Python matplotlib scatter size

matplotlib.pyplot.scatter#

matplotlib.pyplot. scatter ( x , y , s = None , c = None , marker = None , cmap = None , norm = None , vmin = None , vmax = None , alpha = None , linewidths = None , * , edgecolors = None , plotnonfinite = False , data = None , ** kwargs ) [source] #

A scatter plot of y vs. x with varying marker size and/or color.

Parameters : x, y float or array-like, shape (n, )

s float or array-like, shape (n, ), optional

The marker size in points**2 (typographic points are 1/72 in.). Default is rcParams[‘lines.markersize’] ** 2 .

c array-like or list of colors or color, optional

The marker colors. Possible values:

• A scalar or sequence of n numbers to be mapped to colors using cmap and norm.
• A 2D array in which the rows are RGB or RGBA.
• A sequence of colors of length n.
• A single color format string.

Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. If you want to specify the same RGB or RGBA value for all points, use a 2D array with a single row. Otherwise, value-matching will have precedence in case of a size matching with x and y.

If you wish to specify a single color for all points prefer the color keyword argument.

Defaults to None . In that case the marker color is determined by the value of color, facecolor or facecolors. In case those are not specified or None , the marker color is determined by the next color of the Axes ‘ current «shape and fill» color cycle. This cycle defaults to rcParams[«axes.prop_cycle»] (default: cycler(‘color’, [‘#1f77b4’, ‘#ff7f0e’, ‘#2ca02c’, ‘#d62728’, ‘#9467bd’, ‘#8c564b’, ‘#e377c2’, ‘#7f7f7f’, ‘#bcbd22’, ‘#17becf’]) ).

The marker style. marker can be either an instance of the class or the text shorthand for a particular marker. See matplotlib.markers for more information about marker styles.

cmap str or Colormap , default: rcParams[«image.cmap»] (default: ‘viridis’ )

The Colormap instance or registered colormap name used to map scalar data to colors.

This parameter is ignored if c is RGB(A).

norm str or Normalize , optional

The normalization method used to scale scalar data to the [0, 1] range before mapping to colors using cmap. By default, a linear scaling is used, mapping the lowest value to 0 and the highest to 1.

If given, this can be one of the following:

• An instance of Normalize or one of its subclasses (see Colormap Normalization ).
• A scale name, i.e. one of «linear», «log», «symlog», «logit», etc. For a list of available scales, call matplotlib.scale.get_scale_names() . In that case, a suitable Normalize subclass is dynamically generated and instantiated.

This parameter is ignored if c is RGB(A).

vmin, vmax float, optional

When using scalar data and no explicit norm, vmin and vmax define the data range that the colormap covers. By default, the colormap covers the complete value range of the supplied data. It is an error to use vmin/vmax when a norm instance is given (but using a str norm name together with vmin/vmax is acceptable).

This parameter is ignored if c is RGB(A).

alpha float, default: None

The alpha blending value, between 0 (transparent) and 1 (opaque).

linewidths float or array-like, default: rcParams[«lines.linewidth»] (default: 1.5 )

The linewidth of the marker edges. Note: The default edgecolors is ‘face’. You may want to change this as well.

edgecolors None> or color or sequence of color, default: rcParams[«scatter.edgecolors»] (default: ‘face’ )

The edge color of the marker. Possible values:

• ‘face’: The edge color will always be the same as the face color.
• ‘none’: No patch boundary will be drawn.
• A color or sequence of colors.

For non-filled markers, edgecolors is ignored. Instead, the color is determined like with ‘face’, i.e. from c, colors, or facecolors.

plotnonfinite bool, default: False

Whether to plot points with nonfinite c (i.e. inf , -inf or nan ). If True the points are drawn with the bad colormap color (see Colormap.set_bad ).

Returns : PathCollection Other Parameters : data indexable object, optional

If given, the following parameters also accept a string s , which is interpreted as data[s] (unless this raises an exception):

**kwargs Collection properties

To plot scatter plots when markers are identical in size and color.

• The plot function will be faster for scatterplots where markers don’t vary in size or color.
• Any or all of x, y, s, and c may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted.
• Fundamentally, scatter works with 1D arrays; x, y, s, and c may be input as N-D arrays, but within scatter they will be flattened. The exception is c, which will be flattened only if its size matches the size of x and y.

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Set Marker Size in Matplotlib Scatterplots

In this tutorial, you’ll learn how to define and set the marker size in Matplotlib scatterplots. Being able to control the size of your Matplotlib scatterplots allows you to add extra information to your scatterplots.

This allows you to communicate more details about your data and make it clearer to your visualization’s readers. Matplotlib allows you to customize the size for all plots as well as for individual plots, based on other data criteria.

By the end of this tutorial, you’ll have learned:

• How to set the marker size for all points in a Matplotlib scatterplot
• How to set the marker size for points in a Matplotlib scatterplot based on other data
• Develop a function to define marker size in Matplotlib

Loading a Sample Matplotlib Scatterplot

In order to follow along with this tutorial, feel free to load the plot below. The code loads both x and y coordinates for a scatterplot. Additionally, we set the x and y limits of the plot and give the plot a title to better understand what is going on.

# Loading a sample scatter plot import matplotlib.pyplot as plt plt.style.use('seaborn') x = [1,2,3,4,5,6,7,8,9] y = [2,4,6,7,4,3,2,4,6] plt.scatter(x, y) plt.xlim(0, 10) plt.ylim(0, 10) plt.title('A Sample Scatter Plot - datagy.io') plt.show()

This returns the following image:

In the next section, you’ll learn how to change the marker size for all points in a Matplotlib scatterplot.

Changing the Marker Size for All Points in Matplotlib Scatterplots

Matplotlib makes it simple to change the plot size for all points in a scatter plot. The size of points is based on the s= parameter. The parameter accepts either an integer or a list of values. These options determine what the size of the markers is:

• Passing in a single value changes the size for all markers
• Passing in a list of values changes the size for each marker individually

Let’s see how we can change the size for all markers using the s= parameter:

# Changing the size for all markers in Matplotlib import matplotlib.pyplot as plt plt.style.use('seaborn') x = [1,2,3,4,5,6,7,8,9] y = [2,4,6,7,4,3,2,4,6] # Using s= to change the marker size plt.scatter(x, y, s=100) plt.xlim(0, 10) plt.ylim(0, 10) plt.title('Changing Marker Sizes for All Points - datagy.io') plt.show()

This returns the following image:

To understand what the s= parameter controls, we need to take a look at the documentation. The s parameter is defined as the marker size in points ** 2, meaning that the value passed in is squared. In order to get a marker that is, say, size 10, we need to pass in the square of that.

Changing the Marker Size for Individual Points in Matplotlib Scatterplots Based on Other Data

In this section, we’ll look at using another set of values to set the size of matplotlib scatterplot markers. Let’s say we had another dimension to our data, we can use the values in that dimension to control the size. Because the s= parameter also accepts an array of values, we can simply pass in that array.

Let’s see what this might look like. We’ll add another array of values that will control the size:

# Controlling the size of markers with another variable import matplotlib.pyplot as plt plt.style.use('seaborn') x = [1,2,3,4,5,6,7,8,9] y = [2,4,6,7,4,3,2,4,6] # Adding another variable to control size z = [10,20,10,15,100,25,50,70,80] plt.scatter(x, y, s=z) plt.xlim(0, 10) plt.ylim(0, 10) plt.title('Changing Marker Sizes Based on Another Value - datagy.io') plt.show()

This returns the following image:

This can be a very helpful method when we’re attempting to control the size of a marker based on a DataFrame column.

Define a Function to Define Marker Sizes in Matplotlib

Following the logic of the example above, we can define a function that uses either the x or y parameters to define the size of markers in a Matplotlib scatterplot. The example below is just that – an example. You can define the function however you like.

In the code below, the size of the marker is relative to the value of x , increasing it by a factor of 3:

# Using a function to define marker size import matplotlib.pyplot as plt plt.style.use('seaborn') x = [1,2,3,4,5,6,7,8,9] y = [2,4,6,7,4,3,2,4,6] def set_marker_size(x, factor): return [x_i**factor for x_i in x] plt.scatter(x, y, s=set_marker_size(x,3)) plt.xlim(0, 10) plt.ylim(0, 10) plt.title('Changing Marker Sizes Using a Function - datagy.io') plt.show()

This returns the following image:

What is the Marker Size in Matplotlib?

Understanding what the marker size represents simplifies a lot of the understanding behind it. It may feel like we’ve been setting values arbitrarily. However, as with everything else in Matplotlib there is significant logic behind it.

Each point is actually the pixel size, which varies by the resolution that you set for the figure itself. By default, Matplotlib uses a resulting of 100 DPI (meaning, per inch). If you were to change this, then the relative sizes that you see would change as well.

Conclusion

In this tutorial, you learned how to set the marker size of scatterplot points in Matplotlib. Being able to modify the size of markers allows you to more effectively communicate the intent of your data. You first learned how to change the size of all markers. Then, you learned how to change the size of markers based on another value. This allows you to add another dimension to your data.

Additional Resources

To learn more about related topics, check out the tutorials below:

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