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Articles by Rishikesh Kumar Rishi
Page 27 of 102
How to get a reverse-order cumulative histogram in Matplotlib?
To create a reverse-order cumulative histogram in Matplotlib, we use the parameter cumulative = -1 in the hist() method. This creates a histogram where each bin shows the cumulative count from the maximum value down to that bin, rather than from the minimum value up. What is a Reverse Cumulative Histogram? A reverse cumulative histogram displays the total count of values greater than or equal to each bin value. Instead of accumulating from left to right, it accumulates from right to left, showing how many data points exceed each threshold. Basic Example Let's create a simple ...
Read MoreHow to use an update function to animate a NetworkX graph in Matplotlib?
To use an update function to animate a NetworkX graph in Matplotlib, we can create dynamic visualizations where nodes and edges change over time. This is useful for visualizing network growth, data flow, or other time-based graph changes. Steps to Animate NetworkX Graphs 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 Initialize a graph with edges, name, and graph attributes Add nodes to the graph using add_nodes_from() method Draw the graph G with Matplotlib Use FuncAnimation() class to make an ...
Read MoreHow to plot a pcolor colorbar in a different subplot in Matplotlib?
To plot a pcolor colorbar in a different subplot in Matplotlib, you can create multiple subplots and add individual colorbars to each one using the fig.colorbar() method. Basic Setup First, let's understand the key components needed ? Create a figure with multiple subplots using plt.subplots() Generate pseudocolor plots with pcolormesh() Add colorbars using fig.colorbar() with specific axis references Use different colormaps for visual distinction Example: Multiple Subplots with Individual Colorbars import matplotlib.pyplot as plt import numpy as np # Set figure size plt.rcParams["figure.figsize"] = [10, 6] plt.rcParams["figure.autolayout"] = True # ...
Read MoreHow to plot a smooth 2D color plot for z = f(x, y) in Matplotlib?
To plot a smooth 2D color plot for z = f(x, y) in Matplotlib, we create a function that maps two variables to a color-coded surface. This visualization is useful for displaying mathematical functions, heat maps, and scientific data. Basic Steps Follow these steps to create a smooth 2D color plot: Set the figure size and adjust the padding between and around the subplots Create x and y data points using numpy Get z data points using f(x, y) Display the data as an image on a 2D regular raster with z data points Use interpolation ...
Read MoreDisplay two Sympy plots as one Matplotlib plot (add the second plot to the first)
To display two SymPy plots as one Matplotlib plot, you can combine multiple symbolic expressions into a single visualization. This is useful when comparing functions or showing relationships between different mathematical expressions. Setting Up the Environment First, import the necessary modules and configure the plot settings ? from sympy import symbols from sympy.plotting import plot from matplotlib import pyplot as plt # Set figure size and layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True Creating and Combining Plots Create two separate SymPy plots and combine them using the extend() method ? ...
Read MoreHow do I show the same Matplotlib figure several times in a single IPython notebook?
To show the same Matplotlib figure several times in a single Jupyter notebook, you can use the fig.show() method or plt.show(). This is useful when you want to display the same plot multiple times at different points in your notebook. Basic Approach Using fig.show() Create a figure once and display it multiple times using the figure's show method ? import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create figure and plot data fig, ax = plt.subplots() ax.plot([2, 4, 7, 5, 4, 1]) ax.set_title('Sample Line Plot') ...
Read MoreHow to plot sine curve on polar axes using Matplotlib?
To plot a sine curve on polar axes using Matplotlib, we need to create a polar coordinate system and plot angular data. Polar plots are useful for representing periodic data and circular patterns. Steps to Create a Polar Sine Plot Set the figure size and adjust the padding between and around the subplots Create a new figure using figure() method Add polar axes using add_subplot(projection='polar') Generate angular data points (theta) and radius values using numpy Plot the data using plot() method on polar axes Display the figure using show() method Example import numpy ...
Read MoreHow do I find the intersection of two line segments in Matplotlib?
To find the intersection of two line segments in Matplotlib, we calculate where two lines meet using their slopes and intercepts, then draw horizontal and vertical lines through that point. Mathematical Formula For two lines with equations y = m1*x + c1 and y = m2*x + c2, the intersection point is: x_intersection = (c1 - c2) / (m2 - m1) y_intersection = m1 * x_intersection + c1 Example Here's how to find and visualize the intersection point ? import matplotlib.pyplot as plt import numpy as np ...
Read MoreHow to show minor tick labels on a log-scale with Matplotlib?
In Matplotlib, displaying minor tick labels on a log-scale plot requires special formatting since log scales typically only show major ticks by default. We can achieve this by using the tick_params() method and FormatStrFormatter to control minor tick appearance. Basic Log-Scale Plot with Minor Ticks Here's how to create a log-scale plot and display minor tick labels ? import numpy as np import matplotlib.pyplot as plt from matplotlib.ticker import FormatStrFormatter # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create data points x = np.linspace(-2, 2, 10) y = np.exp(x) ...
Read MoreHow to fill the area under a step curve using pyplot? (Matplotlib)
To fill the area under a step curve using pyplot, you can use the fill_between() method with the step parameter. This creates filled regions beneath step plots, which are useful for displaying discrete data or histogram-like visualizations. Basic Step Curve with Fill Here's how to create a simple step curve with filled area ? 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(-10, 10, 100) y1 = np.sin(x) y2 = np.cos(x) # Fill area under step ...
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