#Pyplot subplot code

#Pyplot subplot code

The output of Code#2 ( Code in which we use subplots_adjust() with parameter right )

Now, let’s see the difference in both the outputs of the code, so we clearly understand what basically subplots_adjust function does. In the above code #2, we implement the subplots_adjust function with parameter right. The above code #1 is just a simple matplotlib subplot code. Let’s do an example for understanding the concepts:

hspace: specifies the size of height for white space between subplots (called padding), as a fraction of the average Axes height.

wspace: specifies the size of width for white space between subplots (called padding), as a fraction of the average Axes width.

top: specifies the top position of the subplots of the figure.

bottom: specifies the bottom position of the subplots of the figure.

right: specifies the right position of the subplots of the figure.left: specifies the left position of the subplots of the figure.In the above syntax the following parameters are used which are outlined below: The syntax for subplots_adjust() is as follows: _adjust(left=None,bottom=None,right=None,top=None,wspace=None,hspace=None) The subplots_adjust() is a function in matplotib library, which is used to adjust or refine the subplot structure or design. Matplotlib subplot_adjust not working Matplotlib subplots_adjust You can also achieve higher flexibility using ‘gridspec’ and ‘subplots’, see details here.24. Plt.subplots() is recommended for generating multiple subplots in grids. (I personally prefer this for individual plot). You can see artist tutorial for more details. Plt.figure() is usually used when you want more customization to you axes, such as positions, sizes, colors and etc. Most of the kwargs that plt.figure takes plt.subplots also takes. Plt.figure just creates a figure (but with no axes in it) whereas plt.subplots takes optional arguments (ex: plt.subplots(2, 2)) to create an array of axes in the figure. In matplotlib, we can plots in two ways like below: plt.figure(1,figsize=(400,8))Īnd though both are correct, they have their differences. Question 3: What is the difference between plt.subplots() and plt.figure() The combination of the correct Locator and Formatter gives very fine control over the tick locations and labels. The location of the ticks is determined by a Locator object and the ticklabel strings are formatted by a Formatter. They take care of setting the graph limits and generating the ticks (the marks on the axis) and ticklabels (strings labeling the ticks). These are the number-line-like objects (circled in green). Each Axes has a title (set via set_title()), an x-label (set via set_xlabel()), and a y-label set via set_ylabel()). The Axes contains two (or three in the case of 3D) Axis objects (be aware of the difference between Axes and Axis) which take care of the data limits (the data limits can also be controlled via set via the set_xlim() and set_ylim() Axes methods). A given figure can contain many Axes, but a given Axes object can only be in one Figure. This is what you think of as a plot, it is the region of the image with the data space (marked as the inner blue box). In the context of matplotlib, axes is not the plural form of axis, it actually denotes the plotting area, including all axis. Question 2: Difference between “axes” and “axis” in matplotlib? This window will be just divided in 4 parts with my example.įigure2, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) This plot 4 figures which are named ax1, ax2, ax3 and ax4 each one but on the same window. Or you can plot multiple figures like this: fig1, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) Plot one or several figure(s) in the same window If you just want to get one graphic, you can use this way. Plot just one figure with (x,y) coordinates plt.plot(x, y)

fig1, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)Īfter reading through a bunch of stackoverflow explainations, I compiled them here: Question 1: What is the difference between drawing plots using plot, axes or figure in matplotlib? When working with python libraries, especially for visualization, I usually get confused my number of options available for plotting.