Understanding Matplotlib Line Plot
Matplotlib Line Plot provides functionalities to create high-quality line plots to represent data in a clear and concise manner.
In this article, we will take a closer look at how to generate line plots with examples and different customization options.
Matplotlib Line Plot
To create a Matplotlib line plot, we first need to import the library and define the data points that we want to plot.
Here is an example:
In above example, we defined two lists, x and y, that represent the x and y coordinates of our data points. We then created a line plot using the plt.plot() function, passing in the x and y lists as arguments.
Finally, we used the plt.show() function to display the plot.
Customizing Matplotlib Line Plots
Matplotlib provides various customization options to make our line plots more informative and visually appealing.
Here are some common customization options that we can use:
The default line style is a solid line, but we can also use dashed, dotted, or dash-dot lines.
You can customize the style of the plotted line through the keyword argument linestyle, or simply ls:
In the following example utilize linestyle = ‘dotted’ argument:
Implement the dotted line style with a pentagon marker:
In the below example apply linestyle = ‘dashed‘ argument:
Utilize the dashed line style with a hexagon marker:
There is a shorter syntax for the line style:
- Linestyle is represented by ls.
- dotted is represented as :.
- Dashed is represented as —.
Implement the simple version of the line style argument:
First apply the marker argument then set the line style to ‘dotted‘:
These are the styles you can pick from:
|‘None’||” or ‘ ‘|
If you want to modify the color of the line, you can pass the keyword argument color or the smaller version c:
Make the line color black in the below example:
Utilize the simpler version of color c:
You can also generate your own color with a hexadecimal code:
Display the following graph with ‘#003d4d‘ color:
Alternatively, you can select any of the 140 available color names.
Show the graph below with tomato color:
Insert the ‘yellowgreen‘ color in the c argument:
To modify the width of the line, you can choose from the keyword argument linewidth or the simpler lw argument.
The value of linewidth is represent in a points form.
Increase the weight of the following graph line:
Apply the simple version of the linewidth argument with the ‘10.5‘ value:
If you provide multiple plt.plot() functions, then you will be able to generate the number of lines you want.
To sketch each line, utilize the plt.plot() function:
Implement the plt.plot() function two times for two lines:
Through the plt.plot() function, you can display many lines by including x- and y-axis points for each line.
As you can see in the example above, we only defined the points on the y-axis, which means that the points on the x-axis got their default values (0, 1, 2, 3) as well.
Provide the x1, y1, x2 and y2 point values for both lines and plot two lines:
Display the two lines in the following example with x and y points values:
The above example utilizes the Matplotlib library to plot a graph.
- It defines four arrays, x1_plane, x2_plane, y1_plane, and y2_plane, which represent the x and y coordinates for two planes.
- The data in these arrays is then used to plot a graph using the “plot” function from Matplotlib.
- The graph is plotted with dotted lines (“ls = ‘:'”), thick line width (“lw = ‘5.5’”), and black color (“color = ‘Black'”).
Finally, the “show” function is used to display the graph.
Matplotlib Line Plots Benefits
- Matplotlib line plots are straightforward to create with only a few lines of code, making them ideal for data analysts and scientists who require quick visualization of their data.
- They are particularly helpful for displaying trends over time, enabling easy comparison of data changes and identification of patterns and outliers.
- Matplotlib line plots are highly customizable, enabling users to adjust the color, line style, width, and marker style of the lines to create visually appealing and effective plots that communicate the intended message.
- Matplotlib line plots can handle massive datasets with ease, allowing users to plot thousands of data points without performance loss.
- Matplotlib integrates well with other Python libraries such as NumPy and Pandas, making it easy to create line plots from data stored in these libraries.