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Articles on Trending Technologies
Technical articles with clear explanations and examples
How to display percentage above a bar chart in Matplotlib?
To display percentage above a bar chart in Matplotlib, you can iterate through the bar patches and use the text() method to add percentage labels. This is commonly used to show data proportions in visualizations. Basic Example Here's how to add percentage labels above each bar ? import matplotlib.pyplot as plt import numpy as np # Set figure size plt.figure(figsize=(8, 5)) # Data categories = ['A', 'B', 'C', 'D', 'E'] values = [25, 40, 15, 35, 20] # Create bar chart bars = plt.bar(categories, values, color='skyblue') # Add percentage labels above bars ...
Read MoreHow to make two markers share the same label in the legend using Matplotlib?
To make two markers share the same label in the legend using Matplotlib, you can assign the same label name to multiple plot elements. When Matplotlib encounters duplicate labels, it automatically groups them under a single legend entry. Basic Example with Shared Labels Here's how to create two different plots that share the same legend label ? import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-5, 5, 100) plt.plot(x, np.sin(x), ls="dotted", label='y=f(x)') plt.plot(x, np.cos(x), ls="-", label='y=f(x)') plt.legend(loc=1) plt.show() In this example, ...
Read MoreManipulation on horizontal space in Matplotlib subplots
To manipulate horizontal space in Matplotlib subplots, we can use the wspace parameter in the subplots_adjust() method. This parameter controls the amount of width reserved for space between subplots, expressed as a fraction of the average axis width. Basic Syntax fig.subplots_adjust(wspace=value) Where value ranges from 0 (no space) to higher values (more space between subplots). Example with Different Horizontal Spacing Let's create subplots with actual plot data and demonstrate different horizontal spacing values − import numpy as np import matplotlib.pyplot as plt # Create sample data x = np.linspace(0, 2 ...
Read MoreHow can I pass parameters to on_key in fig.canvas.mpl_connect('key_press_event',on_key)?
When working with matplotlib event handling, you sometimes need to pass additional parameters to your callback function. The fig.canvas.mpl_connect('key_press_event', on_key) method only accepts the event handler function, but there are several ways to pass extra parameters. Method 1: Using Lambda Functions The most straightforward approach is to use a lambda function to wrap your callback ? import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() ax.set_xlim(0, 10) ax.set_ylim(0, 10) # Parameters to pass marker_style = 'ro-' marker_size = 8 def onkey(event, marker, size): ...
Read MoreCustomizing annotation with Seaborn's FacetGrid
Seaborn's FacetGrid allows you to create multiple subplots based on categorical variables and customize annotations for each subplot. You can set custom titles, labels, and other annotations to make your visualizations more informative. Basic FacetGrid Setup First, let's create a basic FacetGrid with custom annotations ? import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # Set figure parameters plt.rcParams["figure.figsize"] = [10, 4] plt.rcParams["figure.autolayout"] = True # Create sample data df = pd.DataFrame({ 'values': [3, 7, 8, 1, 5, 9, 2, 6], 'category': ...
Read MoreManipulation on vertical space in Matplotlib subplots
To manipulate vertical space in Matplotlib subplots, we can use the hspace parameter in the subplots_adjust() method. This allows us to control the spacing between subplot rows. Understanding hspace Parameter The hspace parameter controls the height of padding between subplots as a fraction of the average subplot height. Values greater than 1.0 create more space, while values less than 1.0 reduce space. Basic Example with Vertical Space Adjustment Let's create a 2×2 subplot layout and adjust the vertical spacing ? import numpy as np import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] ...
Read MoreHow to convert data values into color information for Matplotlib?
Converting data values into color information for Matplotlib allows you to create visually appealing scatter plots where colors represent data patterns. This process involves using colormaps to map numerical values to specific colors. Basic Color Mapping with Colormaps The most straightforward approach uses Matplotlib's built-in colormaps to automatically map data values to colors ? 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 sample data values = np.random.rand(100) x = np.random.rand(len(values)) y = np.random.rand(len(values)) # Create scatter plot with automatic color ...
Read MoreInteractive plotting with Python Matplotlib via command line
Interactive plotting in Matplotlib allows you to modify plots dynamically and see changes in real-time. Using plt.ion() and plt.ioff(), you can enable or disable interactive mode to control when plot updates are displayed. Setting Up Interactive Mode First, you need to activate the matplotlib backend and enable interactive mode. Open IPython shell and enter the following commands − %matplotlib auto import matplotlib.pyplot as plt plt.ion() # Enable interactive mode Creating an Interactive Plot Let's create a figure and demonstrate how interactive plotting works − # Create figure and axis fig, ...
Read MoreDisplaying different images with actual size in a Matplotlib subplot
To display different images with actual size in a Matplotlib subplot, we can take the following steps − Set the figure size and adjust the padding between and around the subplots. Read two images using imread() method (im1 and im2) Create a figure and a set of subplots. Turn off axes for both the subplots. Use imshow() method to display im1 and im2 data. To display the figure, use show() method. Example ...
Read MoreHow do I make bar plots automatically cycle across different colors?
To make bar plots automatically cycle across different colors, we can use matplotlib's cycler to set up automatic color rotation for each bar group. This ensures each data series gets a distinct color without manual specification. Steps to Create Color-Cycling Bar Plots Set the figure size and adjust the padding between and around the subplots Configure automatic cycler for different colors using plt.cycler() Create a Pandas DataFrame with the data to plot Use plot() method with kind="bar" to create the bar chart Display the figure using show() method Example Here's how to create a ...
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