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Articles on Trending Technologies
Technical articles with clear explanations and examples
How to change the color of a single bar if a condition is true (Matplotlib)?
To change the color of a single bar based on a condition in Matplotlib, we can create a list of colors that applies different colors based on our criteria. This technique is useful for highlighting specific data points in bar charts. Steps Set the figure size and adjust the padding between and around the subplots. Initialize a variable for bar width. Create lists of values and corresponding colors based on conditions. Use bar() method to plot bars with conditional colors. To display the figure, use show() method. Example Here's how to highlight bars with ...
Read MoreHow to annotate each cell of a heatmap in Seaborn?
To annotate each cell of a heatmap in Seaborn, we can set annot=True in the heatmap() method. This displays the actual data values inside each cell, making the heatmap more informative and easier to interpret. Basic Heatmap with Annotations Here's how to create a simple annotated heatmap ? import seaborn as sns import pandas as pd 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 sample data data = pd.DataFrame(np.random.random((5, 5)), ...
Read MoreHow do you create line segments between two points in Matplotlib?
To create line segments between two points in Matplotlib, you can use the plot() method to connect coordinates. This technique is useful for drawing geometric shapes, connecting data points, or creating custom visualizations. Basic Line Segment Here's how to create a simple line segment between two points ? import matplotlib.pyplot as plt # Set figure size and layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Define two points point1 = [1, 2] point2 = [3, 4] # Extract x and y coordinates x_values = [point1[0], point2[0]] y_values = [point1[1], point2[1]] # ...
Read MoreMatplotlib Backend Differences between Agg and Cairo
Matplotlib offers different backends for rendering graphics, each optimized for specific output formats. The Agg and Cairo backends are two popular choices with distinct capabilities and use cases. Backend Comparison Backend File Types Graphics Type Description Agg PNG Raster High-quality images using Anti-Grain Geometry engine Cairo PNG, PS, PDF, SVG Raster & Vector Versatile output using Cairo graphics library Using Agg Backend The Agg backend is ideal for high-quality raster images. Here's how to use it ? import matplotlib as mpl import ...
Read MoreFind the area between two curves plotted in Matplotlib
To find the area between two curves in Matplotlib, we use the fill_between() method. This is useful for visualizing the difference between datasets, confidence intervals, or regions of interest between mathematical functions. Basic Example Let's create two curves and fill the area between them ? 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 # Create data points x = np.linspace(0, 1, 100) curve1 = x ** 2 # Parabola curve2 = x # Linear function ...
Read MoreHow to make the Parula colormap in Matplotlib?
The Parula colormap is MATLAB's default colormap known for its perceptually uniform color transitions. In Matplotlib, we can create a custom Parula colormap using LinearSegmentedColormap with the official Parula color values. Creating the Parula Colormap We'll use the exact RGB values from MATLAB's Parula colormap to ensure authenticity − import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import LinearSegmentedColormap # Official Parula colormap RGB values parula_colors = [ (0.2081, 0.1663, 0.5292), (0.2116, 0.1898, 0.5777), (0.2123, 0.2138, 0.6270), ...
Read MoreSetting the size of the plotting canvas in Matplotlib
To set the size of the plotting canvas in Matplotlib, you can control the figure dimensions using several approaches. The figure size determines how large your plot will appear when displayed or saved. Using rcParams (Global Setting) The most common approach is to set global parameters that affect all subsequent plots − import numpy as np import matplotlib.pyplot as plt # Set figure size globally plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create data points x = np.linspace(-2, 2, 100) y = np.sin(x) # Create the plot plt.plot(x, y) plt.title("Sine Wave with ...
Read MoreWhat is the name of the default Seaborn color palette?
The default Seaborn color palette is called "deep". It consists of 10 distinct colors designed for categorical data visualization and provides good contrast between different categories. Getting the Default Color Palette You can retrieve and display the default Seaborn color palette using the following approach ? import seaborn as sns import matplotlib.pyplot as plt # Set figure size for better visualization plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Get the default color palette current_palette = sns.color_palette() # Display the palette as a horizontal array sns.palplot(current_palette) plt.title("Default Seaborn Color Palette: 'deep'") plt.show() ...
Read MoreHow to get different font sizes in the same annotation of Matplotlib?
To add different font sizes in the same annotation method, we can create multiple annotations with varying font sizes at different positions. This technique is useful for creating visually appealing text displays with hierarchical information. Step-by-Step Approach Make lists of x and y data points where text could be placed. Initialize a variable 'labels', i.e., a string. Make a list of sizes of the fonts. Use subplots() method to create a figure and a set of subplots. Iterate above lists and annotate each label's text and set its fontsize. To display the figure, use show() method. ...
Read MoreHow to load an image and show the image using Keras?
To load and display an image using Keras, we use the load_img() method from keras.preprocessing.image. This method loads an image file and allows us to set a target size for display. Steps Import the image module from keras.preprocessing Use load_img() method to load the image file Set the target size of the image using the target_size parameter Display the image using the show() method Syntax The basic syntax for loading an image is ? keras.preprocessing.image.load_img(path, target_size=None) Parameters path ? Path to the image file target_size ? Tuple of ...
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