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 follow 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()