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Data Visualization Articles
Page 32 of 68
Plot scatter points on polar axis in Matplotlib
To plot scatter points on polar axis in Matplotlib, we can create a polar coordinate system where points are positioned using angles (theta) and radial distances (r). This is useful for visualizing circular or angular data patterns. Basic Polar Scatter Plot Let's start with a simple example that demonstrates the key components ? 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 = 150 r = 2 * np.random.rand(N) theta = 2 * np.pi * np.random.rand(N) area = 200 ...
Read MoreHow do I omit Matplotlib printed output in Python / Jupyter notebook?
When creating plots in Matplotlib within Jupyter notebooks, you often see unwanted printed output like []. This happens because Matplotlib functions return objects that Jupyter displays. Here are three effective methods to suppress this output. Method 1: Using Semicolon The simplest approach is adding a semicolon at the end of your plot command ? import numpy as np import matplotlib.pyplot as plt x = np.linspace(1, 10, 1000) y = np.sin(x) # Without semicolon - shows output plt.plot(x, y) [] import numpy as np import matplotlib.pyplot as plt ...
Read MoreHow to save figures to pdf as raster images in Matplotlib?
To save figures to PDF as raster images in Matplotlib, you need to use the rasterized=True parameter when creating plots. This converts vector graphics to bitmap format, which can be useful for complex plots with many data points. Basic Setup First, let's set up the basic requirements and create a simple rasterized plot − import numpy as np import matplotlib.pyplot as plt # Set figure size and layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create figure and subplot with rasterization fig = plt.figure() ax = fig.add_subplot(111, rasterized=True) # Generate random data ...
Read MoreHow can I get the color of the last figure in Matplotlib?
In Matplotlib, you can retrieve the color of any plotted line using the get_color() method. This is particularly useful when you want to identify the automatically assigned colors or when working with multiple plots in the same figure. Basic Example Let's start with a simple example to understand how get_color() works ? 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 data points x = np.arange(10) # Plot multiple lines p = plt.plot(x, x, x, x ** 2, x, x ** ...
Read MoreHow to plot contourf and log color scale in Matplotlib?
In Matplotlib, you can create contour plots with logarithmic color scaling using contourf() combined with LogLocator(). This is particularly useful when your data spans several orders of magnitude. Basic Setup First, let's understand the key components needed for logarithmic contour plots − Use contourf() method for filled contour plots Apply ticker.LogLocator() for logarithmic color scale Handle negative or zero values with masked arrays Add a colorbar to visualize the scale Example Here's how to create a contour plot with logarithmic color scaling − import matplotlib.pyplot as plt import numpy as np ...
Read MoreHow do I customize the display of edge labels using networkx in Matplotlib?
Customizing edge labels in NetworkX with Matplotlib allows you to control the appearance and positioning of text displayed along graph edges. You can adjust label position, font properties, and styling to create clear, professional network visualizations. Basic Edge Label Display First, let's create a simple graph with edge labels ? import matplotlib.pyplot as plt import networkx as nx # Create a directed graph G = nx.DiGraph() G.add_nodes_from([1, 2, 3, 4]) G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1), (1, 3)]) # Position nodes using spring layout pos = nx.spring_layout(G, seed=42) # Draw the ...
Read MoreHow to independently set horizontal and vertical, major and minor gridlines of a plot?
Matplotlib allows you to independently control horizontal and vertical, major and minor gridlines using the grid() method along with axis-specific methods. This provides fine-grained control over plot appearance. Basic Syntax The grid() method can be applied to specific axes: ax.xaxis.grid(which="major", color='color', linestyle='style', linewidth=width) ax.yaxis.grid(which="minor", color='color', linestyle='style', linewidth=width) Parameters which − Specifies "major" or "minor" gridlines color − Sets the gridline color linestyle − Defines line style ('-', '--', '-.', ':') linewidth − Controls line thickness Complete Example Here's how to create independent horizontal and vertical gridlines with different styles ...
Read MoreContour hatching in Matplotlib plot
Contour hatching in Matplotlib allows you to add visual patterns to filled contour plots, making it easier to distinguish between different regions. This is particularly useful for creating publication-ready plots or when working with grayscale images. Basic Contour Hatching Here's how to create a contour plot with different hatch patterns ? 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 coordinate arrays x = np.linspace(-3, 5, 150) y = np.linspace(-3, 5, 120) X, Y = np.meshgrid(x, y) # Generate sample data ...
Read MoreHow can I move a tick label without moving corresponding tick in Matplotlib?
In Matplotlib, you can move tick labels independently from their corresponding ticks using the set_xticklabels() or set_yticklabels() methods, or by creating custom annotations. This is useful when you need to adjust label positioning for better readability without affecting the tick marks themselves. Method 1: Using set_xticklabels() with Custom Positions The most straightforward approach is to get existing tick positions and create custom labels ? import matplotlib.pyplot as plt import numpy as np # Create sample data x = np.linspace(0, 10, 100) y = np.sin(x) plt.figure(figsize=(10, 6)) plt.plot(x, y) # Get current tick positions ...
Read MoreHow to access axis label object in Matplotlib?
To access axis label objects in Matplotlib, you can use the get_label() method on the axis object, which returns the label text object. This is useful when you need to retrieve or manipulate axis labels programmatically. Basic Syntax The main methods for accessing axis labels are ? # Get X-axis label text x_label_text = ax.xaxis.get_label().get_text() # Get Y-axis label text y_label_text = ax.yaxis.get_label().get_text() # Get the label object itself x_label_obj = ax.xaxis.get_label() y_label_obj = ax.yaxis.get_label() Example: Accessing Axis Label Objects Here's a complete example showing how to set and ...
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