Dec 6, 2022 · In this article, we will discuss adding labels to the plot using Matplotlib in Python. But first, understand what are labels in a plot. The heading or sub-heading written at the vertical axis (say Y-axis) and the horizontal axis(say X-axis) …

Apr 15, 2023 · If you want to show the labels next to the lines, there's a matplotlib extension package matplotx (can be installed via pip install matplotx[all]) that has a method that does that. N.B. A similar task using only matplotlib would look like: to add label. plt.xlabel (' insert your label ') plt.ylabel (' insert your label ')

Mar 22, 2025 · Adding value labels. To add text labels on top of the bars, we use the plt.text() function. Syntax: plt.text(x, y, s, ha, bbox) Parameters: x, y: Coordinates where the text will be displayed. s: The string (value label) to be displayed. ha: …

ylabel() functions to set a label for the x- and y-axis. Add labels to the x- and y-axis: With Pyplot, you can use the title() function to set a title for the plot. Add a plot title and labels for the x- and y-axis: You can use the fontdict parameter in xlabel(), ylabel(), and title() to set font properties for the title and labels.

Apr 6, 2017 · In Pandas version 1.10 you can use parameters xlabel and ylabel in the method plot: df.plot(xlabel='X Label', ylabel='Y Label', title='Plot Title')

Jul 30, 2013 · import matplotlib.pyplot as plt. plt.plot([1,2,3],'r-',label='Sample Label Red') plt.plot([0.5,2,3.5],'b-',label='Sample Label Blue') plt.legend() plt.show() This will plot 2 lines as shown: The arrow function supports labels. Do check this link: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.arrow.

Examples of adding text, labels, and annotations in Matplotlib plots.

Jun 23, 2018 · Add labels to points in scatter plots. Loop over the data arrays (x and y) and call plt.annotate(<label>, <coords>) using the value itself as label:

Jun 27, 2024 · In this article, we have explored various ways to add labels to different parts of a plot in Matplotlib. Labels such as titles, axis labels, legends, annotations, grid lines, color bars, and more play a crucial role in providing context and understanding to the plotted data.

In this tutorial, we're going to cover legends, titles, and labels within Matplotlib. A lot of times, graphs can be self-explanatory, but having a title to the graph, labels on the axis, and a legend that explains what each line is can be necessary. To start: import matplotlib.pyplot as plt x = [1,2,3] y = [5,7,4] x2 = [1,2,3] y2 = [10,14,12]