in Python, 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 axes to the cureent figure as a subplot arrangement.Create x3, y3 and z3 data points using numpy.Create dx, dy and dz data points using numpy.Use bar3d() method to plot 3D bars.To hide the axes use axis('off') class by name.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 ... Read More

To add a legend to a matplotlib boxplot with multiple plots on the same axis, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data, a and b, using numpy.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 with different facecolors.To place the legend, use legend() method with two boxplots, bp1 and bp2, and ordered label for legend elements.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt ... Read More

To plot data against specific dates on the X-axis using matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a list of dates and convert them in datetime format as x.Make a list of y data points.Set the formatter of the major ticker.Set the locator of the major ticker.Plot x and y data points using plot() method.To display the figure, use show() method.Examplefrom datetime import datetime from matplotlib import pyplot as plt, dates as mdates plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True dates = ["01/02/2021", "01/03/2021", "01/04/2021", ... Read More

To draw an average line for a plot in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make x and y data points using numpy.Use subplots() method to create a figure and a set of subplots.Use plot() method for x and y data points.Find the average value of the array, x.Plot x and y_avg data points using plot() method.Place a legend on the figure.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 ... Read More