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Articles by Rishikesh Kumar Rishi
Page 16 of 102
How to decrease the hatch density in Matplotlib?
In Matplotlib, hatch patterns have a default density that might appear too dense for certain visualizations. You can decrease hatch density by creating a custom hatch class that overrides the default density behavior. Understanding Hatch Density Hatch density refers to how closely packed the hatch lines or patterns appear in a plot. Lower density means more spacing between pattern elements, while higher density creates tighter patterns. Creating a Custom Hatch Class To control hatch density, we need to create a custom hatch class that inherits from Matplotlib's built-in hatch classes ? import matplotlib.pyplot as ...
Read MoreHow to make a quiver plot in polar coordinates using Matplotlib?
A quiver plot in polar coordinates displays vector fields using arrows positioned at polar coordinates (radius, angle). Matplotlib's quiver() function with polar projection creates these directional arrow plots. Basic Polar Quiver Plot First, let's create a simple quiver plot with vectors radiating outward ? import numpy as np import matplotlib.pyplot as plt # Create polar coordinate grid radii = np.linspace(0.2, 1, 4) thetas = np.linspace(0, 2 * np.pi, 12) theta, r = np.meshgrid(thetas, radii) # Create figure with polar projection fig, ax = plt.subplots(subplot_kw=dict(projection='polar'), figsize=(8, 6)) # Define vector components (radial and tangential) ...
Read MoreWhat is the correct way to replace matplotlib tick labels with computed values?
We can use ax.loglog(x, y) and set_major_formatter() methods to replace matplotlib tick labels with computed values. This technique is particularly useful when working with logarithmic scales or when you need custom formatting for your axis labels. Steps Set the figure size and adjust the padding between and around the subplots. Create a figure and a set of subplots. Make a plot with log scaling on both the X and Y axis. Set the formatter of the major ticker. To display the figure, use show() method. Example 1: Using LogFormatterExponent Here's how to replace tick ...
Read MoreHow to make a simple lollipop plot in Matplotlib?
A lollipop plot is a variation of a bar chart where bars are replaced with lines and circles, resembling lollipops. This visualization is effective for showing values across categories while reducing visual clutter compared to traditional bar charts. Creating a Basic Lollipop Plot We'll create a lollipop plot using Matplotlib's stem() function with sample data ? import numpy as np import matplotlib.pyplot as plt import pandas as pd # Set figure size plt.figure(figsize=(10, 6)) # Create sample data categories = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'] values = [23, 45, 56, 78, ...
Read MoreHow to put the title at the bottom of a figure in Matplotlib?
In Matplotlib, you can position the title at the bottom of a figure by adjusting the y parameter in the title() method. This is useful for creating custom layouts or when you want the title to appear below the plot area. Basic Approach Use the y parameter in plt.title() to control vertical positioning. Values below 1.0 move the title downward ? 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 # Generate sample data N = 100 x = np.random.rand(N) y = np.random.rand(N) ...
Read MoreHow to make multipartite graphs using networkx and Matplotlib?
A multipartite graph is a graph where nodes are divided into multiple disjoint sets, with edges only connecting nodes from different sets. NetworkX provides tools to create and visualize these structures using multipartite_layout() for positioning nodes in distinct layers. Steps to Create a Multipartite Graph Set the figure size and adjust the padding between and around the subplots Create a list of subset sizes and colors for each layer Define a method for multilayered graph that returns a graph object Assign colors to nodes based on their layers Position the nodes in layers using multipartite_layout() Draw the ...
Read MoreHow to plot the difference of two distributions in Matplotlib?
To plot the difference between two distributions in Matplotlib, we use kernel density estimation (KDE) to create smooth probability density functions from our data, then visualize both distributions and their difference. Understanding Kernel Density Estimation Kernel Density Estimation creates a continuous probability density function from discrete data points using Gaussian kernels. This allows us to compare distributions smoothly. Complete Example Here's how to plot two distributions and their difference ? import numpy as np import matplotlib.pyplot as plt import scipy.stats # Set figure size plt.rcParams["figure.figsize"] = [10, 6] plt.rcParams["figure.autolayout"] = True # ...
Read MoreHow to visualize 95% confidence interval in Matplotlib?
To visualize a 95% confidence interval in Matplotlib, you need to plot your main data line and fill the area around it representing the uncertainty range. This technique is commonly used in statistical plots to show the reliability of predictions or measurements. Basic 95% Confidence Interval Plot Here's how to create a confidence interval visualization with synthetic data ? 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 sample data x = np.arange(0, 10, 0.05) y = np.sin(x) # Define the ...
Read MorePlotting an imshow() image in 3d in Matplotlib
To plot an imshow() image in 3D in Matplotlib, you can display 2D data as both a traditional image and as a 3D surface plot. This technique is useful for visualizing data from different perspectives. Step-by-Step Approach Here's the process to create 3D visualizations of imshow data ? Create xx and yy coordinate grids using numpy Generate 2D data using mathematical functions Create a figure with multiple subplots Display data as a 2D image using imshow() Display the same data as a 3D contour plot Show the combined visualization Basic Example This example ...
Read MoreAdding extra contour lines using Matplotlib 2D contour plotting
To add extra contour lines using Matplotlib 2D contour plotting, we can control the density and positioning of contour lines by specifying custom levels. This allows us to highlight specific values or create more detailed visualizations. Steps to Add Extra Contour Lines Set the figure size and adjust the padding between and around the subplots. Create a function f(x, y) to get the z data points from x and y. Create x and y data points using NumPy. Make a list of levels using NumPy to define where contour lines appear. Make a contour plot using contour() ...
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