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Articles on Trending Technologies
Technical articles with clear explanations and examples
Exporting an svg file from a Matplotlib figure
To export an SVG file from a matplotlib figure, we can use the savefig() method with the .svg format. SVG (Scalable Vector Graphics) files are ideal for plots as they maintain quality at any zoom level and are perfect for web publishing. Steps to Export SVG File Set the figure size and adjust the padding between and around the subplots. Create a figure and a set of subplots. Create random x and y data points using numpy. Plot x and y data points using plot() method. Save the .svg format file using savefig() method. Example ...
Read MorePlot numpy datetime64 with Matplotlib
To plot a time series in Python using matplotlib with numpy datetime64, we need to create datetime arrays and plot them against numerical values. This is commonly used for visualizing time-based data like stock prices, sensor readings, or any data that changes over time. Basic Steps Create x points using numpy datetime64 arrays Create corresponding y points with numerical data Plot the datetime x-axis against y values using plot() method Display the figure using show() method Example Let's create a simple time series plot with hourly data for a single day ? ...
Read MoreHow to give sns.clustermap a precomputed distance matrix in Matplotlib?
To use sns.clustermap with a precomputed distance matrix in Matplotlib, you need to pass your distance matrix to the row_linkage and col_linkage parameters. This allows you to provide custom clustering results instead of letting seaborn compute distances automatically. Creating a Basic Clustermap with Default Distance First, let's see how clustermap() works with default distance calculation ? import matplotlib.pyplot as plt import seaborn as sns import numpy as np from scipy.cluster.hierarchy import linkage from scipy.spatial.distance import pdist plt.rcParams["figure.figsize"] = [10, 6] sns.set_theme(color_codes=True) # Load example dataset iris = sns.load_dataset("iris") species = iris.pop("species") # Create ...
Read MoreHow to sharex when using subplot2grid?
When using subplot2grid, you can share the x-axis between subplots by passing the sharex parameter. This creates linked subplots where zooming or panning one plot affects the other. Steps to Share X-axis Create random data using numpy Create a figure using figure() method Create the first subplot with subplot2grid() Create the second subplot with sharex=ax1 parameter Plot data on both subplots Use tight_layout() to adjust spacing Display the figure with show() Example Here's how to create subplots with shared x-axis using subplot2grid ? import numpy as np import matplotlib.pyplot as plt ...
Read MoreHow to add a text into a Rectangle in Matplotlib?
To add text into a rectangle in Matplotlib, you can use the annotate() method to place text at the center point of the rectangle. This technique is useful for creating labeled diagrams, flowcharts, or annotated visualizations. Steps Create a figure using figure() method Add a subplot to the current figure Create a rectangle using patches.Rectangle() class Add the rectangle patch to the plot Calculate the center coordinates of the rectangle Use annotate() method to place text at the center Set axis limits and display the plot Example Here's how to create a rectangle with ...
Read MoreHow to get the center of a set of points using Python?
To get the center of a set of points, we calculate the centroid by finding the average of all x-coordinates and y-coordinates. This gives us the geometric center of the point cloud. Method: Calculate Arithmetic Mean The center (centroid) is calculated as ? Center X = sum(all x coordinates) / number of points Center Y = sum(all y coordinates) / number of points Example Let's create a scatter plot and mark the center point ? import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True ...
Read MoreHow do I set color to Rectangle in Matplotlib?
In Matplotlib, you can set colors to rectangles using the patches.Rectangle class. This allows you to customize both the edge color and fill color of rectangular shapes in your plots. Basic Rectangle with Colors Here's how to create a colored rectangle using edgecolor and facecolor parameters − import matplotlib.pyplot as plt import matplotlib.patches as patches # Create figure and axis fig, ax = plt.subplots(figsize=(7, 4)) # Create a rectangle with custom colors rectangle = patches.Rectangle((1, 1), 3, 2, ...
Read MoreHow to scale axes in Mplot3d?
To scale axes in Matplotlib's 3D plotting, you need to control the range of each axis using specific methods. This allows you to zoom into particular regions or adjust the proportions of your 3D visualization. Steps to Scale 3D Axes Create a figure using figure() method Create a 3D axes instance using Axes3D() class Use set_xlim3d() to scale the X-axis range Use set_ylim3d() to scale the Y-axis range Use set_zlim3d() to scale the Z-axis range Display the plot using show() method Basic Example Here's how to create a 3D plot with scaled axes ? ...
Read MoreHow to increase the font size of the legend in my Seaborn plot using Matplotlib?
When creating Seaborn plots, you might need to increase the legend font size for better readability. Seaborn uses Matplotlib under the hood, so we can use plt.legend() with the fontsize parameter to control legend text size. Basic Example Let's create a DataFrame and plot a bar chart with custom legend font size ? import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # Set figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Create sample data df = pd.DataFrame({ 'number': [2, 5, 1, 6, 3], ...
Read MoreHow to adjust the size of a Matplotlib legend box?
To adjust the size of a Matplotlib legend box, we can use the borderpad parameter in the legend() method. This parameter controls the whitespace inside the legend border, effectively changing the legend box size. Basic Legend with Default Size Let's first create a simple plot with a default legend ? import matplotlib.pyplot as plt # Create sample data line1, = plt.plot([1, 5, 1, 7], linewidth=1.0, label='Line 1') line2, = plt.plot([5, 1, 7, 1], linewidth=2.0, label='Line 2') # Add legend with default size plt.legend() plt.title('Default Legend Size') plt.show() Adjusting Legend Size with borderpad ...
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