This can be improved by using plt.tight_layout() By default, the layout of the plots in a grid of sub-plots doesn’t use up the available space particularly well.Because ax.set_title() creates the title for the individual plot referenced by ax, in order to create a title for the entire figure you need to use plt.suptitle().plt.rc('figure', figsize=(w, h)) to set the same figsize parameter as above but for all the figures in your code.plt.figure(figsize=(w, h)) to set the width and height of the figure you are currently working on.This topic is discussed on it’s own separate page but, in short, your options are: In order to create enough space for all of these plots, it’s a good idea to re-size your figure.The plots are numbered using ‘reading order’ (left-to-right, top-to-bottom), ie plot 2 will be top-right, plot 3 will be middle-left and so on. For example, plt.subplot(321) will divide your figure into a grid with 3 rows and 2 columns (ie space for 6 plots) and then create an axes object for the first (top-left) of these plots. plt.subplot(rcn) will create a sub-plot object where r is the total number of rows of plots you intend to make, c is the number of columns of plots you intend to make and n is the number that this plot will be within the grid of plots.To create two (or more) completely separate plots in the same figure you still need to create axes objects, but now these objects need to be sub-plots as opposed to multiple sets of axes on the same plot: If you want to make it look cleaner by removing the “.0” from the tick labels you can change the lambda function to convert the values from floats to integers: fmt = ticker.FuncFormatter(lambda x, _: int(x * 60)) The graph looks exactly the same as before except the values on the x-axis are now 60 times larger. Y = įmt = ticker.FuncFormatter(lambda x, _: x * 60) Applying this formatting object to the major tick marks on the x-axis with t_major_formatter().Turning this lambda function into a formatting object using FuncFormatter().Using the lambda statement to create a function ‘object’ (called a ‘lambda function’) which takes a dummy variable and multiplies it by 60.In this example, we want to scale up the x-axis by a factor of 60 to convert from minutes to seconds. This can create format objects for the axis tick marks The matplotlib.ticker library to provide access to the FuncFormatter() function.The t_major_formatter() or t_major_formatter() method, depending on which axis you want to change the format of.If you want to change the scale on an axis (eg show the tick marks in units of seconds when your data is in minutes) you can edit its format using the following: Y_4 = Īx.scatter(x_1, y_1, label='First data set')Īx.scatter(x_2, y_2, label='Second data set')Īx.scatter(x_3, y_3, label='Third data set')Īx.scatter(x_4, y_4, label='Fourth data set') This is created via ax.legend() after having specified the label keyword argument in the ax.scatter() calls: x_1 = When doing this, it’s usually best to include a legend. To plot multiple data series on the same axes, simply use the ax.scatter() function multiple times. To make this easier, it can be helpful to get the current limits of the axes using ax.get_ylim() and ax.get_xlim():Īx.set_title("Anscombe's First Data Set")Īx.set_facecolor((232 / 255, 232 / 255, 232 / 256)) Alter the aspect ratio of the plotting area using ax.set_aspect().Add text labels with ax.text(), specifying the x- and y-coordinates of the label along with the string that will appear there (read the full documentation for text labels here).This can be done in a for loop and is demonstrated in the example below. set_color() method of the spines (Matplotlib’s word for the axis lines). To colour the axis lines (eg if you want them to match your gridlines) you will need to use the.Use ax.set_axisbelow(True) after adding gridlines to move them behind your data points.If you want minor gridlines and axis ticks you will also need to use plt.minorticks_on().Gridlines: use the plt.grid() function in which you can set which gridlines to mark (major, minor or both) and the axis to apply the lines to (x, y or both), along with other keyword arguments related to line plots.Colour of the graph area: use the ax.set_facecolor((R, G, B)) function where R, G and B are the red, green and blue colour proportions on a scale of 0 to 1. Transparency of the markers: use the alpha keyword argument within plt.scatter().Some more options that can be tinkered with:
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