Understanding Line Thickness in Matplotlib

As a data visualization library in Python, Matplotlib allows users to create a wide variety of graphs and plots to represent data. One key aspect of customizing plots is controlling the thickness of lines. Being able to tweak the thickness of lines in plots can help highlight certain data trends, differentiate between multiple line series, and improve overall aesthetics.

In this comprehensive guide, we will cover the ins and outs of manipulating line thickness in Matplotlib, including:

• What is Matplotlib and why is line thickness important
• How Matplotlib handles default line thickness
• Setting the thickness for a single line
• Adjusting thickness for multiple lines
• Techniques for highlighting lines
• Best practices for choosing line widths
• Common issues and solutions when working with line thickness

What is Matplotlib and Why is Line Thickness Important?

Matplotlib is the most popular 2D plotting library for Python. It gives developers control of figure sizes, fonts, line and axis widths, color palettes, and plenty more customization options. This flexibility makes Matplotlib suited for everything from quick prototyping to publication-ready images.

When creating line plots, one of the easiest ways to highlight certain data or trends is to adjust line thickness. Thicker lines grab viewers‘ attention, while thinner lines fade more into the background. Appropriate line thickness balances between accentuating key lines without overwhelming the entire chart.

Controlling line thickness also enables plots with multiple line series to be easily distinguished. Assigning each line a different width is a simple way to differentiate them without needing other visual cues like color or line style.

Overall, line thickness impacts the clarity, readability, and aesthetic appeal of Matplotlib visualizations. Thoughtfully manipulating thickness takes plots from bland default settings to presentation-ready.

How Matplotlib Handles Default Line Thickness

Before adjusting line thickness, it‘s important to understand Matplotlib‘s default behavior. By default, Matplotlib assigns a line width of 1 point to plots. For example:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.show() 

This sine wave plot will have a line thickness of 1 pt:

The default width tends to be on the thinner side. This allows multiple plot lines to be shown clearly without them blending together. But for plots with few lines, the default thin width might look oddly sparse.

Matplotlib measures line thickness in points (pt). This allows the thickness to scale appropriately when saving plots as vector graphics vs pixel-based raster formats. Points translate reasonably well between mediums.

Now that we‘ve seen Matplotlib‘s default behavior, let‘s explore how to override the defaults to set specific line thicknesses.

Setting the Thickness for a Single Line

Adjusting the thickness of a single line is straightforward in Matplotlib. We simply specify the linewidth parameter in plt.plot().

For example, to make our sine wave plot have a thicker 5 pt line:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y, linewidth=5) 
plt.show()

This now plots a much thicker, bolder sine wave:

We can go the opposite direction and make an ultra-thin 0.5 pt sine wave line via:

plt.plot(x, y, linewidth=0.5)

The linewidth parameter accepts floating point numbers, allowing precision down to decimal places. This granular control makes it possible to fine-tune small variations in line widths.

Now that we can set the thickness of a single line, let‘s explore adjusting multiple lines at once.

Adjusting Thickness for Multiple Lines

Often times plots contain multiple lines to represent and compare distinct data sets. In these cases, it‘s common to vary the thickness of lines to make them easier to differentiate.

Matplotlib makes it easy to customize each line independently. For example, here is a plot with two sine waves, the second one phase shifted:

x = np.linspace(0, 10, 100)

# Sine wave 1
y1 = np.sin(x)  

# Phase shifted sine wave 2
y2 = np.sin(x - np.pi/2)

plt.plot(x, y1)
plt.plot(x, y2)
plt.show()

This plots two sine waves with the default thickness:

To differentiate these lines, we can thicken the second sine wave by specifying its linewidth independently:

plt.plot(x, y1)  
plt.plot(x, y2, linewidth=3)  
plt.show()

Now the phase shifted wave clearly stands out:

By individually tweaking line thickness, it becomes much easier to compare multiple lines at a glance, even with similar styles and colors.

This technique extends seamlessly to any number of lines by supplying each plot command its own thickness setting.

Techniques for Highlighting Lines

Varying line thickness is one of the easiest ways to highlight important lines or trends in data. Here are some best practices for selectively emphasizing lines:

Make key lines thicker – Increase the linewidth of lines representing primary data results or model projections. For example, make a fitted model line thicker to stand out from raw input data.

Thicken unpredictable distributions – If the distribution of a data set is unknown or prone to outliers, using a thicker line can help smooth out volatility. Thin lines tend to accentuate variability.

Standardize line roles – Use thickness consistency to represent meaning. For example, always plot original data as thin lines and analyses/models as thick lines.

Establish visual hierarchy – Order line series by importance from thickest to thinnest lines. Guiding the viewer to focus on the most significant relationships first.

Contrast trends – Emphasize exceptional outlier data by making their representing lines extra thick to contrast against standard thickness lines.

Highlighting provides instant visual cues for viewers to navigate plots by drawing their attention. Used judiciously in moderation, selective line thickening can tell a clearer data story.

Best Practices for Choosing Line Widths

When adjusting Matplotlib line thickness, aim for balance. Lines that are too thick clutter up the plot, overwhelm data points, and limit available white space. On the flip side, extra thin lines often get lost and are hard to decipher at small plot sizes.

Here are some line width best practices:

• Only increase thickness 2-3x over default – Greater ratios start obscuring data
• Thicker lines for static plots vs interactive – More width helps clarity when not manipulable
• Thin lines for plots with 10+ series – Help differentiate using color/style instead
• Reduce thickness for publications/prints – Overly thick lines appear bolder on paper
• Standardize widths of similar line types – For example, raw data vs. models vs. projected

Also consider the plot size and context the visualization will be displayed. Key lines can absorb more thickness on wall-sized dashboard monitors versus paper journal figures.

Aim for balance, highlight judiciously, and standardize roles. With practice, setting appropriate line thicknesses becomes second nature.

Common Issues and Solutions with Line Thickness

While adjusting line thickness is straightforward, some common hurdles can appear:

Overcrowded plots – Too many thick lines cover up the data. Use thinner lines, reduce opacity, or plot fewer series per subplot.

Thick lines overpower data points – Markers become hidden behind bold lines. Decrease thickness or increase marker sizes.

Jagged lines from excess thickness – Extremely thick lines reveal aliasing of diagonals. Smooth data beforehand or use curve fitting.

Mixing absolute and relative widths – Absolute thickness works differently across output resolutions. Use exact point values or proportional ratios instead.

Exaggerated thickness in exports – Some file types and printers alter line widths. Preview final output and adjust accordingly.

Appears fine on screen but too thick in print – Screen resolutions hide excess thickness revealed during printing. Test various export formats first.

Troubleshooting thickness issues takes experimentation with balancing line boldness, data point visibility, plot cleanliness, and output target medium. As a rule of thumb, err on the side of thin until a thickness concern forces emboldening. And leverage Matplotlib‘s iterative development flow to quickly visualize adjustments.

Conclusion

Controlling line thickness is an essential customization technique for any Matplotlib user. Tuning width and boldness enables clearer visualization of data, isolates specific trends, and ultimately crafts more readable plots. By following the best practices outlined, both novice and advanced Python developers can utilize line widths to bring their Matplotlib graphs to the next level.

The default thin lines serve as a blank, non-distracting base template. But learning techniques like per-series thickness adjustment, targeted highlighting, hierarchy emphasis, and balancing ratios takes static default plots into dynamic, publication-grade visualizations. Matplotlib‘s flexibility makes intricate control over line widths simple and intuitive.

So whether creating quick data sketches or refined figures for an academic journal, line thickness deserves consideration. Matplotlib puts ease of thickness tweaks close at hand. The path to better visibility and attractive plots starts with a simple line width adjustment.