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Articles on Trending Technologies
Technical articles with clear explanations and examples
How to set labels in matplotlib.hlines?
The matplotlib.hlines() function draws horizontal lines across the plot. To add labels to these lines, you can use the plt.text() method or create a legend by combining multiple hlines() calls with label parameters. Method 1: Using plt.text() for Direct Labels This approach places text labels directly next to the horizontal lines ? import matplotlib.pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Add horizontal line with direct text label plt.hlines(y=1, xmin=1, xmax=4, lw=7, color='orange') plt.text(4, 1, 'y=1', ha='left', va='center', fontsize=12) # Add another horizontal line with ...
Read MoreHow to create broken horizontal bar graphs in matplotlib?
A broken horizontal bar graph displays data as horizontal bars with gaps or breaks, useful for showing discontinuous time periods, interrupted processes, or segmented data. Matplotlib's broken_barh() method creates these visualizations effectively. Syntax ax.broken_barh(xranges, yrange, **kwargs) Parameters xranges − List of (start, width) tuples defining horizontal segments yrange − Tuple (bottom, height) defining vertical position and height facecolors − Colors for each segment (single color or tuple of colors) Example Let's create a broken horizontal bar graph showing project timelines with interruptions ? import matplotlib.pyplot as plt ...
Read MoreHow to modify a 2d Scatterplot to display color based on a third array in a CSV file?
To modify a 2D scatterplot to display color based on a third array in a CSV file, we use the c parameter in matplotlib's scatter() function. This allows us to map colors to data values, creating a visually informative plot. Steps to Create a Color-Coded Scatterplot Read the CSV file with three columns of data Use the first two columns for X and Y coordinates Map the third column to colors using the c parameter Add a colorbar to show the color-to-value mapping Example with Sample Data Let's create a complete example that generates ...
Read MoreMake a multiline plot from .CSV file in matplotlib
Creating multiline plots from CSV data is a common task in data visualization. Matplotlib combined with pandas makes this straightforward by reading CSV data and plotting multiple columns as separate lines on the same graph. Steps to Create Multiline Plot To make a multiline plot from a CSV file in matplotlib, follow these steps − Set the figure size and adjust the padding between and around the subplots Create a list of columns to fetch the data from a CSV file (ensure column names match those in the CSV) Read the data from the CSV file ...
Read MorePlotting two different arrays of different lengths in matplotlib
When plotting data in matplotlib, you often need to plot arrays of different lengths on the same graph. This is useful for comparing datasets with different sampling rates or time periods. Steps to Plot Arrays of Different Lengths Set up matplotlib figure configuration Create arrays of different lengths using NumPy Use the plot() method for each array with its corresponding x-values Display the plot using show() Example Let's create two datasets with different lengths and plot them together ? import numpy as np import matplotlib.pyplot as plt # Set figure configuration ...
Read MoreHow to shift a graph along the X-axis in matplotlib?
To shift a graph along the X-axis in matplotlib, you need to modify the x-coordinates of your data points. This technique is useful for comparing different versions of the same data or creating animations. Basic X-axis Shifting The simplest way to shift a graph is to add a constant value to all x-coordinates ? import numpy as np import matplotlib.pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Create x and y data points x = np.linspace(-5, 5, 100) y = np.sin(x) # Plot original and ...
Read MoreMatplotlib – How to set xticks and yticks with imshow plot?
When working with imshow() plots in Matplotlib, you often need to customize the tick positions and labels on both axes. The set_xticks() and set_yticks() methods allow you to control exactly where ticks appear on your image plot. Basic Example with Custom Tick Positions Here's how to set custom tick positions for an imshow plot − import numpy as np import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Get current axis ax = plt.gca() # Create random dataset data = np.random.rand(6, 6) # Display data ...
Read MoreRemove white border when using subplot and imshow in Python Matplotlib
When using subplot() and imshow() in Matplotlib, white borders often appear around images due to default padding and axes settings. This can be removed by adjusting figure parameters and axes configuration. Understanding the Problem By default, Matplotlib adds padding around subplots and displays axes with ticks and labels, creating unwanted white space around images displayed with imshow(). Method 1: Using Custom Axes Create a custom axes object that fills the entire figure without any padding ? import numpy as np import matplotlib.pyplot as plt # Set figure parameters plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] ...
Read MoreHow to show tick labels on top of a matplotlib plot?
To show tick labels on top of a matplotlib plot, we can use the set_tick_params() method with labeltop=True. This is useful when you want axis labels at the top instead of the default bottom position. Basic Example Here's how to move tick labels to the top of a plot − import matplotlib.pyplot as plt import numpy as np # Create sample data x = np.linspace(0, 10, 50) y = np.sin(x) # Create the plot fig, ax = plt.subplots(figsize=(8, 4)) ax.plot(x, y, 'b-', linewidth=2) # Move tick labels to top ax.xaxis.set_tick_params(labeltop=True) ax.xaxis.set_tick_params(labelbottom=False) # ...
Read MoreHow should I pass a matplotlib object through a function; as Axis, Axes or Figure?
When passing matplotlib objects through functions, you typically work with Axes objects for individual subplots, Figure objects for the entire figure, or iterate through multiple axes. Here's how to properly structure functions that accept matplotlib objects. Understanding Matplotlib Objects The main matplotlib objects you'll pass through functions are: Figure − The entire figure containing all plots Axes − Individual subplot areas where you draw Array of Axes − Multiple subplot objects when using subplots Example: Passing Axes Objects Here's a complete example showing how to pass matplotlib objects through functions ? ...
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