seaborn.scatterplot

seaborn.scatterplot(x=None, y=None, hue=None, style=None, size=None, data=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, markers=True, style_order=None, x_bins=None, y_bins=None, units=None, estimator=None, ci=95, n_boot=1000, alpha=’auto’, x_jitter=None, y_jitter=None, legend=’brief’, ax=None, **kwargs)

Draw a scatter plot with possibility of several semantic groupings.

The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters. These parameters control what visual semantics are used to identify the different subsets. It is possible to show up to three dimensions independently by using all three semantic types, but this style of plot can be hard to interpret and is often ineffective. Using redundant semantics (i.e. both hue and style for the same variable) can be helpful for making graphics more accessible.

See the tutorial for more information.

Parameters:
x, y : names of variables in data or vector data, optional

Input data variables; must be numeric. Can pass data directly or reference columns in data.

hue : name of variables in data or vector data, optional

Grouping variable that will produce points with different colors. Can be either categorical or numeric, although color mapping will behave differently in latter case.

size : name of variables in data or vector data, optional

Grouping variable that will produce points with different sizes. Can be either categorical or numeric, although size mapping will behave differently in latter case.

style : name of variables in data or vector data, optional

Grouping variable that will produce points with different markers. Can have a numeric dtype but will always be treated as categorical.

data : DataFrame

Tidy (“long-form”) dataframe where each column is a variable and each row is an observation.

palette : palette name, list, or dict, optional

Colors to use for the different levels of the hue variable. Should be something that can be interpreted by color_palette(), or a dictionary mapping hue levels to matplotlib colors.

hue_order : list, optional

Specified order for the appearance of the hue variable levels, otherwise they are determined from the data. Not relevant when the hue variable is numeric.

hue_norm : tuple or Normalize object, optional

Normalization in data units for colormap applied to the hue variable when it is numeric. Not relevant if it is categorical.

sizes : list, dict, or tuple, optional

An object that determines how sizes are chosen when size is used. It can always be a list of size values or a dict mapping levels of the size variable to sizes. When size is numeric, it can also be a tuple specifying the minimum and maximum size to use such that other values are normalized within this range.

size_order : list, optional

Specified order for appearance of the size variable levels, otherwise they are determined from the data. Not relevant when the size variable is numeric.

size_norm : tuple or Normalize object, optional

Normalization in data units for scaling plot objects when the size variable is numeric.

markers : boolean, list, or dictionary, optional

Object determining how to draw the markers for different levels of the style variable. Setting to True will use default markers, or you can pass a list of markers or a dictionary mapping levels of the style variable to markers. Setting to False will draw marker-less lines. Markers are specified as in matplotlib.

style_order : list, optional

Specified order for appearance of the style variable levels otherwise they are determined from the data. Not relevant when the style variable is numeric.

{x,y}_bins : lists or arrays or functions

Currently non-functional.

units : {long_form_var}

Grouping variable identifying sampling units. When used, a separate line will be drawn for each unit with appropriate semantics, but no legend entry will be added. Useful for showing distribution of experimental replicates when exact identities are not needed.

Currently non-functional.

estimator : name of pandas method or callable or None, optional

Method for aggregating across multiple observations of the y variable at the same x level. If None, all observations will be drawn. Currently non-functional.

ci : int or “sd” or None, optional

Size of the confidence interval to draw when aggregating with an estimator. “sd” means to draw the standard deviation of the data. Setting to None will skip bootstrapping. Currently non-functional.

n_boot : int, optional

Number of bootstraps to use for computing the confidence interval. Currently non-functional.

alpha : float

Proportional opacity of the points.

{x,y}_jitter : booleans or floats

Currently non-functional.

legend : “brief”, “full”, or False, optional

How to draw the legend. If “brief”, numeric hue and size variables will be represented with a sample of evenly spaced values. If “full”, every group will get an entry in the legend. If False, no legend data is added and no legend is drawn.

ax : matplotlib Axes, optional

Axes object to draw the plot onto, otherwise uses the current Axes.

kwargs : key, value mappings

Other keyword arguments are passed down to plt.scatter at draw time.
Returns:
ax : matplotlib Axes

Returns the Axes object with the plot drawn onto it.

See also

lineplot
Show the relationship between two variables connected with lines to emphasize continuity.
swarmplot
Draw a scatter plot with one categorical variable, arranging the points to show the distribution of values.

Examples

Draw a simple scatter plot between two variables:

>>> import seaborn as sns; sns.set()
>>> import matplotlib.pyplot as plt
>>> tips = sns.load_dataset("tips")
>>> ax = sns.scatterplot(x="total_bill", y="tip", data=tips)
../_images/seaborn-scatterplot-1.png

Group by another variable and show the groups with different colors:

>>> ax = sns.scatterplot(x="total_bill", y="tip", hue="time",
...                      data=tips)
../_images/seaborn-scatterplot-2.png

Show the grouping variable by varying both color and marker:

>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      hue="time", style="time", data=tips)
../_images/seaborn-scatterplot-3.png

Vary colors and markers to show two different grouping variables:

>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      hue="day", style="time", data=tips)
../_images/seaborn-scatterplot-4.png

Show a quantitative variable by varying the size of the points:

>>> ax = sns.scatterplot(x="total_bill", y="tip", size="size",
...                      data=tips)
../_images/seaborn-scatterplot-5.png

Also show the quantitative variable by also using continuous colors:

>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      hue="size", size="size",
...                      data=tips)
../_images/seaborn-scatterplot-6.png

Use a different continuous color map:

>>> cmap = sns.cubehelix_palette(dark=.3, light=.8, as_cmap=True)
>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      hue="size", size="size",
...                      palette=cmap,
...                      data=tips)
../_images/seaborn-scatterplot-7.png

Change the minimum and maximum point size and show all sizes in legend:

>>> cmap = sns.cubehelix_palette(dark=.3, light=.8, as_cmap=True)
>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      hue="size", size="size",
...                      sizes=(20, 200), palette=cmap,
...                      legend="full", data=tips)
../_images/seaborn-scatterplot-8.png

Use a narrower range of color map intensities:

>>> cmap = sns.cubehelix_palette(dark=.3, light=.8, as_cmap=True)
>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      hue="size", size="size",
...                      sizes=(20, 200), hue_norm=(0, 7),
...                      legend="full", data=tips)
../_images/seaborn-scatterplot-9.png

Vary the size with a categorical variable, and use a different palette:

>>> cmap = sns.cubehelix_palette(dark=.3, light=.8, as_cmap=True)
>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      hue="day", size="smoker",
...                      palette="Set2",
...                      data=tips)
../_images/seaborn-scatterplot-10.png

Use a specific set of markers:

>>> markers = {"Lunch": "s", "Dinner": "X"}
>>> ax = sns.scatterplot(x="total_bill", y="tip", style="time",
...                      markers=markers,
...                      data=tips)
../_images/seaborn-scatterplot-11.png

Control plot attributes using matplotlib parameters:

>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      s=100, color=".2", marker="+",
...                      data=tips)
../_images/seaborn-scatterplot-12.png

Pass data vectors instead of names in a data frame:

>>> iris = sns.load_dataset("iris")
>>> ax = sns.scatterplot(x=iris.sepal_length, y=iris.sepal_width,
...                      hue=iris.species, style=iris.species)
../_images/seaborn-scatterplot-13.png

Pass a wide-form dataset and plot against its index:

>>> import numpy as np, pandas as pd; plt.close("all")
>>> index = pd.date_range("1 1 2000", periods=100,
...                       freq="m", name="date")
>>> data = np.random.randn(100, 4).cumsum(axis=0)
>>> wide_df = pd.DataFrame(data, index, ["a", "b", "c", "d"])
>>> ax = sns.scatterplot(data=wide_df)
../_images/seaborn-scatterplot-14.png