More discussion and examples of available arrow and annotation styles can be found in the Matplotlib gallery, in particular the Annotation Demo. Unfortunately, it also means that these sorts of features often must be manually tweaked, a process that can be very time consuming when producing publication-quality graphics! Finally, I’ll note that the preceding mix of styles is by no means best practice for presenting data, but rather included as a demonstration of some of the available options. You’ll notice that the specifications of the arrows and text boxes are very detailed: this gives you the power to create nearly any arrow style you wish. # Format the x axis with centered month labelsĪx.t_major_locator(())Īx.t_minor_locator((bymonthday=15))Īx.t_major_formatter(plt.NullFormatter())Īx.t_minor_formatter(('%h')) Let’s demonstrate several of the possible options using the birthrate plot from before:In : fig, ax = plt.subplots(figsize=(12, 4))Īx.annotate("New Year's Day", xy=('', 4100), xycoords='data', These options are fairly well-documented in Matplotlib’s online documentation, so rather than repeating them here it is probably more useful to quickly show some of the possibilities. The arrow style is controlled through the arrowprops dictionary, which has numerous options available. ![]() Mu, sig = quartiles, 0.74 * (quartiles - quartiles)īirths = births.query('(births > - 5 * & (births ",Ĭonnectionstyle="angle3,angleA=0,angleB=-90")) We’ll start with the same cleaning procedure we used there, and plot the results:In : births = pd.read_csv('data/births.csv') Let’s return to some data we worked with earler, in “Example: Birthrate Data”, where we generated a plot of average births over the course of the calendar year as already mentioned, that this data can be downloaded at. Import numpy as np import pandas as pd Example: Effect of Holidays on US Births ![]() Import matplotlib.pyplot as plt import matplotlib as mpl We’ll start by setting up the notebook for plotting and importing the functions we will use:In : % matplotlib inline Let’s take a look at some data and how we might visualize and annotate it to help convey interesting information. Perhaps the most basic types of annotations you will use are axes labels and titles, but the options go beyond this. In some cases, this story can be told in an entirely visual manner, without the need for added text, but in others, small textual cues and labels are necessary. ax.transData : Transform associated with data coordinates ax.transAxes : Transform associated with the axes (in units of axes dimensions) fig.transFigure. This seems to me a very strange error.Creating a good visualization involves guiding the reader so that the figure tells a story. Why is the yRadius of the first cell wrong here? After trying out a bunch of different values for xlim and ylim I found the pattern that the minimum of the two radii in the first cell is correct, while the other one is off. I think the bottom cell's values are correct, because since the ax aspect is set to be equal, the two radii should be the same. Why are the values of the two cells not the same? Although the xRadius of the first cell matches the values of the bottom cell, the yRadius is off. In a cell below the above cell, if I run yradius = (ax.ansform() - ax.ansform()) ![]() To demonstrate, here is code running the transformation in the cell where fig, ax = plt.subplots() is declared: fig, ax = plt.subplots() However, if you run ax.ansform in a later cell, it returns the correct values. Basically, in the cell where fig, ax = plt.subplots() is declared, running ax.ansform for either the x or y axis is incorrect. I was suffering a really bizarre issue with matplotlib's ax.ansform in ipython.
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