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To plot the function y=1/x as a single graph in Python, we use matplotlib and numpy libraries. The hyperbola y=1/x has two branches separated by asymptotes at x=0 and y=0. Steps to Plot y=1/x Set the figure size and adjust the padding between and around the subplots Create data points using numpy, excluding x=0 to avoid division by zero Plot x and 1/x data points using plot() method Add labels and legend for better visualization Display the figure using show() method Example Here's how to plot the hyperbola y=1/x with proper handling of the ... Read More
In Matplotlib, you can control the spacing between tick labels and axis labels using the labelpad parameter. This parameter adjusts the distance between the axis label and the tick labels, giving you precise control over your plot's appearance. Syntax plt.xlabel("label_text", labelpad=distance) plt.ylabel("label_text", labelpad=distance) The labelpad parameter accepts a numeric value representing the distance in points between the axis label and tick labels. Basic Example Here's how to adjust the separation between tick labels and axis labels ? import matplotlib.pyplot as plt # Set figure size plt.figure(figsize=(8, 5)) # Create ... Read More
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 More
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 More
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 More
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 More
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 More
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 More
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 More
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 More