{"id":123023,"date":"2022-03-19T22:57:43","date_gmt":"2022-03-19T22:57:43","guid":{"rendered":"https:\/\/blog.finxter.com\/?p=250394"},"modified":"2022-03-19T22:57:43","modified_gmt":"2022-03-19T22:57:43","slug":"is-there-a-list-of-line-styles-in-matplotlib","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2022\/03\/19\/is-there-a-list-of-line-styles-in-matplotlib\/","title":{"rendered":"Is There A List Of Line Styles In Matplotlib?"},"content":{"rendered":"

You want to plot a series of data with unique styles. You need to pick various line styles from a list but not sure how to get started. This tutorial will help you out.<\/p>\n

Yes, there is a list of line styles in matplotlib<\/a><\/code>. To get the list, import the lines from the matplotlib<\/code> library.<\/p>\n

from matplotlib import lines<\/pre>\n
Next, get the keys from the lineStyle<\/code> attribute.<\/p>\n
lines.lineStyles.keys()<\/pre>\n
You can then print the styles and choose your preferred style per plot.<\/p>\n
print(lines.lineStyles.keys())<\/pre>\n
The result is<\/p>\n
dict_keys(['-', '--', '-.', ':', 'None', ' ', ''])<\/pre>\n
As you will see in this tutorial, you can also generate line styles using colors and markers. What is more? Find out below.<\/p>\n
Lab Setup To explore Line Styles In Matplotlib<\/a><\/strong><\/h2>\n
Assume we want to plot the annual earnings of company X’s employees (designers, developers, and accountants) aged between 20 and 29. We store the ages in a list as follows.<\/p>\n
ages = [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]<\/pre>\n
Next, we create lists for each group of employees’ salaries.<\/p>\n
designers = [45893, 50375, 52000, 52009, 54000, 55555, 56979, 57040, 55991, 54000]\ndevelopers = [55893, 53375, 52000, 51008, 58000, 58555, 59979, 60050, 65991, 67000]\naccountants = [40000, 41500, 42000, 42508, 43001, 44555, 46979, 48050, 49991, 51000]<\/pre>\n
Now that we have some data to practice various line styles in matplotlib<\/code>, let’s go ahead and manipulate the data.<\/p>\n
Practical Ways To Use Line Styles In Matplotlib<\/h2>\n
\u27a5Get the required functions<\/h3>\n
Install the matplotlib<\/code> library using pip.<\/p>\n
pip install matplotlib\n# OR\npip3 install matplotlib<\/pre>\n
After installing Matplotlib<\/code>, create a file and import the library’s functions into it. I have created one called employees.py<\/code> and imported lines<\/code> and pyplot<\/code> as follows.<\/p>\n
from matplotlib import lines, pyplot as plt<\/pre>\n
We will get the line styles from lines and plot the lines with pyplot<\/code>. It is a convention to shorten pyplot<\/code> as plt<\/code>. In simple words it is an alias.<\/p>\n
\u27a5Plot the data<\/h3>\n
Plotting a bunch of data on a curve requires specifying the values for the x-axis and y-axis.<\/p>\n
plt.plot(<x-axis data list>, <y-axis data list>)<\/pre>\n
We can plot our employee values as follows.<\/p>\n
ages = [20, 21, 22, 23, 24, 25, 26, 27, 28, 29] designers = [45893, 50375, 52000, 52009, 54000, 55555, 56979, 57040, 55991, 54000]\ndevelopers = [55893, 53375, 52000, 51008, 58000, 58555, 59979, 60050, 65991, 67000]\naccountants = [40000, 41500, 42000, 42508, 43001, 44555, 46979, 48050, 49991, 51000] plt.plot(ages, designers)\nplt.plot(ages, developers)\nplt.plot(ages, accountants) plt.show()<\/pre>\n
The plt.show()<\/code> function instructs Python to reveal the graphical information cached in the memory. As soon as you run the file on the terminal, you get three solid lines of different colors.<\/p>\n
python employees.py<\/pre>\n
\"List<\/figure>\n
Now let us label the lines and add a title to the plots for easier identification. Insert the code before the plt.show()<\/code> line.<\/p>\n
plt.xlabel(\"Ages\")\nplt.ylabel(\"Annual salary in USD\")\nplt.title(\"Salaries of X employees for 20-29 ages\")<\/pre>\n
Output:<\/strong><\/p>\n
\"List<\/figure>\n
However, we cannot tell what the green, blue, and coral lines represent. So, let’s mark the lines using labels and a legend.<\/p>\n
\u27a4labels<\/em><\/p>\n
plt.plot(ages, designers, label='designers')\nplt.plot(ages, developers, label='developers')\nplt.plot(ages, accountants, label='accountants')<\/pre>\n
\u27a4legend<\/em> represents the area that describes specific elements of the graph. In this case, we will use the legend()<\/code> method to describe the labels assigned previously.<\/p>\n
plt.legend()\nplt.show()<\/pre>\n
Output:<\/strong><\/p>\n
\"Displaying<\/figure>\n
Full code:<\/strong> Here’s the full code that helps to represent our data in the form of different lines. <\/p>\n
from matplotlib import lines, pyplot as plt ages = [20, 21, 22, 23, 24, 25, 26, 27, 28, 29] designers = [45893, 50375, 52000, 52009, 54000, 55555, 56979, 57040, 55991, 54000]\ndevelopers = [55893, 53375, 52000, 51008, 58000, 58555, 59979, 60050, 65991, 67000]\naccountants = [40000, 41500, 42000, 42508, 43001, 44555, 46979, 48050, 49991, 51000] plt.plot(ages, designers, label='designers')\nplt.plot(ages, developers, label='developers')\nplt.plot(ages, accountants, label='accountants') plt.xlabel(\"Ages\")\nplt.ylabel(\"Annual salary in USD\")\nplt.title(\"Salaries of X employees for 20-29 ages\") plt.legend() plt.show()<\/pre>\n
Looks better, right? Yes, but we can improve the visuals by changing the line styles.<\/p>\n
\"\ud83d\udca1\"Change The Line Styles<\/strong><\/h2>\n
We assigned different labels to distinguish different data but is there a way to change the style of the line? Yes! The first step is to grab the lines with the help of the lines<\/code> module. You can import lines from matplotlib as we have done above or get them from Matplotlib line style docs<\/a>.<\/p>\n
Let’s print the styles from the imported lines. After commenting out the plt.show()<\/code> method, run the print line below the import statement.<\/p>\n
print(lines.lineStyles)<\/pre>\n
We get a dictionary<\/a> which represents the type of styles in which the lines can be represented as follows:<\/p>\n
{'-': '_draw_solid', '--': '_draw_dashed', '-.': '_draw_dash_dot', ':': '_draw_dotted', 'None': '_draw_nothing', ' ': '_draw_nothing', '': '_draw_nothing'}<\/code><\/pre>\n
Let’s find the keys and store them as list_styles<\/code>.<\/p>\n
line_styles = lines.lineStyles.keys()<\/pre>\n
We get a list on running printing line_styles<\/code>.<\/p>\n
print(line_styles)<\/pre>\n
dict_keys(['-', '--', '-.', ':', 'None', ' ', ''])<\/code><\/pre>\n
Let’s use the result to change line styles for the plots.<\/p>\n
Combining the lines.lineStyles.keys()<\/code> and pyplot.linestyle<\/code> helps us change the designers<\/code> and developers<\/code> plots as follows.<\/p>\n
from matplotlib import pyplot as plt ages = [20, 21, 22, 23, 24, 25, 26, 27, 28, 29] designers = [45893, 50375, 52000, 52009, 54000, 55555, 56979, 57040, 55991, 54000]\ndevelopers = [55893, 53375, 52000, 51008, 58000, 58555, 59979, 60050, 65991, 67000]\naccountants = [40000, 41500, 42000, 42508, 43001, 44555, 46979, 48050, 49991, 51000] plt.plot(ages, designers, label='designers', linestyle='--')\nplt.plot(ages, developers, label='developers', linestyle=':')\nplt.show()<\/pre>\n
Output:<\/strong><\/p>\n
\"\"<\/figure>\n
Here, lines.lineStyles<\/code> show the styles, while pyplot<\/code>‘s linestyle<\/code> attribute changes the lines graphically.<\/p>\n
\"\ud83d\ude0e\"<\/strong> Tidbits<\/strong><\/p>\n