![]() It will become more clear as we discuss a few examples. Similarly, the function helps us label graphs generated with the help of matplotlib. Suppose you draw a VI characteristic graph in this, you label the x-axis as V(voltage) and the y-axis as I(current). So, in general, the term annotate means to label something. Now coming back to our function Matplotlib Annotate. It comes very handy when dealing with writing programs for data science. With this library’s help, we plot different graphs justifying our programs. It is the plotting library of Python and an extension to the NumPy library. But before that, I will give you an overview of the Matplotlib library. Then we will see the application of all the theory part through a couple of examples.įirst, let us try to develop a brief understanding of Matplotlib Annotate. Along with that, for an overall better understanding, we will also look at its syntax and parameter. Hello geeks and welcome in this article, we will cover Matplotlib Annotate. annotate ( 'outside the plot \n relative to figure window', xy = ( 20, 75 ), xycoords = 'figure pixels', horizontalalignment = 'left', verticalalignment = 'top', bbox = bbox_props )Īx. ![]() # third annotation relative to the figure window bbox_props = dict ( boxstyle = "larrow,pad=0.5", fc = "w", ec = "k", lw = 2 )Īx. annotate ( 'half of range \n relative to axis limits', xy = ( 0, 0.5 ), xycoords = 'axes fraction', xytext = ( 0.2, 0.5 ), bbox = bbox_props, arrowprops = dict ( facecolor = 'black', shrink = 0.05 ), horizontalalignment = 'left', verticalalignment = 'center' ) # second annotation relative to the axis limits bbox_props = dict ( boxstyle = "round,pad=0.5", fc = "w", ec = "k", lw = 2 )Īx. ![]() annotate ( 'function minium \n relative to data', xy = ( 0, 0 ), xycoords = 'data', xytext = ( 2, 3 ), arrowprops = dict ( facecolor = 'black', shrink = 0.05 ), horizontalalignment = 'left', verticalalignment = 'top' ) # first annotation relative to the data ax. Import numpy as np import matplotlib.pyplot as plt # if using a Jupyter notebook, include: % matplotlib inline The next code section builds a figure with three annotation arrows. ax.annotate() keywordĪnnotation location relative to figure windowĭefine bounding box properties with a dictionaryĭefine arrow properties with a dictionary The chart below summarizes Matplotlib's ax.annotate() keyword arguments. Since xy=(20, 75), the third annotation arrow points 20 pixels to the right and 75 pixels up from the bottom left corner of the figure window. This means the third annotation is placed relative to the figure window. In the third annotation, xycoords='figure pixels'. Since xy=(0, 0.5), the annotation arrow points all the way to the left edge of the x-axis and half way up the y-axis. This means the second annotation is placed relative to the axis. In the second annotation, xycoords='axes fraction'. Since xy=(0, 0), the annotation arrow points to the data point 0,0. This means the annotation is placed relative to the data. In the first annotation, xycoords='data'. The annotation can be located relative to the plot data, located relative to the axis, or located relative to the figure window. Each of the three annotations in the next figure has a different xycoords= keyword argument. The keyword argument to pay attention to in the next code section is xycoords=. ax.annotate('text', xy=, xycoodrs=, xytext=, arrowprops= ) Multiple keyword arguments can be passed to ax.annotate() method to specify the annotation location and style the annotation. ![]() ![]() Matplotlib's ax.annotate() method creates the annotations. The code section below builds a simple line plot and applies three annotations (three arrows with text) on the plot. Text can be included on a plot to indicate a point of interest or highlight a specific feature of a plot. Sometimes it is useful for problem solvers to annotate plots. Problem Solving with Python Book Construction ![]()
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