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Articles by Rishikesh Kumar Rishi
Page 22 of 102
Changing the color of a single X-axis tick label in Matplotlib
To change the color of a single X-axis tick label in matplotlib, you can use the tick_params() method to change all tick labels, or target specific labels using get_xticklabels() for individual customization. Method 1: Changing All X-axis Tick Labels Use tick_params() to change the color of all X-axis tick labels at once − import numpy as np import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create figure and subplot fig = plt.figure() ax = fig.add_subplot(111) # Create data x = np.linspace(-10, 10, 100) y = ...
Read MoreHow to appropriately plot the losses values acquired by (loss_curve_) from MLPClassifier? (Matplotlib)
The MLPClassifier from scikit-learn provides a loss_curve_ attribute that tracks training loss at each iteration. Plotting these values helps visualize training convergence across different hyperparameters and datasets. Understanding MLPClassifier Loss Curves The loss_curve_ attribute stores the loss function value after each iteration during training. By plotting these values, we can compare how different solvers and learning rates affect convergence behavior. Complete Example Here's how to plot loss curves for different MLPClassifier configurations across multiple datasets ‒ import warnings import matplotlib.pyplot as plt from sklearn.neural_network import MLPClassifier from sklearn.preprocessing import MinMaxScaler from sklearn import datasets ...
Read MoreHow to use Font Awesome symbol as marker in matplotlib?
Font Awesome symbols can be used as custom markers in matplotlib plots by using Unicode characters with the text() function. This approach allows you to create visually appealing plots with unique symbolic markers. Setup and Prerequisites First, configure the figure settings and import required libraries ? import numpy as np import matplotlib.pyplot as plt # Set figure size and enable automatic layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True Using Unicode Symbols as Markers Define a list of Unicode symbols and plot them using the text() function ? import numpy ...
Read MoreHow to save a Librosa spectrogram plot as a specific sized image?
Librosa is a Python package that helps to analyze audio and music files. This package also helps to create music information retrieval systems. In this article, we will see how to save a Librosa spectrogram plot as an image of specific size. Understanding Spectrogram Parameters Before creating the spectrogram, we need to understand the key parameters that control the output image dimensions ? hl (hop_length) − Number of samples per time-step in spectrogram hi (height) − Height of the output image (number of mel bins) wi (width) − Width of the output image (time frames) ...
Read MoreHow to plot an image with non-linear Y-axis with Matplotlib using imshow?
To plot an image with a non-linear Y-axis using Matplotlib's imshow() method, you need to customize the Y-axis tick positions while displaying your 2D data. This technique is useful when you want specific spacing or values on your Y-axis that don't follow a linear pattern. Step-by-Step Approach The process involves the following steps: Set the figure size and adjust the padding between and around the subplots Add a subplot to the current figure Set non-linear Y-axis ticks using custom positions Create or prepare your 2D data array Display the data as an image using imshow() Display ...
Read MoreHow to create a matplotlib colormap that treats one value specially?
To create a matplotlib colormap that treats one value specially, we can use set_under(), set_over(), or set_bad() methods to assign special colors for out-of-range or invalid values. Basic Approach Using set_under() The set_under() method assigns a special color to values below the colormap range ? import matplotlib.pyplot as plt import numpy as np # Create sample data data = np.random.randn(5, 5) eps = np.spacing(0.0) # Get colormap and set special color for low values cmap = plt.get_cmap('rainbow') cmap.set_under('red') # Create plot fig, ax = plt.subplots(figsize=(8, 6)) im = ax.imshow(data, interpolation='nearest', vmin=eps, cmap=cmap) fig.colorbar(im, ...
Read MoreHow to show a figure that has been closed in Matplotlib?
When you close a figure in Matplotlib, it's removed from memory and cannot be displayed again using the standard plt.show(). However, you can restore a closed figure by creating a new canvas manager and transferring the figure data. Understanding the Problem Once plt.close() is called on a figure, the canvas connection is broken. To display it again, we need to create a new canvas and reassign the figure to it. Example: Restoring a Closed Figure Here's how to show a figure that has been closed ? import numpy as np import matplotlib.pyplot as plt ...
Read MoreHow to set the Y-axis in radians in a Python plot?
To set the Y-axis in radians in a Python plot, we need to customize the axis ticks and labels to display radian values like π/2, π/4, etc. This is commonly needed when plotting trigonometric functions. Steps to Set Y-axis in Radians Create data points using NumPy Plot the data using matplotlib Define custom tick positions in radian units Set custom tick labels using LaTeX formatting for fractions Apply the ticks and labels using set_yticks() and set_yticklabels() Example Let's plot the arctangent function and set its Y-axis in radians ? import matplotlib.pyplot as ...
Read MoreHow can box plot be overlaid on top of swarm plot in Seaborn?
Overlaying a box plot on top of a swarm plot in Seaborn creates an effective visualization that combines individual data points with summary statistics. The swarm plot shows each data point while the box plot provides quartile information and outliers. Basic Overlay Example Here's how to create a box plot overlaid on a swarm plot using sample data − import seaborn as sns import matplotlib.pyplot as plt import pandas as pd import numpy as np # Set figure size plt.rcParams["figure.figsize"] = [8, 5] plt.rcParams["figure.autolayout"] = True # Create sample data np.random.seed(42) data = pd.DataFrame({ ...
Read MoreAdjust the width of box in boxplot in Python Matplotlib
In Python Matplotlib, you can adjust the width of boxes in a boxplot using the widths parameter in the boxplot() method. This allows you to create boxes of different sizes for better visualization and comparison. Steps Set the figure size and adjust the padding between and around the subplots Create sample data using Pandas DataFrame Use the boxplot() method with the widths parameter to adjust box dimensions Display the plot using the show() method Example Here's how to create a boxplot with different box widths ? import pandas as pd import numpy ...
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