Plot a horizontal line on a given plot

Use axhline (a horizontal axis line). For example, this plots a horizontal line at y = 0.5:

import matplotlib.pyplot as plt
plt.axhline(y=0.5, color='r', linestyle='-')
plt.show()

A nice and easy way for those people who always forget the command axhline is the following

plt.plot(x, [y]*len(x))

In your case xs = x and y = 40.
If len(x) is large, then this becomes inefficient and you should really use axhline.

If you want to draw a horizontal line in the axes, you might also try ax.hlines() method. You need to specify y position and xmin and xmax in the data coordinate (i.e, your actual data range in the x-axis). A sample code snippet is:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 21, 200)
y = np.exp(-x)

fig, ax = plt.subplots()
ax.plot(x, y)
ax.hlines(y=0.2, xmin=4, xmax=20, linewidth=2, color='r')

plt.show()

The snippet above will plot a horizontal line in the axes at y=0.2. The horizontal line starts at x=4 and ends at x=20. The generated image is:

In addition to the most upvoted answer here, one can also chain axhline after calling plot on a pandas‘s DataFrame.

import pandas as pd

(pd.DataFrame([1, 2, 3])
   .plot(kind='bar', color='orange')
   .axhline(y=1.5));

You can use plt.grid to draw a horizontal line.

import numpy as np
from matplotlib import pyplot as plt
from scipy.interpolate import UnivariateSpline
from matplotlib.ticker import LinearLocator

# your data here
annual = np.arange(1,21,1)
l = np.random.random(20)
spl = UnivariateSpline(annual,l)
xs = np.linspace(1,21,200)

# plot your data
plt.plot(xs,spl(xs),'b')

# horizental line?
ax = plt.axes()
# three ticks:
ax.yaxis.set_major_locator(LinearLocator(3))
# plot grids only on y axis on major locations
plt.grid(True, which='major', axis='y')

# show
plt.show()

Use matplotlib.pyplot.hlines:

• These methods are applicable to plots generated with seaborn and pandas.DataFrame.plot, which both use matplotlib.
• Plot multiple horizontal lines by passing a list to the y parameter.
• y can be passed as a single location: y=40
• y can be passed as multiple locations: y=[39, 40, 41]
• Also matplotlib.axes.Axes.hlines for the object oriented api.
  • If you’re a plotting a figure with something like fig, ax = plt.subplots(), then replace plt.hlines or plt.axhline with ax.hlines or ax.axhline, respectively.
• matplotlib.pyplot.axhline & matplotlib.axes.Axes.axhline can only plot a single location (e.g. y=40)
• See this answer for vertical lines with .vlines

plt.plot

import numpy as np
import matplotlib.pyplot as plt

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

plt.figure(figsize=(6, 3))
plt.hlines(y=39.5, xmin=100, xmax=175, colors='aqua', linestyles='-', lw=2, label='Single Short Line')
plt.hlines(y=[39, 40, 41], xmin=[0, 25, 50], xmax=[len(xs)], colors='purple', linestyles='--', lw=2, label='Multiple Lines')
plt.legend(bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)

ax.plot

import numpy as np
import matplotlib.pyplot as plt

xs = np.linspace(1, 21, 200)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 6))

ax1.hlines(y=40, xmin=0, xmax=len(xs), colors='r', linestyles='--', lw=2)
ax1.set_title('One Line')

ax2.hlines(y=[39, 40, 41], xmin=0, xmax=len(xs), colors='purple', linestyles='--', lw=2)
ax2.set_title('Multiple Lines')

plt.tight_layout()
plt.show()

Seaborn axis-level plot

import seaborn as sns

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

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

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

# x min and max
xmin, ymax = g.get_xlim()

# vertical lines
g.hlines(y=[c_max, s_max], xmin=xmin, xmax=xmax, 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 max values (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", style="event", kind="line")

# iterate through the axes
for ax in g.axes.flat:
    # get x min and max
    xmin, xmax = ax.get_xlim()  
    # 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].max() 
    # add horizontal lines 
    ax.hlines(y=[c_max, s_max], xmin=xmin, xmax=xmax, colors=['tab:orange', 'tab:blue'], ls='--', lw=2, alpha=0.5)

Time Series Axis

• xmin and xmax will accept a date like '2020-09-10' or datetime(2020, 9, 10)
  • Using from datetime import datetime
  • xmin=datetime(2020, 9, 10), xmax=datetime(2020, 9, 10) + timedelta(days=3)
  • Given date = df.index[9], xmin=date, xmax=date + pd.Timedelta(days=3), where the index is a DatetimeIndex.
• The date column on the axis must be a datetime dtype. If using pandas, then use pd.to_datetime. For an array or list, refer to Converting numpy array of strings to datetime or Convert datetime list into date python, respectively.

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

# get test data; the Date index is already downloaded as 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
ax = df.plot(figsize=(9, 6), title='S&P 500', ylabel='Price')

# add horizontal line
ax.hlines(y=3450, xmin='2020-09-10', xmax='2020-09-17', color='purple', label='test')

ax.legend()
plt.show()

• Sample time series data if web.DataReader doesn’t work.

data = {pd.Timestamp('2020-09-01 00:00:00'): {'High': 3528.03, 'Low': 3494.6}, pd.Timestamp('2020-09-02 00:00:00'): {'High': 3588.11, 'Low': 3535.23}, pd.Timestamp('2020-09-03 00:00:00'): {'High': 3564.85, 'Low': 3427.41}, pd.Timestamp('2020-09-04 00:00:00'): {'High': 3479.15, 'Low': 3349.63}, pd.Timestamp('2020-09-08 00:00:00'): {'High': 3379.97, 'Low': 3329.27}, pd.Timestamp('2020-09-09 00:00:00'): {'High': 3424.77, 'Low': 3366.84}, pd.Timestamp('2020-09-10 00:00:00'): {'High': 3425.55, 'Low': 3329.25}, pd.Timestamp('2020-09-11 00:00:00'): {'High': 3368.95, 'Low': 3310.47}, pd.Timestamp('2020-09-14 00:00:00'): {'High': 3402.93, 'Low': 3363.56}, pd.Timestamp('2020-09-15 00:00:00'): {'High': 3419.48, 'Low': 3389.25}, pd.Timestamp('2020-09-16 00:00:00'): {'High': 3428.92, 'Low': 3384.45}, pd.Timestamp('2020-09-17 00:00:00'): {'High': 3375.17, 'Low': 3328.82}, pd.Timestamp('2020-09-18 00:00:00'): {'High': 3362.27, 'Low': 3292.4}, pd.Timestamp('2020-09-21 00:00:00'): {'High': 3285.57, 'Low': 3229.1}, pd.Timestamp('2020-09-22 00:00:00'): {'High': 3320.31, 'Low': 3270.95}, pd.Timestamp('2020-09-23 00:00:00'): {'High': 3323.35, 'Low': 3232.57}, pd.Timestamp('2020-09-24 00:00:00'): {'High': 3278.7, 'Low': 3209.45}, pd.Timestamp('2020-09-25 00:00:00'): {'High': 3306.88, 'Low': 3228.44}, pd.Timestamp('2020-09-28 00:00:00'): {'High': 3360.74, 'Low': 3332.91}}

df = pd.DataFrame.from_dict(data, 'index')

Barplot and Histograms

• Note that bar plot tick locations have a zero-based index, regardless of the axis tick labels, so select xmin and xmax 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  # for tips data

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

# histogram
ax = tips.plot(kind='hist', y='total_bill', bins=30, ec='k', title='Histogram with Horizontal Line')
_ = ax.hlines(y=6, xmin=0, xmax=55, 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.hlines(y=6, xmin=3, xmax=15, colors='r')