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Matplotlib Articles
Page 4 of 91
How to fill an area within a polygon in Python using matplotlib?
Matplotlib provides several ways to fill areas within polygons. The most common approaches are using Polygon patches, fill() method, or PatchCollection for multiple polygons. Method 1: Using fill() Method The simplest way to fill a polygon is using matplotlib's fill() method ? import matplotlib.pyplot as plt import numpy as np # Define polygon vertices (triangle) x = [1, 4, 2] y = [1, 2, 4] plt.figure(figsize=(8, 6)) plt.fill(x, y, color='lightblue', alpha=0.7, edgecolor='blue') plt.title('Filled Triangle Polygon') plt.grid(True, alpha=0.3) plt.show() Method 2: Using Polygon Patch For more control over polygon properties, use Polygon ...
Read MoreHow to get data labels on a Seaborn pointplot?
To get data labels on a Seaborn pointplot, you need to access the plotted points and add annotations manually using matplotlib's annotate() function. This technique helps display exact values on each data point for better visualization. Steps Set the figure size and adjust the padding between and around the subplots. Create a DataFrame with sample data for visualization. Create a pointplot using Seaborn. Iterate through the plot points and add data labels using annotations. Display the figure using show() method. ...
Read MoreHow to draw a precision-recall curve with interpolation in Python Matplotlib?
A precision-recall curve is a fundamental evaluation metric for binary classification models. With interpolation, we create a monotonically decreasing curve that shows the trade-off between precision and recall at different thresholds. Understanding Precision-Recall Curves In machine learning, precision measures the accuracy of positive predictions, while recall measures the completeness of positive predictions. The interpolated curve ensures that precision values are monotonically decreasing as recall increases. Creating Sample Data First, let's generate sample recall and precision data points ? import numpy as np import matplotlib.pyplot as plt # Set figure parameters plt.rcParams["figure.figsize"] = [7.50, ...
Read MoreHow to plot additional points on the top of a scatter plot in Matplotlib?
Matplotlib allows you to add additional points on top of existing scatter plots. This is useful for highlighting specific data points or overlaying different datasets with distinct markers. Steps Create initial scatter plot with base data points Use plt.plot() or plt.scatter() to add additional points Customize markers using marker, markersize, and color parameters Display the plot using show() method Basic Example Here's how to add star markers on top of a scatter plot ? import matplotlib.pyplot as plt # Base data points x = [1, 2, 6, 4] y = [1, ...
Read MoreTransparent error bars without affecting the markers in Matplotlib
To make transparent error bars without affecting markers in matplotlib, you need to modify the alpha transparency of the error bar components while keeping the markers opaque. Steps Set the figure size and adjust the padding between and around the subplots. Create data lists for x, y coordinates and error values. Initialize error bar width parameter. Plot data with error bars using errorbar() method. Set the alpha transparency for bars and caps separately. Display the figure using show() method. Example Here's how to create transparent error bars while keeping markers fully visible ? ...
Read MoreHow to set legend marker size and alpha in Matplotlib?
In Matplotlib, you can customize the appearance of legend markers by adjusting their size and transparency (alpha) independently from the plot markers. This is useful when you want the legend to be more readable while maintaining the original plot styling. Setting Legend Marker Properties After creating a legend, access the legend handles and modify the marker properties using _legmarker attributes ? import matplotlib.pyplot as plt import numpy as np # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Generate sample data N = 10 x = np.random.rand(N) y = np.random.rand(N) ...
Read MoreShowing points coordinate in a plot in Python Matplotlib
To show point coordinates in a plot in Python Matplotlib, we can use the annotate() method to add text labels at specific positions. This technique is useful for displaying exact coordinate values directly on the plot. Steps Set the figure size and adjust the padding between and around the subplots. Initialize a variable N and create x and y data points using NumPy. Plot the points using plot() method. Zip the x and y data points; iterate them and place coordinate annotations. Display the figure using show() method. Example import matplotlib.pyplot as plt ...
Read MoreHow to unset 'sharex' or 'sharey' from two axes in Matplotlib?
When creating multiple subplots in Matplotlib, you might want each subplot to have independent X and Y axes instead of sharing them. You can unset sharex and sharey by setting them to 'none' or False. Basic Syntax To create subplots without shared axes − fig, axes = plt.subplots(rows, cols, sharex='none', sharey='none') # or fig, axes = plt.subplots(rows, cols, sharex=False, sharey=False) Example with Independent Axes Let's create a 2x4 grid of subplots where each has independent X and Y axes − import matplotlib.pyplot as plt import numpy as np # Set ...
Read MoreHow to obtain 3D colored surface via Python?
To create a 3D colored surface plot in Python, we use matplotlib's 3D plotting capabilities with numpy for data generation. The surface colors are mapped based on z-values using colormaps. Steps to Create 3D Colored Surface Set the figure size and adjust the padding between and around the subplots. Create x and y data points using numpy. Generate 3D data (z values) based on x and y coordinates. Create a new figure or activate an existing figure. Get the 3D axes using ...
Read MoreScatter a 2D numpy array in matplotlib
Creating scatter plots from 2D NumPy arrays is a common visualization task in data analysis. Matplotlib's scatter() function can effectively plot multi-dimensional data using different columns for x, y coordinates, and colors. Basic Scatter Plot from 2D Array Let's start with a simple example using the first two columns of a 2D array ? import numpy as np import matplotlib.pyplot as plt # Create a 2D array with random data data = np.random.random((50, 3)) # Scatter plot using first two columns plt.figure(figsize=(8, 6)) plt.scatter(data[:, 0], data[:, 1]) plt.xlabel('X values (Column 0)') plt.ylabel('Y values (Column ...
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