seaborn.lineplot

seaborn.lineplot(x=None, y=None, hue=None, size=None, style=None, data=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, dashes=True, markers=None, style_order=None, units=None, estimator='mean', ci=95, n_boot=1000, sort=True, err_style='band', err_kws=None, legend='brief', ax=None, **kwargs)

Draw a line 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.

By default, the plot aggregates over multiple y values at each value of x and shows an estimate of the central tendency and a confidence interval for that estimate.

参数：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 lines 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 lines with different widths. 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 lines with different dashes and/or 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.

dashes：boolean, list, or dictionary, optional

Object determining how to draw the lines for different levels of the style variable. Setting to True will use default dash codes, or you can pass a list of dash codes or a dictionary mapping levels of the style variable to dash codes. Setting to False will use solid lines for all subsets. Dashes are specified as in matplotlib: a tuple of (segment, gap) lengths, or an empty string to draw a solid line.

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.

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.

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.

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.

n_boot：int, optional

Number of bootstraps to use for computing the confidence interval.

sort：boolean, optional

If True, the data will be sorted by the x and y variables, otherwise lines will connect points in the order they appear in the dataset.

err_style：“band” or “bars”, optional

Whether to draw the confidence intervals with translucent error bands or discrete error bars.

err_band：dict of keyword arguments

Additional paramters to control the aesthetics of the error bars. The kwargs are passed either to ax.fill_between or ax.errorbar, depending on the err_style.

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.plot 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 without emphasizing continuity of the x variable.Show the relationship between two variables when one is categorical.

Examples

Draw a single line plot with error bands showing a confidence interval:

>>> import seaborn as sns; sns.set()
>>> import matplotlib.pyplot as plt
>>> fmri = sns.load_dataset("fmri")
>>> ax = sns.lineplot(x="timepoint", y="signal", data=fmri)

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

>>> ax = sns.lineplot(x="timepoint", y="signal", hue="event",
...                   data=fmri)

Show the grouping variable with both color and line dashing:

>>> ax = sns.lineplot(x="timepoint", y="signal",
...                   hue="event", style="event", data=fmri)

Use color and line dashing to represent two different grouping variables:

>>> ax = sns.lineplot(x="timepoint", y="signal",
...                   hue="region", style="event", data=fmri)

Use markers instead of the dashes to identify groups:

>>> ax = sns.lineplot(x="timepoint", y="signal",
...                   hue="event", style="event",
...                   markers=True, dashes=False, data=fmri)

Show error bars instead of error bands and plot the standard error:

>>> ax = sns.lineplot(x="timepoint", y="signal", hue="event",
...                   err_style="bars", ci=68, data=fmri)

Show experimental replicates instead of aggregating:

>>> ax = sns.lineplot(x="timepoint", y="signal", hue="event",
...                   units="subject", estimator=None, lw=1,
...                   data=fmri.query("region == 'frontal'"))

Use a quantitative color mapping:

>>> dots = sns.load_dataset("dots").query("align == 'dots'")
>>> ax = sns.lineplot(x="time", y="firing_rate",
...                   hue="coherence", style="choice",
...                   data=dots)

Use a different normalization for the colormap:

>>> from matplotlib.colors import LogNorm
>>> ax = sns.lineplot(x="time", y="firing_rate",
...                   hue="coherence", style="choice",
...                   hue_norm=LogNorm(), data=dots)

Use a different color palette:

>>> ax = sns.lineplot(x="time", y="firing_rate",
...                   hue="coherence", style="choice",
...                   palette="ch:2.5,.25", data=dots)

Use specific color values, treating the hue variable as categorical:

>>> palette = sns.color_palette("mako_r", 6)
>>> ax = sns.lineplot(x="time", y="firing_rate",
...                   hue="coherence", style="choice",
...                   palette=palette, data=dots)

Change the width of the lines with a quantitative variable:

>>> ax = sns.lineplot(x="time", y="firing_rate",
...                   size="coherence", hue="choice",
...                   legend="full", data=dots)

Change the range of line widths used to normalize the size variable:

>>> ax = sns.lineplot(x="time", y="firing_rate",
...                   size="coherence", hue="choice",
...                   sizes=(.25, 2.5), data=dots)

Plot from a wide-form DataFrame:

>>> 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.lineplot(data=wide_df)

Plot from a list of Series:

>>> list_data = [wide_df.loc[:"2005", "a"], wide_df.loc["2003":, "b"]]
>>> ax = sns.lineplot(data=list_data)

Plot a single Series, pass kwargs to plt.plot:

>>> ax = sns.lineplot(data=wide_df["a"], color="coral", label="line")

Draw lines at points as they appear in the dataset:

>>> x, y = np.random.randn(2, 5000).cumsum(axis=1)
>>> ax = sns.lineplot(x=x, y=y, sort=False, lw=1)