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Matplotlib Articles
Page 31 of 91
How to make simple double head arrows on the axes in Matplotlib?
Creating double head arrows on Matplotlib axes helps visualize relationships and measurements between points. You can achieve this using the annotate() method with specific arrow properties. Basic Double Head Arrow Use the annotate() method with arrowstyle="" to create arrows pointing in both directions ? import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True plt.annotate('Double Arrow', xy=(0.1, 0.1), xytext=(0.5, 0.5), arrowprops=dict(arrowstyle='', color='red')) plt.show() Customizing Arrow Properties You can customize the arrow appearance by modifying properties like ...
Read MoreHow to add legends and title to grouped histograms generated by Pandas? (Matplotlib)
To add legends and title to grouped histograms generated by Pandas, we can use the DataFrame's plot() method with kind='hist' and then customize the plot using Matplotlib functions. Basic Grouped Histogram with Title First, let's create a DataFrame and plot a basic grouped histogram with a title ? import matplotlib.pyplot as plt import pandas as pd # Set figure size for better visibility plt.rcParams["figure.figsize"] = [10, 6] # Create sample data df = pd.DataFrame({ 'Math': [85, 92, 78, 88, 95, 82, 90], 'Science': [88, 85, 92, ...
Read MorePlot 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 ...
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