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Matplotlib Articles
Page 34 of 91
How to plot a nested pie chart in Matplotlib?
A nested pie chart displays hierarchical data with an outer ring representing main categories and an inner ring showing subcategories. Matplotlib's pie() function can create concentric pie charts by adjusting the radius and width parameters. Creating a Basic Nested Pie Chart Here's how to create a nested pie chart with outer and inner rings ? import matplotlib.pyplot as plt import numpy as np # Set figure size plt.rcParams["figure.figsize"] = [8, 6] plt.rcParams["figure.autolayout"] = True # Create figure and subplot fig, ax = plt.subplots() # Define data and styling size = 0.3 vals = ...
Read MorePlotting power spectral density in Matplotlib
A Power Spectral Density (PSD) plot shows how the power of a signal is distributed across different frequencies. Matplotlib provides the psd() method to create these plots, which is useful for analyzing signal frequency content and noise characteristics. Basic PSD Plot Let's create a signal with noise and plot its power spectral density ? import matplotlib.pyplot as plt import numpy as np # Set figure parameters plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create time series data dt = 0.01 t = np.arange(0, 10, dt) nse = np.random.randn(len(t)) r = np.exp(-t / 0.05) ...
Read MoreHow to plot single data with two Y-axes (two units) in Matplotlib?
To plot single data with two Y-axes (two units) in Matplotlib, you can use twinx() to create a secondary Y-axis that shares the same X-axis. This is useful when plotting two datasets with different units or scales. Basic Setup First, let's understand the key steps ? Create the primary plot with the first dataset Use twinx() to create a twin Y-axis Plot the second dataset on the twin axis Add appropriate labels and legends Example Here's a complete example plotting speed and acceleration with different Y-axis scales ? import matplotlib.pyplot as ...
Read MoreHow to reverse the colormap of an image to scalar values in Matplotib?
To reverse the colormap of an image in Matplotlib, you can use the .reversed() method on any colormap object. This creates a new colormap with inverted colors, which is useful for changing the visual emphasis or meeting specific design requirements. Steps to Reverse a Colormap Set the figure size and adjust the padding between and around the subplots Create data points using numpy arrays Get the desired colormap using plt.cm.get_cmap() method Create subplots to compare original and reversed colormaps Plot data points using scatter() method with original colormap Plot the same data with reversed colormap using .reversed() ...
Read MoreHow to embed an interactive Matplotlib plot on a webpage?
To create an interactive plot on a webpage, we can use Bokeh, which generates HTML files with JavaScript interactivity. This allows users to pan, zoom, and interact with plots directly in their browser. Installation and Setup First, install Bokeh if you haven't already ? pip install bokeh Basic Interactive Plot Example Here's how to create a simple interactive scatter plot ? from bokeh.plotting import figure, show, output_file import numpy as np # Configure output to HTML file output_file('interactive_plot.html') # Generate sample data x = np.random.random(100) * 100 y = ...
Read MoreHow to plot a layered image in Matplotlib in Python?
To plot a layered image in Matplotlib in Python, you can overlay multiple images using the imshow() function with transparency settings. This technique is useful for creating composite visualizations or comparing datasets. Steps to Create Layered Images Set the figure size and adjust the padding between subplots Create coordinate arrays and extent data using NumPy Generate or load your image data arrays Use multiple imshow() calls with different alpha values for transparency Apply different colormaps to distinguish layers Display the final layered result Example Here's how to create a layered image with two overlapping ...
Read MoreHow to get alternating colours in a dashed line using Matplotlib?
To get alternating colors in a dashed line using Matplotlib, we can overlay two plots with different linestyles and colors. This creates a visually appealing effect where one color shows through the gaps of the dashed pattern. Steps to Create Alternating Colors Set the figure size and adjust the padding between and around the subplots Get the current axis Create x and y data points using NumPy Plot the same data twice with different linestyles: solid ("-") and dashed ("--") Use different ...
Read MoreHow to plot masked and NaN values in Matplotlib?
Matplotlib provides several approaches to handle masked and NaN values in data visualization. This is useful when you need to exclude certain data points from your plots based on specific conditions or handle missing data. Understanding Masked Arrays and NaN Values Masked arrays use NumPy's ma module to hide certain values without removing them from the dataset. NaN (Not a Number) values are treated as missing data and are automatically excluded from plot lines. Example: Plotting with Different Masking Approaches Let's create a dataset and demonstrate four different approaches to handle data exclusion ? ...
Read MoreHow to Zoom with Axes3D in Matplotlib?
Matplotlib's Axes3D allows you to create interactive 3D plots that support zooming, rotating, and panning. While basic zooming is handled automatically through mouse interaction, you can also control zoom programmatically by setting axis limits. Basic 3D Plot with Interactive Zoom Create a 3D scatter plot that supports interactive zooming ? from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = Axes3D(fig) x = [2, 4, 6, 3, 1] y = [1, 6, 8, 1, 3] z = [3, 4, 10, 3, 1] ...
Read MoreHow to move labels from bottom to top without adding "ticks" in Matplotlib?
To move labels from bottom to top without adding ticks in Matplotlib, we can use the tick_params() method to control label positioning. This is particularly useful for heatmaps where you want labels at the top for better readability. Step-by-Step Approach The process involves these key steps − Set the figure size and adjust the padding between and around the subplots Create data to visualize (we'll use a 5×5 matrix) Display the data as an image using imshow() method Use tick_params() method to move labels from bottom to top Display the figure using show() method ...
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