How can you loop through a subplot grid? # Example dataset #īefore we can demonstrate the plotting methods, we need an example dataset.įor this analysis, we will use a dataset containing the daily closing stock prices of some popular tech stocks and demonstrate how to plot each time-series on a separate subplot. In this post, I outline two different methods for plotting subplots in a single loop which I find myself using on a regular basis. While this gives you a lot of flexibility it can be overwhelming and difficult to understand the best way to do things, particularly when starting out or learning new functionality. One strength, but also arguably one of Matplotlib’s biggest weaknesses, is its flexibility which allows you to accomplish the same task in many different ways. So what can we do in this situation? We have a list of items we want to plot and we have a list of lists with our subplots, is there a way to conveniently plot our data in a single for loop? This is because, when creating the subplot grid using plt.subplots, you are returned list of lists containing the subplot objects, rather than a single list containing of subplot objects which you can iterate through in a single for loop (see below): However, when using Matplotlib’s plotting API it is not straightforward to just create a grid of subplots and directly iterate through them in conjunction with your list of plotting attributes. total order value by day) on a grid of individual subplots. a list of customer IDs) and sequentially plot their values (e.g. In an ideal world, you would like to be able to iterate this list of items (e.g. ![]() For example, when you have a list of attributes or cross-sections of the data which you want investigate further by plotting on separate plots. When carrying out exploratory data analysis (EDA), I repeatedly find myself Googling how to plot subplots in Matplotlib using a single for loop. other options for subplots using Pandas inbuilt methods and Seabornįor this post are available in this Github repository Problem Statement #.how to dynamically adjust the subplot grid layout.two different methods for populating Matplotlib subplots.Line 7-10: Index the ax array to plot different subplots on the figure fig.Trouble getting to grips with the Matplotlib subplots API? This post will go through:.Line 5: Generate some data using numpy.Line 4: Generate a figure with 2 rows and 2 columns of subplots.Line 1-2: Import matplotlib.pyplot for plotting and numpy for generating data to plot.Here is an example on how to use the method: ![]() ax: A single object of the axes.Axes object if there is only one plot, or an array of axes.Axes objects if there are multiple plots, as specified by the nrows and ncols.fig: The object to be used as a container for all the subplots.Here is an explanation of the tuple returned by the function: **fig_kw: Any additional keyword arguments to be passed to pyplot.figure call.gridspec_kw: Dict of grid specifications passed to GridSpec constructor to place grids on each subplot.subplot_kw: Dict of keywords to be passed to the add_subplot call to add keywords to each subplot.squeeze: Boolean value specifying whether to squeeze out extra dimension from the returned axes array ax.Possible values are none, all, row, col or a boolean with a default value of False. sharex, sharey: Specifies sharing of properties between axes.Both of these are optional with a default value of 1. ![]() ![]()
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