添加覆盖整个绘图窗口的垂直线而无需指定其实际高度的标准方法是plt.axvline

import matplotlib.pyplot as plt

plt.axvline(x=0.22058956)
plt.axvline(x=0.33088437)
plt.axvline(x=2.20589566)

或

xcoords = [0.22058956, 0.33088437, 2.20589566]
for xc in xcoords:
    plt.axvline(x=xc)

您可以使用许多可用于其他绘图命令的关键字（例如color、linestyle、linewidth ...）。如果您喜欢坐标轴，可以传入关键字参数ymin和ymax（例如ymin=0.25，ymax=0.75将覆盖图的中半部分）。水平线（axhline）和矩形（axvspan）有相应的函数。

matplotlib.pyplot.vlines对比matplotlib.pyplot.axvline

• 这些方法适用于使用seaborn和pandas.DataFrame.plot生成的图，这两个图都使用matplotlib。
• 不同之处在于vlines接受x的一个或多个位置，而axvline允许一个位置。
• 单一位置：x=37。
• 多个地点：x=[37, 38, 39]。
• vlines将ymin和ymax作为y轴上的位置，而axvline将ymin和ymax作为y轴范围的百分比。
• 向vlines传递多行时，将list传递给ymin和ymax。
• 还有面向对象API的matplotlib.axes.Axes.vlines和matplotlib.axes.Axes.axvline。
• 如果你要用类似fig, ax = plt.subplots()的东西绘制一个图形，那么分别用ax.vlines或ax.axvline替换plt.vlines或plt.axvline。
• 请参阅此answer，了解带有.hlines的水平线。

import numpy as np
import matplotlib.pyplot as plt

xs = np.linspace(1, 21, 200)

plt.figure(figsize=(10, 7))

# only one line may be specified; full height
plt.axvline(x=36, color='b', label='axvline - full height')

# only one line may be specified; ymin & ymax specified as a percentage of y-range
plt.axvline(x=36.25, ymin=0.05, ymax=0.95, color='b', label='axvline - % of full height')

# multiple lines all full height
plt.vlines(x=[37, 37.25, 37.5], ymin=0, ymax=len(xs), colors='purple', ls='--', lw=2, label='vline_multiple - full height')

# multiple lines with varying ymin and ymax
plt.vlines(x=[38, 38.25, 38.5], ymin=[0, 25, 75], ymax=[200, 175, 150], colors='teal', ls='--', lw=2, label='vline_multiple - partial height')

# single vline with full ymin and ymax
plt.vlines(x=39, ymin=0, ymax=len(xs), colors='green', ls=':', lw=2, label='vline_single - full height')

# single vline with specific ymin and ymax
plt.vlines(x=39.25, ymin=25, ymax=150, colors='green', ls=':', lw=2, label='vline_single - partial height')

# place the legend outside
plt.legend(bbox_to_anchor=(1.0, 1), loc='upper left')

plt.show()

海运轴级图

import seaborn as sns

# sample data
fmri = sns.load_dataset("fmri")

# x index for max y values for stim and cue
c_max, s_max = fmri.pivot_table(index='timepoint', columns='event', values='signal', aggfunc='mean').idxmax()

# plot
g = sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event")

# y min and max
ymin, ymax = g.get_ylim()

# vertical lines
g.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2)

Seaborn图形级图

• 每个轴都必须迭代通过。

import seaborn as sns

# sample data
fmri = sns.load_dataset("fmri")

# used to get the index values (x) for max y for each event in each region
fpt = fmri.pivot_table(index=['region', 'timepoint'], columns='event', values='signal', aggfunc='mean')

# plot
g = sns.relplot(data=fmri, x="timepoint", y="signal", col="region", hue="event", kind="line")

# iterate through the axes
for ax in g.axes.flat:
    # get y min and max
    ymin, ymax = ax.get_ylim()
    # extract the region from the title for use in selecting the index of fpt
    region = ax.get_title().split(' = ')[1]
    # get x values for max event
    c_max, s_max = fpt.loc[region].idxmax()
    # add vertical lines
    ax.vlines(x=[c_max, s_max], ymin=ymin, ymax=ymax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2, alpha=0.5)

• 对于'region = frontal'，两个事件的最大值都发生在5处。

条形图

• 条形图具有分类独立轴，因此刻度位置具有从零开始的索引，而不管轴刻度标签如何。
• 根据条形图索引而不是刻度标签选择x。ax.get_xticklabels()将显示位置和标签。

import pandas as pd
import seaborn as sns

# load data
tips = sns.load_dataset('tips')

# histogram
ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
_ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')

# barplot
ax = tips.loc[5:25, ['total_bill', 'tip']].plot(kind='bar', figsize=(15, 4), title='Barplot with Vertical Lines', rot=0)
_ = ax.vlines(x=[0, 17], ymin=0, ymax=45, colors='r')

直方图

• 直方图具有连续的独立轴。

import pandas as pd
import seaborn as sns

# load data
tips = sns.load_dataset('tips')

# histogram from pandas, pyplot, or seaborn 
ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Vertical Line')
_ = ax.vlines(x=16.5, ymin=0, ymax=30, colors='r')

时序轴

• 数据框中作为x轴的日期必须是datetime dtype。如果列或索引的类型不正确，则必须转换为pd.to_datetime。
• 如果使用的是日期数组或列表，请分别参考Converting numpy array of strings to datetime或Convert datetime list into date python。
• x将接受类似'2020-09-24'或datetime(2020, 9, 2)的日期。

import pandas_datareader as web  # conda or pip install this; not part of pandas
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

# get test data; this data is downloaded with the Date column in the index as a datetime dtype
df = web.DataReader('^gspc', data_source='yahoo', start='2020-09-01', end='2020-09-28').iloc[:, :2]

# display(df.head(2))
                   High          Low
Date                                
2020-09-01  3528.030029  3494.600098
2020-09-02  3588.110107  3535.229980

# plot dataframe; the index is a datetime index
ax = df.plot(figsize=(9, 6), title='S&P 500', ylabel='Price')

# add vertical lines
ax.vlines(x=[datetime(2020, 9, 2), '2020-09-24'], ymin=3200, ymax=3600, color='r', label='test lines')

ax.legend(bbox_to_anchor=(1, 1), loc='upper left')
plt.show()

对于多行

xposition = [0.3, 0.4, 0.45]
for xc in xposition:
    plt.axvline(x=xc, color='k', linestyle='--')

要将legend和/或colors添加到某些垂直线，请使用以下命令：

import matplotlib.pyplot as plt

# x coordinates for the lines
xcoords = [0.1, 0.3, 0.5]
# colors for the lines
colors = ['r','k','b']

for xc,c in zip(xcoords,colors):
    plt.axvline(x=xc, label='line at x = {}'.format(xc), c=c)

plt.legend()
plt.show()

结果

像其他人建议的那样，在循环中调用axvline是可行的，但这可能不方便，因为
1.每一行都是一个单独的情节对象，这会导致当你有很多行的时候速度非常慢。
1.创建图例时，每行都有一个新条目，这可能不是您想要的。
相反，您可以使用以下便利函数将所有线创建为单个绘图对象：

import matplotlib.pyplot as plt
import numpy as np

def axhlines(ys, ax=None, lims=None, **plot_kwargs):
    """
    Draw horizontal lines across plot
    :param ys: A scalar, list, or 1D array of vertical offsets
    :param ax: The axis (or none to use gca)
    :param lims: Optionally the (xmin, xmax) of the lines
    :param plot_kwargs: Keyword arguments to be passed to plot
    :return: The plot object corresponding to the lines.
    """
    if ax is None:
        ax = plt.gca()
    ys = np.array((ys, ) if np.isscalar(ys) else ys, copy=False)
    if lims is None:
        lims = ax.get_xlim()
    y_points = np.repeat(ys[:, None], repeats=3, axis=1).flatten()
    x_points = np.repeat(np.array(lims + (np.nan, ))[None, :], repeats=len(ys), axis=0).flatten()
    plot = ax.plot(x_points, y_points, scalex = False, **plot_kwargs)
    return plot

def axvlines(xs, ax=None, lims=None, **plot_kwargs):
    """
    Draw vertical lines on plot
    :param xs: A scalar, list, or 1D array of horizontal offsets
    :param ax: The axis (or none to use gca)
    :param lims: Optionally the (ymin, ymax) of the lines
    :param plot_kwargs: Keyword arguments to be passed to plot
    :return: The plot object corresponding to the lines.
    """
    if ax is None:
        ax = plt.gca()
    xs = np.array((xs, ) if np.isscalar(xs) else xs, copy=False)
    if lims is None:
        lims = ax.get_ylim()
    x_points = np.repeat(xs[:, None], repeats=3, axis=1).flatten()
    y_points = np.repeat(np.array(lims + (np.nan, ))[None, :], repeats=len(xs), axis=0).flatten()
    plot = ax.plot(x_points, y_points, scaley = False, **plot_kwargs)
    return plot

除了上面答案中提供的plt.axvline和plt.plot((x1, x2), (y1, y2)) * 或 * plt.plot([x1, x2], [y1, y2])之外，还可以使用

plt.vlines(x_pos, ymin=y1, ymax=y2)

在x_pos处绘制一条从y1到y2的垂直线，其中值y1和y2是绝对数据坐标。