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Articles by Rishikesh Kumar Rishi
Page 54 of 102
How to repress scientific notation in factorplot Y-axis in Seaborn / Matplotlib?
To repress scientific notation in factorplot Y-axis in Seaborn/Matplotlib, we can use style="plain" in ticklabel_format()method.StepsSet the figure size and adjust the padding between and around the subplots.Make a dataframe with keys, col1 and col2.The factorplot() has been renamed to catplot().To repress the scientific notation, use style="plain" in ticklabel_format() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import pandas as pd import seaborn as sns plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({"col1": [1, 3, 5, 7, 1], "col2": [1, 5, 7, 9, 1]}) sns.catplot(y="col1", x="col2", kind='bar', data=df, label="Total", height=3.5) plt.ticklabel_format(style='plain', ...
Read MoreHow to make axes transparent in Matplotlib?
To make axes transparent 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 '~.axes.Axes' to the figure as part of a subplot arrangement.Set face color of the current axes.Add an axes to the figure.Create t and s data using numpy.Plot t and s data points using plot() method on axis 2 (from step 5).To make the axis transparent, use set_alpha() method and keep alpha value minimum.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import ...
Read MoreHow to name different lines in the same plot of Matplotlib?
To name different lines in the same plot of matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make two lists of data points.Plot point1 and point2 using plot() method.Place a legend on the figure.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 points1 = [2, 4, 1, 5, 1] points2 = [3, 2, 0, 4, 3] plt.plot(points1, 'g--', label="plot A") plt.plot(points2, 'r-o', label="plot A") plt.legend() plt.show()Output
Read MoreDrawing circles on an image with Matplotlib and NumPy
To draw a circle on an image with matplotlib and numpy, 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.Create x and y data points using numpy.Create a figure and a set of subplots using subplots() method.Display data as an image, i.e., on a 2D regular raster using imshow() method.Turn off the axes.Add patches on the current axes.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Circle plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = ...
Read MoreHow to change the legend fontname in Matplotlib?
To change the legend fontname in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x data points using numpy.Plot x, sin(x) and cos(x) using plot() method.Use legend() method to place the legend.Iterate legend.get_texts() and update the legend fontname.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(-5, 5, 100) plt.plot(x, np.sin(x), label="$y=sin(x)$") plt.plot(x, np.cos(x), label="$y=cos(x)$") legend = plt.legend(loc='upper right') i = 1 for t in legend.get_texts(): ...
Read MoreAdding units to heatmap annotation in Seaborn
To add units to a heatmap annotation in Seaborn, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a 5×5 dimension matrix using numpy.Plot rectangular data as a color-encoded matrix.Annotate heatmap value with %age unit.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import seaborn as sns import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = np.random.rand(5, 5) ax = sns.heatmap(data, annot=True, fmt='.1f', square=1, linewidth=1.) for t in ax.texts: t.set_text(t.get_text() + " %") plt.show()Output
Read MoreHow to color a Matplotlib scatterplot using a continuous value?
To color a matplotlib scatterplot using continuous value, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and z random data points using numpy.Create a figure and a set of subplots.Create a scatter plot.Draw a colorbar in an existing axes, with scatter points scalar mappable instance.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, y, z = np.random.rand(3, 50) f, ax = plt.subplots() points = ax.scatter(x, y, c=z, s=50, cmap="plasma") f.colorbar(points) ...
Read MoreText alignment in a Matplotlib legend
To make text alignment in a matplotlib legend, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x data points using numpy.Plot x, sin(x) and cos(x) using plot() method.Place legend using legend() method and initialize a method.Iterate the legend.get_texts() method to set the horizontal alignment.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(-5, 5, 100) plt.plot(x, np.sin(x), label="$y=sin(x)$") plt.plot(x, np.cos(x), label="$y=cos(x)$") legend = plt.legend(loc='upper right') for t in ...
Read MorePlot Matplotlib 3D plot_surface with contour plot projection
To plot 3d plot_surface with contour plot projection, we can use plot_surface() and contourf() methods.StepsSet the figure size and adjust the padding between and around the subplots.Create x, y, X, Y and Z data points using numpy.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, with 3D projection.Use plot_surface() method to create a surface plot.Create a 3D filled contour plotm using contourf() method.Trurn off the axes.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] ...
Read MoreHow to add Matplotlib Colorbar Ticks?
To add ticks to the colorbar, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and z data points using numpy.Use imshow() method to display the data as an image, i.e., on a 2D regular raster.Create ticks using numpy in the range of min and max of z.Create a colorbar for a ScalarMappable instance, *mappable*, with ticks=ticks.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, y = np.mgrid[-1:1:100j, -1:1:100j] z = (x + ...
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