![]() Setting the title, axis labels, ticks, and tick labels Each corresponds to two methods on the subplot object itself in the case of xlim, these are ax.get_xlim and ax.set_xlim. They can be used in two ways:Ĭalled with no arguments returns the current parameter value (e.g., ax.xlim() returns the current x-axis plotting range)Ĭalled with parameters sets the parameter value (e.g., ax.xlim() sets the x-axis range to 0 to 10)Īll such methods act on the active or most recently created AxesSubplot. ![]() These control the plot range, tick locations, and tick labels, respectively. This includes methods like xlim, xticks, and xticklabels. Most kinds of plot decorations can be accessed through methods on matplotlib axes objects. You must call ax.legend to create the legend, whether or not you passed the label options when plotting the data. Here is a small example you can execute in Jupyter where I shrink the spacing all the way to zero (see Data visualization with no inter-subplot spacing): Wspace and hspace control the percent of the figure width and figure height, respectively, to use as spacing between subplots. You can change the spacing using the subplots_adjust method on Figure objects: subplots_adjust(left=None, bottom=None, right=None, top=None, This spacing is all specified relative to the height and width of the plot, so that if you resize the plot either programmatically or manually using the GUI window, the plot will dynamically adjust itself. Table 9.1: options ArgumentĪll subplots should use the same x-axis ticks (adjusting the xlim will affect all subplots)Īll subplots should use the same y-axis ticks (adjusting the ylim will affect all subplots)ĭictionary of keywords passed to add_subplot call used to create each subplotĪdditional keywords to subplots are used when creating the figure, such as plt.subplots(2, 2, figsize=(8, 6))īy default, matplotlib leaves a certain amount of padding around the outside of the subplots and in spacing between subplots. To set this up, execute the following statement in a Jupyter notebook: %matplotlib inline The simplest way to follow the code examples in the chapter is to output plots in the Jupyter notebook. One of these is seaborn, which we explore later in this chapter. Over time, matplotlib has spawned a number of add-on toolkits for data visualization that use matplotlib for their underlying plotting. With the exception of a few diagrams, nearly all of the graphics in this book were produced using matplotlib. matplotlib supports various GUI backends on all operating systems and can export visualizations to all of the common vector and raster graphics formats (PDF, SVG, JPG, PNG, BMP, GIF, etc.). The matplotlib and IPython communities have collaborated to simplify interactive plotting from the IPython shell (and now, Jupyter notebook). The project was started by John Hunter in 2002 to enable a MATLAB-like plotting interface in Python. Matplotlib is a desktop plotting package designed for creating plots and figures suitable for publication. ![]() Python has many add-on libraries for making static or dynamic visualizations, but I’ll be mainly focused on matplotlib and libraries that build on top of it. For others, building an interactive visualization for the web may be the end goal. It may be a part of the exploratory process-for example, to help identify outliers or needed data transformations, or as a way of generating ideas for models. Making informative visualizations (sometimes called plots) is one of the most important tasks in data analysis. The code examples are MIT licensed and can be found on GitHub or Gitee. The content from this website may not be copied or reproduced. If you find the online edition of the book useful, please consider ordering a paper copy or a DRM-free eBook to support the author. If you encounter any errata, please report them here. This Open Access web version of Python for Data Analysis 3rd Edition is now available as a companion to the print and digital editions.
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