Linestyles in Matplotlib Python

Matplotlib is a Python library for plotting graphs and visualizing data. It is also used for creating static, animated, and interactive visualizations and data visualization. The Matplotlib library was originally developed by John D. Hunter in 2003 and now has a large community of developers.

Some Key Properties of Matplotlib:

Matplotlib can be used to plot charts, graphs, line plots, scatter plots, bar plots, histograms and charts such as pie charts and many more.
Matplotlib provides extensive customization options to control each aspect of the plot, including line styles, colors, markers, labels and annotations.
Matplotlib can easily integrate with NumPy, which makes it easy to plot the data directly.
High-quality plots and visuals are made by Matplotlib, which is suitable for publication with fine-grained control over the plot aesthetics.
It is a highly extensible library that provides add-on toolkits and extensions, such as Seaborn, pandas plotting functions and a base map for geographical plotting.
Matplotlib is platform independent and can be run on various OS, which include windows, macOS and Linux.
Interactive plots are supported by Matplotlib, which consists of widgets and event handling, enabling users to explore data dynamically.

There Are Basic Components of Matplotlib:

Figures in Matplotlib: This is a top-level container for all elements of the plot. This is a type of canvas on which the plot is drawn. It can be thought of as a blank sheet of paper on which the visuals and graphs are created.
Axes: These are rectangular areas within the figures where the data is plotted. In matplotlib, each figure includes one or more axes, which are arranged in rows and columns if necessary. Axes provide the coordinate system and are where most of the plotting occurs.
Axis in Matplotlib: It shows the plot's x-axis and y-axis. There are various properties that can be defined, such as data limits, tick locations, tick labels, and axis labels. Each axis in the matplotlib has a scale and a locator, which is used to determine how the marks are spaced.
Marker in Matplotlib: These are symbols that denote individual data points in a plot. It can be shaped such as circles, squares, triangles, or custom symbols. Markers are basically used in scatter plots with connected points and other types of plots. The relationship or trend between the data points is represented and can be styled with different colors, widths, and styles to show additional information.
Adding lines to Figures: The data points on a plot are connected by the lines and are basically used in line plots, scatter plots with connected points, and other types of plots.
Titles in Matplotlib: The title in matplotlib is used to set the text element that offers a descriptive title for the plot. It appears at the top of the figure and provides context or information.
Axis labels in Matplotlib: Labels are used to give the title to the matplotlib axis, and these are text elements used to find the data being plotted and provide units or other relevant information.
Ticks: Tick marks are small marks that is used to axis which indicate specific data points or intervals. Ticks are used to interpret the scale of the plot and locate the data values that are specific to the data points.
Tick Labels in Matplotlib: These are the elements of the text that give the labels for the tick marks. The data values are displayed by these ticks corresponding to each tick mark, and to show specific units, they can be customized.
Legend in Matplotlib: These are matplotlib properties that are used to show color or symbols in the plot to show different data points, series or categories, which help users understand the plot and interpret the meaning of each element.
Grid Lines in Matplotlib: These are the horizontal and vertical lines that are used to extend the plot specific to data intervals or divisions. These grid lines are used as visual guides to the data and help identify patterns or trends.
Spines of Matplotlib Figures: These are lines that is use to form borders of the plot area. The plot is separated from the surrounding whitespace and can be customized to change the representation of the plot borders.

Different Types of Plots in Matplotlib:

There are several plots provided by matplotlib to visualize the different types of data. Some matplotlib plots are commonly used.

Line Graph
Stem Plot
Bar chart
Histograms
Scatter plot
Box plot
Pie chart
Error plot
Violin plot
3D plot

The classic styles and built-in styles are provided by Matplotlib, which are reminiscent of traditional scientific plots and modern styles with vibrant colors and sleek lines for presentation or grayscale printing; specialized styles are used for specific purposes. Matplotlib gives the facility for customizing the plot styles to match the preferences or corporate branding, which ensures that the visualization is both informative and visually appealing. The are various other properties also.

Python Pyplot
Figure Class
Axes Class
Setting colors
Adding text, font and grid lines in the plot
Customizing the legend in Matplotlib
Ticks and Tick Labels
Style plots
Creating Multiple subplots in Matplotlib
Adding images to matplotlib plots.

The popularity of matplotlib is because of its ease of use, documentation, and many plots available in matplotlib. Matplotlib provides customization flexibility, supports a wide range of plot types and can be integrated with other Python libraries such as NumPy and Pandas. It is suitable for several data visualization works, which include exploratory data analysis, plotting scientific graphs, and creating publication-quality plots. It is enhanced in situations where there are requirements for fine-grained control over the customization of plots and there is a need for the creation of complex or specialized visualizations.

Advantages of Matplotlib:

Matplotlib is widely used in visualizing data, and it is a plotting library provided by Python that as several plotting tools and capabilities for plotting different types of graphs, charts and plots. Some advantages of matplotlib are discussed below:

Matplotlib is versatile and can be used to plot wide range of charts and graphs, such as line plots, scatter plots, bar plots, histograms, pie charts and many more.
Matplotlib provides customization options to control or customize the each and every aspect of the plot, which includes line styles, colors, markers, labels and annotations.
Matplotlib can also integrate with different Python libraries to visualize the data such as NumPy, which makes it is easy to plot data directly and efficiently.
Matplotlib can also be used to plot high-quality plots, which are suitable for publication with fine-grained control.
Because of flexibility, matplotlib is widely used for scientific and engineering tasks.
Matplotlib provides extensibility, with add-on toolkits and extensions such as seaborn, Pandas plotting functions and base map for plotting the geographical data.
Matplotlib is independent of the platform and can be run on different operating systems, which include windows, macOS and Linux.
Interactive plots are plotted with the help of matplotlib, and widgets can be used to handle the event, which enables users to explore the data dynamically.
Matplotlib can be easily integrated with Jupyter Notebook for interactive plotting and display of inline plots.
Matplotlib is rich in documentation and has a supporting community of users and developers, which makes it easy to get help, tutorials and examples.

Disadvantages of Matplotlib:

While there are many advantages of matplotlib, there are some disadvantages of matplotlib also which are discussed below:

Steep Learning Curve: Due to the extensive customization of the matplotlib and its complex syntax, beginners can experience a steep learning curve in their initial phase.
The syntax of matplotlib can be verbose and less intuitive compared to other plotting libraries, such as Seaborn or plotly, which makes it more time-consuming to create and customize plots.
The plots that are default aesthetics in matplotlib are often considered less visually appealing when compared to other libraries.
There are many interactive features that matplotlib does not offer. On the other hand, matplotlib does not support interactive plotting.
For 3D visualization, matplotlib is not as advanced in capabilities and user-friendly as some other specialized 3D plotting libraries.
Since matplotlib is rich in documentation, but for some users, it is challenging to navigate, and error messages can sometimes be cryptic and hard to debug.
When visualizing large datasets, matplotlib can be performed slowly and less efficiently when it comes to plotting large datasets, especially when compared to more optimized plotting libraries.
Matplotlib can lead to compatibility issue as it depends on other external libraries such as NumPy and SciPy for many functionalities and there a issue in dependency management also.
Basic statistical plots can be plotted by matplotlib, but there is a lack of advanced statistical plotting capabilities available, such as Seaborn.

Linestyles in Matplotlib:

Matplotlib provides many line-style methods to improve the aesthetic of the plots. Using line styles in the plots makes the plots more interactive. The line patterns that can be plotted on the line are defined by the line styles in Matplotlib. There are various linestyle methods present in matplotlib, such as solid, dashed, dash-dot, and dotted lines, which are the most popular line types provided by matplotlib. Let's see a simple code:

Code:

 
import numpy as np
import matplotlib.pyplot as plt
# Define a simple range of values for x and reshape it so it can be used in multiple plots
x = np.linspace(0, 10, 1000).reshape(-1,1)

# Set up the figure and axis
fig, ax = plt.subplots(1,1, figsize=(10,6))

# solid line style
ax.plot(x, np.sin(x), linestyle='-', label='solid')

# dashed line style
ax.plot(x, np.cos(x), linestyle='--', label='dashed')

# dashdot line style
ax.plot(x, -np.sin(x), linestyle='-.', label='dashdot')

# dotted line style
ax.plot(x, -np.cos(x), linestyle=':', label='dotted')

ax.legend()

plt.show()   

Output:

Explanation:

In the above code, numpy module and matplotlib module is imported. NumPy module is used to generate the data and several line plots are created. First, a subplot is created with 1 row and 1 column, and the figure size of the subplot is defined as 10, 6. Various line plots are plotted with the line style as solid, dashed, dashdot and dotted with the labels.

Line Style Shortcodes:

Code:

 
import numpy as np
import matplotlib.pyplot as plt
# Define a simple range of values for x and reshape it so it can be used in multiple plots
x = np.linspace(0, 10, 1000).reshape(-1,1)

# Set up the figure and axis
fig, ax = plt.subplots(1,1, figsize=(10,6))

# solid line style
ax.plot(x, np.sin(x), linestyle='-', label='solid')

# dashed line style
ax.plot(x, np.cos(x), linestyle='dashed', label='dashed')

# dashdot line style
ax.plot(x, -np.sin(x), linestyle='dashdot', label='dashdot')

# dotted line style
ax.plot(x, -np.cos(x), linestyle='dotted', label='dotted')

ax.legend()

plt.show()   

Output:

Explanation:

In the above code, the linestyles are replaced by the dashed, dashdot and datted values, and the code remains the same.

Line Styles with Custom Spacing:

Code:

 
import numpy as np
import matplotlib.pyplot as plt
# Define a simple range of values for x and reshape it so it can be used in multiple plots
x = np.linspace(0, 10, 1000).reshape(-1,1)

fig, ax = plt.subplots(1,1, figsize=(10,6))

# Define custom line styles
line_styles = [(0, (1, 10)), (0, (1, 1)), (0, (5, 10)), (0, (5, 5))]

for i, style in enumerate(line_styles, start=1):
   ax.plot(x, i * np.sin(x), linestyle=style, label=f'line style {i}')

ax.legend()

plt.show()   

Output:

Explanation:

In the above code, a tuple is defined for the line style, and the offset is the tuple's first value. The second value in the tuple specifies the dash and the lengths of the spaces.

Combining Line Styles with Colors:

Code:

 
import numpy as np
import matplotlib.pyplot as plt
# Define a simple range of values for x and reshape it so it can be used in multiple plots
x = np.linspace(0, 10, 1000).reshape(-1,1)

fig, ax = plt.subplots(1,1, figsize=(10,6))

ax.plot(x, np.sin(x), linestyle='-', color='blue', label='solid blue')
ax.plot(x, np.cos(x), linestyle='--', color='red', label='dashed red')
ax.plot(x, -np.sin(x), linestyle='-.', color='green', label='dashdot green')
ax.plot(x, -np.cos(x), linestyle=':', color='purple', label='dotted purple')

ax.legend()

plt.show()   

Output:

Explanation:

In the above code, various trigonometric functions are plotted, and the line styles are set for the plots.

Line Style and Marker Combination:

Code:

 
import numpy as np
import matplotlib.pyplot as plt
# Define a simple range of values for x and reshape it so it can be used in multiple plots
x = np.linspace(0, 10, 1000).reshape(-1,1)

fig, ax = plt.subplots(1,1, figsize=(10,6))

# Define a smaller range for x
x = np.linspace(0, 10, 10)

ax.plot(x, np.sin(x), linestyle='-', marker='o', label='solid with marker')
ax.plot(x, np.cos(x), linestyle='--', marker='x', label='dashed with marker')

ax.legend()

plt.show()   

Output: