How do you plot a vertical line on a time series plot in Pandas?

Question

• How do you plot a vertical line ( vlines ) in a Pandas series plot?
• I am using Pandas to plot rolling means, etc., and would like to mark important positions with a vertical line.
• Is it possible to use vlines , or something similar, to accomplish this?
• In this case, the x axis is datetime .

Answer 1

plt.axvline(x_position)

It takes the standard plot formatting options ( linestlye , color , ect)

(doc)

If you have a reference to your axes object:

ax.axvline(x, color='k', linestyle='--')

Answer 2

If you have a time-axis, and you have Pandas imported as pd, you can use:

ax.axvline(pd.to_datetime('2015-11-01'), color='r', linestyle='--', lw=2)

For multiple lines:

xposition = [pd.to_datetime('2010-01-01'), pd.to_datetime('2015-12-31')]
for xc in xposition:
    ax.axvline(x=xc, color='k', linestyle='-')

Answer 3

DataFrame plot function returns AxesSubplot object and on it, you can add as many lines as you want. Take a look at the code sample below:

%matplotlib inline

import pandas as pd
import numpy as np

df = pd.DataFrame(index=pd.date_range("2019-07-01", "2019-07-31"))  # for sample data only
df["y"] = np.logspace(0, 1, num=len(df))  # for sample data only

ax = df.plot()
# you can add here as many lines as you want
ax.axhline(6, color="red", linestyle="--")
ax.axvline("2019-07-24", color="red", linestyle="--")

Answer 4

matplotlib.pyplot.vlines

• For a time series, the dates for the axis must be proper datetime objects , not strings.
  • Use pandas.to_datetime to convert columns to datetime dtype.
• Allows for single or multiple locations
• ymin & ymax are specified as a specific y-value, not as a percent of ylim
• If referencing axes with something like fig, axes = plt.subplots() , then change plt.xlines to axes.xlines
• Also see How to draw vertical lines on a given plot
• Tested in python 3.10 , pandas 1.4.2 , matplotlib 3.5.1 , seaborn 0.11.2

Imports and Sample Data

from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns  # if using seaborn

# configure synthetic dataframe
df = pd.DataFrame(index=pd.bdate_range(datetime(2020, 6, 8), freq='1d', periods=500).tolist())
df['v'] = np.logspace(0, 1, num=len(df))

# display(df.head())
                   v
2020-06-08  1.000000
2020-06-09  1.004625
2020-06-10  1.009272
2020-06-11  1.013939
2020-06-12  1.018629

Make the initial plot

Using matplotlib.pyplot.plot or matplotlib.axes.Axes.plot

fig, ax = plt.subplots(figsize=(9, 6))
ax.plot('v', data=df, label='v')
ax.set(xlabel='date', ylabel='v')

Using pandas.DataFrame.plot

ax = df.plot(ylabel='v', figsize=(9, 6))

Using seaborn.lineplot

fig, ax = plt.subplots(figsize=(9, 6))
sns.lineplot(data=df, ax=ax)
ax.set(ylabel='v')

Add the vertical lines

• This should following any of the 3 methods used to make the plot

y_min = df.v.min()
y_max = df.v.max()

# add x-positions as a list of date strings
ax.vlines(x=['2020-07-14', '2021-07-14'], ymin=y_min, ymax=y_max, colors='purple', ls='--', lw=2, label='vline_multiple')

# add x-positions as a datetime
ax.vlines(x=datetime(2020, 12, 25), ymin=4, ymax=9, colors='green', ls=':', lw=2, label='vline_single')

ax.legend(bbox_to_anchor=(1.04, 0.5), loc="center left")
plt.show()