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Matplotlib Articles
Page 42 of 91
Draw a border around subplots in Matplotlib
To draw a border around subplots in Matplotlib, we can use a Rectangle patch that creates a visible border around the subplot area. This technique is useful for highlighting specific subplots or creating visual separation in multi−subplot figures. Basic Border Around Single Subplot Here's how to add a simple border around one subplot ? import matplotlib.pyplot as plt import matplotlib.patches as patches # Create figure and subplot fig, ax = plt.subplots(1, 1, figsize=(6, 4)) # Plot some sample data ax.plot([1, 2, 3, 4], [1, 4, 2, 3], 'b-o') ax.set_title("Subplot with Border") # Get ...
Read MorePlotting a cumulative graph of Python datetimes in Matplotlib
To plot a cumulative graph of Python datetimes in Matplotlib, you can combine Pandas datetime functionality with Matplotlib's plotting capabilities. This is useful for visualizing data trends over time, such as daily sales, website visits, or any time-series data that accumulates. Basic Setup First, let's create a dataset with datetime values and plot a cumulative sum ? import pandas as pd import matplotlib.pyplot as plt from datetime import datetime, timedelta import numpy as np # Set figure size plt.rcParams["figure.figsize"] = [10, 6] plt.rcParams["figure.autolayout"] = True # Create sample datetime data start_date = datetime(2024, 1, ...
Read MoreHow can I programmatically select a specific subplot in Matplotlib?
To programmatically select a specific subplot in Matplotlib, you can use several approaches including add_subplot(), subplots(), or subplot() methods. This allows you to modify individual subplots after creation. Method 1: Using add_subplot() Create subplots in a loop and then select a specific one by calling add_subplot() again with the same position ? import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [10, 4] plt.rcParams["figure.autolayout"] = True fig = plt.figure() # Create 4 subplots for index in [1, 2, 3, 4]: ax = fig.add_subplot(1, 4, index) ...
Read MoreHow to get smooth interpolation when using pcolormesh (Matplotlib)?
Pcolormesh in Matplotlib creates pseudocolor plots with rectangular grids. By default, it produces blocky, pixelated output. To achieve smooth interpolation and eliminate harsh edges, you can use the shading="gouraud" parameter. Default Pcolormesh vs Smooth Interpolation Let's first see the difference between default shading and Gouraud shading ? import matplotlib.pyplot as plt import numpy as np # Create sample data data = np.random.random((5, 5)) x = np.arange(0, 5, 1) y = np.arange(0, 5, 1) x, y = np.meshgrid(x, y) # Create subplots for comparison fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) # Default ...
Read MoreHow to change axes background color in Matplotlib?
To change the axes background color in Matplotlib, we can use the set_facecolor() method. This method allows us to customize the background color of the plotting area to make our visualizations more appealing or to match specific design requirements. Using set_facecolor() Method The most straightforward approach is to get the current axes and apply set_facecolor() ? 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 # Get current axes and set background color ax = plt.gca() ax.set_facecolor("orange") # Create data points x = ...
Read MorePopulating Matplotlib subplots through a loop and a function
Matplotlib subplots can be efficiently populated using loops and functions to create organized grid layouts. This approach is especially useful when you need to display multiple related plots with similar formatting. Creating the Subplot Grid First, let's set up a 3×2 grid of subplots and define our plotting function ? import numpy as np import matplotlib.pyplot as plt # Set figure size and layout plt.rcParams["figure.figsize"] = [10, 8] plt.rcParams["figure.autolayout"] = True # Create 3 rows and 2 columns of subplots fig, axes = plt.subplots(3, 2) def iterate_columns(cols, x, color='red'): ...
Read MoreHow to hide the colorbar of a Seaborn heatmap?
To hide the colorbar of a Seaborn heatmap, we can use cbar=False in the heatmap() method. This is useful when you want to focus on the pattern visualization without showing the color scale legend. Basic Example Here's how to create a heatmap without a colorbar − import seaborn as sns import pandas as pd import numpy as np import matplotlib.pyplot as plt # Set figure size plt.figure(figsize=(8, 6)) # Create sample data data = np.random.random((4, 4)) df = pd.DataFrame(data, columns=["A", "B", "C", "D"]) # Create heatmap without colorbar sns.heatmap(df, cbar=False, annot=True, fmt='.2f') ...
Read MoreSetting active subplot using axes object in Matplotlib
To set an active subplot using axes objects in Matplotlib, you can use the plt.axes() function to switch between different subplot axes. This allows you to work with multiple subplots and control which one is currently active for plotting operations. Syntax fig, axs = plt.subplots(rows, cols) plt.axes(axs[index]) # Set specific subplot as active Basic Example Here's how to create subplots and switch between them using axes objects ? import matplotlib.pyplot as plt # Set figure configuration plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create sample data x = ...
Read MoreHow to insert a scale bar in a map in Matplotlib?
To insert a scale bar in a map in matplotlib, we can use AnchoredSizeBar() class to instantiate the scalebar object. This is particularly useful for geographic visualizations where you need to show distance measurements. Steps Set the figure size and adjust the padding between and around the subplots. Create random data using numpy. Use imshow() method to display data as an image, i.e., on a 2D regular raster. Get the current axis using gca() method. Draw a horizontal scale bar with a center-aligned label underneath. Add a scalebar artist to the current axis. Turn off the axes. ...
Read MoreHow to plot gamma distribution with alpha and beta parameters in Python using Matplotlib?
The gamma distribution is a continuous probability distribution with two parameters: alpha (shape) and beta (scale). In Python, we can plot gamma distributions using scipy.stats.gamma.pdf() and Matplotlib to visualize how different parameter values affect the distribution shape. Steps Set the figure size and adjust the padding between and around the subplots. Create x values using numpy and y values using gamma.pdf() function. Plot x and y data points using plot() method. Use legend() method to place the legend elements for the plot. To display the figure, use show() method. Basic Gamma Distribution Plot Here's ...
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