seaborn.scatterplot

The relationship between 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.

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

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 , 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

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 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.

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

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.

返回值：ax：matplotlib Axes

Returns the Axes object with the plot drawn onto it.

See also

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

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)

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

>>> ax = sns.scatterplot(x="total_bill", y="tip", hue="time",
...                      data=tips)

Show the grouping variable by varying both color and marker:

>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      hue="time", style="time", data=tips)

Vary colors and markers to show two different grouping variables:

>>> ax = sns.scatterplot(x="total_bill", y="tip",

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

Also show the quantitative variable by also using continuous colors:

>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      hue="size", size="size",
...                      data=tips)

Use a different continuous color map:

>>> cmap = sns.cubehelix_palette(dark=.3, light=.8, as_cmap=True)
...                      hue="size", size="size",
...                      palette=cmap,
...                      data=tips)

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)

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)

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

Use a specific set of markers:

>>> markers = {"Lunch": "s", "Dinner": "X"}
...                      markers=markers,
...                      data=tips)

Control plot attributes using matplotlib parameters:

>>> ax = sns.scatterplot(x="total_bill", y="tip",
...                      s=100, color=".2", marker="+",
...                      data=tips)

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)

>>> 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)