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Matplotlib Articles
Page 28 of 91
How to modify the font size in Matplotlib-venn?
To modify the font size in Matplotlib-venn, we can use the set_fontsize() method on the text objects returned by the venn diagram functions. This allows you to customize both set labels and subset labels independently. Installation First, ensure you have the required library installed ? pip install matplotlib-venn Basic Example Here's how to create a 3-set Venn diagram with custom font sizes ? from matplotlib import pyplot as plt from matplotlib_venn import venn3 plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True set1 = {'a', 'b', 'c', 'd'} set2 = {'a', ...
Read MoreHow to plot a non-square Seaborn jointplot or JointGrid? (Matplotlib)
Seaborn's jointplot() creates square plots by default, but you can create non-square (rectangular) plots by adjusting the figure dimensions using set_figwidth() and set_figheight() methods. Basic Non-Square Jointplot Here's how to create a rectangular jointplot by modifying the figure dimensions ? import seaborn as sns import numpy as np import matplotlib.pyplot as plt import pandas as pd # Generate sample data np.random.seed(42) x_data = np.random.randn(1000) y_data = 0.2 * np.random.randn(1000) + 0.5 # Create DataFrame df = pd.DataFrame({'x': x_data, 'y': y_data}) # Create jointplot jp = sns.jointplot(x="x", y="y", data=df, height=4, ...
Read MoreHow to add a legend on Seaborn facetgrid bar plot using Matplotlib?
Adding a legend to a Seaborn FacetGrid bar plot requires using map_dataframe() with the plotting function and then calling add_legend(). This approach works with various plot types including bar plots, scatter plots, and line plots. Basic FacetGrid with Legend Let's start with a simple example using sample data ? import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # Set figure parameters plt.rcParams["figure.figsize"] = [10, 4] plt.rcParams["figure.autolayout"] = True # Create sample data df = pd.DataFrame({ 'category': ['A', 'B', 'C', 'A', 'B', 'C'], ...
Read MoreHow to retrieve the list of supported file formats for Matplotlib savefig()function?
To retrieve the list of supported file formats for Matplotlib's savefig() function, we can use the get_supported_filetypes() method. This is useful when you want to programmatically check which formats are available for saving plots. Using get_supported_filetypes() The get_supported_filetypes() method returns a dictionary where keys are file extensions and values are format descriptions ? import matplotlib.pyplot as plt # Get current figure and access canvas fig = plt.gcf() supported_formats = fig.canvas.get_supported_filetypes() # Display all supported formats for extension, description in supported_formats.items(): print(f"{extension}: {description}") eps: Encapsulated Postscript jpg: Joint ...
Read MoreHow to make an arrow that loops in Matplotlib?
To make an arrow that loops in Matplotlib, we can combine a circular arc with an arrowhead. This creates a visual loop with directional indication, useful for representing cyclic processes or feedback loops in diagrams. Creating the Loop Function We'll create a custom function that combines a wedge (for the circular part) and a polygon (for the arrow) ? from matplotlib import pyplot as plt, patches, collections plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True def make_loop(ax, center, radius, theta1=-30, theta2=180): # Create the circular arc rwidth ...
Read MoreHow to change the datetime tick label frequency for Matplotlib plots?
When plotting time series data in Matplotlib, you often need to control how datetime tick labels appear on the x-axis. By default, Matplotlib may create too many or too few tick labels, making the plot hard to read. You can customize the frequency using locators and formatters from the matplotlib.dates module. Basic Datetime Tick Control Here's how to create a time series plot and control the datetime tick frequency ? import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates # Set figure size plt.rcParams["figure.figsize"] = [10, 6] plt.rcParams["figure.autolayout"] ...
Read MoreHow to add a legend to a Matplotlib pie chart?
A legend in a Matplotlib pie chart helps identify different sections by mapping colors to category names. You can add a legend using the legend() method with the patches returned by pie(). Basic Pie Chart with Legend Create a simple pie chart and add a legend using patches ? import matplotlib.pyplot as plt # Data for the pie chart labels = ['Walk', 'Talk', 'Sleep', 'Work'] sizes = [23, 45, 12, 20] colors = ['red', 'blue', 'green', 'yellow'] # Create pie chart and get patches patches, texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90) # Add ...
Read MoreHow to make more than 10 subplots in a figure using Matplotlib?
Creating more than 10 subplots in a single figure is common when visualizing multiple datasets or comparing different plots. Matplotlib's subplots() function makes this straightforward by arranging plots in a grid layout. Basic Grid Layout Use nrows and ncols parameters to create a grid of subplots ? import matplotlib.pyplot as plt import numpy as np # Set figure size for better visibility plt.rcParams["figure.figsize"] = [12, 8] plt.rcParams["figure.autolayout"] = True # Create 4x3 grid (12 subplots) rows = 4 cols = 3 fig, axes = plt.subplots(nrows=rows, ncols=cols) # Add sample plots to each ...
Read MoreHow to plot a jointplot with 'hue' parameter in Seaborn? (Matplotlib)
To create a jointplot with the hue parameter in Seaborn, we can use sns.jointplot() to visualize the relationship between two variables while coloring points by a third categorical variable. Basic Jointplot with Hue The hue parameter allows us to color-code data points based on different categories ? import matplotlib.pyplot as plt import pandas as pd import seaborn as sns import numpy as np # Set figure parameters plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create sample data x = np.linspace(0, 1, 5) # Create dictionary with curve data data_dict = { ...
Read MoreHow to annotate the end of lines using Python and Matplotlib?
To annotate the end of lines using Python and Matplotlib, we can add text labels at the end points of plotted lines. This is particularly useful for line charts where you want to identify each line without relying solely on legends. Steps to Annotate Line Endpoints Set the figure size and adjust the padding between and around the subplots Initialize a variable, rows, to get the number of rows data Get a Pandas dataframe with rectangular tabular data Calculate the cumsum (cumulative sum) of the dataframe Plot the dataframe using plot() method Iterate through line and name ...
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