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Python Articles
Page 450 of 855
How to adjust the branch lengths of a dendrogram in Matplotlib?
To adjust the branch lengths of a dendrogram in Matplotlib, you need to understand that branch lengths represent the distance between clusters. You can control this by modifying the linkage method, distance metric, or by manipulating the dendrogram parameters. Basic Dendrogram Creation First, let's create a simple dendrogram with default settings ? import matplotlib.pyplot as plt from scipy.cluster.hierarchy import dendrogram, linkage import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Generate sample data a = np.random.multivariate_normal([0, 10], [[3, 1], [1, 4]], size=[2, ]) b = np.random.multivariate_normal([0, 10], [[3, 1], [1, 4]], ...
Read MoreHow to add bold annotated text in Matplotlib?
To add bold annotated text in Matplotlib, we can use LaTeX representation with \bf{} command. This is useful for emphasizing important labels or annotations in data visualizations. Basic Bold Annotation Syntax The key is using LaTeX formatting within the annotate() method ? import matplotlib.pyplot as plt plt.figure(figsize=(8, 6)) plt.plot([1, 2, 3], [1, 4, 2]) plt.annotate(r'$\bf{Bold\ Text}$', xy=(2, 4), xytext=(2.5, 3), arrowprops=dict(arrowstyle='->', color='red')) plt.title('Bold Annotation Example') plt.show() Complete Example with Scatter Plot Here's how to add bold annotations to multiple data ...
Read MoreHow to plot half or quarter polar plots in Matplotlib?
To plot half or quarter polar plots in Matplotlib, we can control the angular range using the set_thetamax() and set_thetamin() methods. This allows us to create partial polar plots that show only specific angular segments. Steps to Create Partial Polar Plots 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 to the figure as part of a subplot arrangement with projection="polar". For half or quarter polar plots, use set_thetamax() method to limit the maximum angle. Optionally use set_thetamin() ...
Read MoreHow to plot a point on 3D axes in Matplotlib?
Matplotlib allows you to create 3D plots and visualize points in three-dimensional space. To plot a point on 3D axes, you need to use the scatter() method with projection='3d'. Basic 3D Point Plotting Here's how to plot a single point in 3D space ? import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create figure with 3D projection fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # Plot a point at coordinates (2, 3, 4) ax.scatter(2, 3, 4, c='red', marker='*', s=1000) # Add labels ax.set_xlabel('X axis') ...
Read MoreHow to display a Dataframe next to a Plot in Jupyter Notebook?
In Jupyter Notebook, you can display a DataFrame and a plot side by side using matplotlib's subplot functionality. This creates a professional-looking visualization that combines both tabular and graphical data representation. Steps to Display DataFrame Next to Plot Set the figure size and adjust the padding between and around the subplots Create a Pandas DataFrame with sample data Create a figure with two subplots using add_subplot() Plot the data in the first subplot using scatter() method Display the DataFrame as a table in the second subplot using table() method Turn off axes for the table subplot for ...
Read MorePlotting histograms against classes in Pandas / Matplotlib
To plot histograms against classes in Pandas/Matplotlib, we can use the hist() method to visualize the distribution of values across different columns (classes) in a DataFrame. This is useful for comparing data distributions side by side. Basic Histogram Plotting Here's how to create histograms for multiple columns in a DataFrame ? import matplotlib.pyplot as plt import pandas as pd # Set figure size for better visualization plt.rcParams["figure.figsize"] = [10, 6] plt.rcParams["figure.autolayout"] = True # Create a sample DataFrame with different classes df = pd.DataFrame({ 'Class_A': [1, 2, 2, 3, 4, ...
Read MoreHow to plot a Bar Chart with multiple labels in Matplotlib?
To plot a bar chart with multiple labels in Matplotlib, we can create grouped bars with data labels. This technique is useful for comparing values across different categories and groups. Basic Grouped Bar Chart First, let's create the data and set up the basic grouped bar chart ? import matplotlib.pyplot as plt import numpy as np # Sample data for comparison men_means, men_std = (20, 35, 30, 35, 27), (2, 3, 4, 1, 2) women_means, women_std = (25, 32, 34, 20, 25), (3, 5, 2, 3, 3) # Set up the positions and width ...
Read MoreRotating axes label text in 3D Matplotlib
To rotate axes label text in 3D matplotlib, we can use the set_xlabel(), set_ylabel(), and set_zlabel() methods with the rotation parameter to control the orientation of axis labels. Basic Z-axis Label Rotation The most common case is rotating the Z-axis label, which can overlap with tick labels in 3D plots ? import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np # Set figure size plt.rcParams["figure.figsize"] = [8, 6] plt.rcParams["figure.autolayout"] = True # Create 3D plot fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # Add some sample data x = np.random.randn(100) ...
Read MoreDrawing multiple legends on the same axes in Matplotlib
To draw multiple legends on the same axes in Matplotlib, we can create separate legends for different groups of lines and position them at different locations on the plot. Steps to Create Multiple Legends Set the figure size and adjust the padding between and around the subplots Plot lines using different labels, linewidth and linestyle Create the first legend and place it at the upper-right location Add the first legend as an artist to the current axis using add_artist() Create the second legend and place it at the lower-right location Display the figure using show() method ...
Read MoreBest way to plot an angle between two lines in Matplotlib
The best way to plot an angle between two lines in Matplotlib is to use the Arc class to create an angular arc that visually represents the angle between two intersecting lines. Basic Approach To plot an angle between two lines, we need to: Calculate the slopes of both lines Convert slopes to angles using math.atan() Create an Arc patch using the calculated angles Add the arc to the plot using add_patch() Complete Example Here's a complete implementation that creates two lines and displays the angle between them ‐ from matplotlib ...
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