Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Economics & Finance
To set the Matplotlib title in bold while using "Times New Roman", we can use fontweight="bold" along with fontname="Times New Roman" in the set_title() method. Basic Example Here's how to create a scatter plot with a bold Times New Roman title − 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, ax = plt.subplots() # Generate random data points x = np.random.rand(100) y = np.random.rand(100) # Create scatter plot ax.scatter(x, y, c=y, marker="v") # Set ... Read More
To plot a 3D surface from x, y and z scatter data in Python, we can use matplotlib's plot_surface() method. This creates stunning 3D visualizations from coordinate data. Basic 3D Surface Plot Here's how to create a 3D surface plot using mathematical function 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 figure and 3D axes fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # Generate coordinate data x = np.linspace(-2, 2, 100) y = np.linspace(-2, 2, 10) X, Y ... Read More
To get the (x, y) values of a line plotted by a contour plot in Matplotlib, you need to access the contour collections and extract the path vertices. This is useful for analyzing specific contour lines or extracting data for further processing. Steps to Extract Contour Line Coordinates Create a contour plot using contour() method Access the contour collections from the returned object Get the paths from each collection Extract vertices (x, y coordinates) from each path Example Here's how to extract the (x, y) coordinates from contour lines ? import matplotlib.pyplot ... Read More
To animate a time-ordered sequence of Matplotlib plots, you can use the FuncAnimation class from matplotlib's animation module. This creates smooth animations by repeatedly calling an update function at regular intervals. Basic Animation Example Here's a simple example that animates random data over time ? import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create figure and axis fig, ax = plt.subplots() # Initialize empty plot line, = ax.plot([], [], 'b-') ax.set_xlim(0, 50) ax.set_ylim(-3, 3) ax.set_xlabel('Time') ax.set_ylabel('Value') ax.set_title('Animated ... Read More
To fill the area under a curve in Matplotlib on a log scale, you can use fill_between() combined with xscale() and yscale() methods. This technique is useful for visualizing data that spans multiple orders of magnitude. Steps to Fill Area on Log Scale Set figure size and layout parameters Create data points using NumPy Plot the curves using plot() method Fill the area between curves using fill_between() Set logarithmic scale for axes Add legend and display the plot Example import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] ... Read More
When plotting multiple datasets in Matplotlib, you may want error bars to appear on top of other plot elements for better visibility. By default, plot elements are drawn in the order they're called, so error bars might be hidden behind other lines. Default Behavior Here's what happens when error bars are plotted before other lines − import matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = plt.gca() # Error bars plotted first (will be hidden) ax.errorbar(range(10), np.random.rand(10), yerr=0.3 * np.random.rand(10), ... Read More
Matplotlib provides numerous built-in colormaps through plt.cm.get_cmap(), and additional colormaps can be registered using matplotlib.cm.register_cmap. You can retrieve a list of all available colormap names to use with get_cmap(). Getting All Available Colormap Names Use plt.colormaps() to retrieve all registered colormap names ? from matplotlib import pyplot as plt cmaps = plt.colormaps() print("Available colormap names:") print(f"Total colormaps: {len(cmaps)}") # Show first 10 colormaps for i, name in enumerate(cmaps[:10]): print(f"{i+1}. {name}") print("...") print(f"And {len(cmaps)-10} more colormaps") Available colormap names: Total colormaps: 170 1. Accent 2. Accent_r ... Read More
To plot multiple time-series DataFrames into a single plot using Pandas and Matplotlib, you can overlay different series on the same axes or use secondary y-axes for different scales. Steps to Create Multiple Time-Series Plot Set the figure size and adjust the padding between subplots Create a Pandas DataFrame with time series data Set the datetime column as the index Plot multiple series using plot() method Use secondary_y=True for different scales Display the figure using show() method Example Here's how to plot multiple time-series data with different currencies ? import numpy as ... Read More
In Matplotlib, you can create a legend entry with multiple keys (symbols) representing different lines or data series. This is useful when you want to group related plots under a single legend label. Basic Approach Use the HandlerTuple class to group multiple plot objects under one legend entry ? import matplotlib.pyplot as plt from matplotlib.legend_handler import HandlerTuple plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create two different line plots p1, = plt.plot([1, 2.5, 3], 'r-d', label='Red line') p2, = plt.plot([3, 2, 1], 'k-o', label='Black line') # Create legend with both keys for ... Read More
Removing horizontal lines from images is useful for preprocessing scanned documents or cleaning up images with unwanted line artifacts. We'll use OpenCV's morphological operations to detect and remove these lines while preserving the important content. Understanding the Process The horizontal line removal process involves several key steps ? Convert the image to grayscale and apply thresholding Use horizontal morphological kernels to detect line structures Find contours of the detected lines Remove the lines by drawing over them Apply repair operations to restore nearby content Complete Implementation Here's a complete example that demonstrates horizontal ... Read More