How to draw vertical lines on a given plot

For multiple lines

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

Calling axvline in a loop, as others have suggested, works, but it can be inconvenient because

1. Each line is a separate plot object, which causes things to be very slow when you have many lines.
2. When you create the legend each line has a new entry, which may not be what you want.

Instead, you can use the following convenience functions which create all the lines as a single plot object:

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

To add a legend and/or colors to some vertical lines, then use this:

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()

Results

In addition to the plt.axvline and plt.plot((x1, x2), (y1, y2)) or plt.plot([x1, x2], [y1, y2]) as provided in the answers above, one can also use

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

to plot a vertical line at x_pos spanning from y1 to y2 where the values y1 and y2 are in absolute data coordinates.

matplotlib.pyplot.vlines vs. matplotlib.pyplot.axvline

• These methods are applicable to plots generated with seaborn and pandas.DataFrame.plot, which both use matplotlib.
• The difference is that vlines accepts one or more locations for x, while axvline permits one location.
  • Single location: x=37.
  • Multiple locations: x=[37, 38, 39].
• vlines takes ymin and ymax as a position on the y-axis, while axvline takes ymin and ymax as a percentage of the y-axis range.
  • When passing multiple lines to vlines, pass a list to ymin and ymax.
• Also matplotlib.axes.Axes.vlines and matplotlib.axes.Axes.axvline for the object-oriented API.
  • If you’re plotting a figure with something like fig, ax = plt.subplots(), then replace plt.vlines or plt.axvline with ax.vlines or ax.axvline, respectively.
• See this answer for horizontal lines with .hlines.

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()

Seaborn axes-level plot

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 figure-level plot

• Each axes must be iterated through.

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)

• For 'region = frontal' the maximum value of both events occurs at 5.

Barplot

• Bar plots have a categorical independent axis, so the tick locations have a zero-based index, regardless of the axis tick labels.
  • Select x based on the bar index, not the tick label. ax.get_xticklabels() will show the locations and labels.

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')

Histograms

• Histograms have a continues independent axis.

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')

Time Series Axis

• The dates in the dataframe to be the x-axis must be a datetime dtype. If the column or index is not the correct type, it must be converted with pd.to_datetime.
  • If an array or list of dates is being used, refer to Converting numpy array of strings to datetime or Convert datetime list into date python, respectively.
• x will accept a date like '2020-09-24' or datetime(2020, 9, 2).

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()