python - Plot a horizontal line using matplotlib

Question

I have used spline interpolation to smooth a time series and would also like to add a horizontal line to the plot. But there seems to be an issue that is out of my grips. Any assistance would be really helpful. Here is what I have:

annual = np.arange(1,21,1)
l = np.array(value_list) # a list with 20 values
spl = UnivariateSpline(annual,l)
xs = np.linspace(1,21,200)
plt.plot(xs,spl(xs),'b')

plt.plot([0,len(xs)],[40,40],'r--',lw=2)
pylab.ylim([0,200])
plt.show()

problem seems to be with my use of [0,len(xs)] for horizontal line plotting.

Answer 1

You are correct, I think the [0,len(xs)] is throwing you off. You'll want to reuse the original x-axis variable xs and plot that with another numpy array of the same length that has your variable in it.

annual = np.arange(1,21,1)
l = np.array(value_list) # a list with 20 values
spl = UnivariateSpline(annual,l)
xs = np.linspace(1,21,200)
plt.plot(xs,spl(xs),'b')

#####horizontal line
horiz_line_data = np.array([40 for i in xrange(len(xs))])
plt.plot(xs, horiz_line_data, 'r--') 
###########plt.plot([0,len(xs)],[40,40],'r--',lw=2)
pylab.ylim([0,200])
plt.show()

Hopefully that fixes the problem!

Answer 2

You're looking for axhline (a horizontal axis line). For example, the following will give you a horizontal line at y = 0.5:

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

Answer 3

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:

Answer 4

Use matplotlib.pyplot.hlines:

• 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]
• 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 can only plot a single location (e.g. y=40)
• For vertical lines, see SO: How to draw vertical lines on a given plot in matplotlib

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()

Time Series Axis

• xmin and xmax will accept a date like '2020-09-10' or datetime(2020, 9, 10)
  • 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.

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
df = web.DataReader('^gspc', data_source='yahoo', start='2020-09-01', end='2020-09-28').iloc[:, :2]

# 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')

Answer 5

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));

Answer 6

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.

Answer 7

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()

Answer 8

pylab.plot(...) can overlay a horizontal or vertical line given coordinates

import pylab as pl  
import numpy as np

observations = [0.797, 1.116, 1.071, 0.998, -0.333, 1.129, 0.381, 0.815, 1.28715,
    0.727, 1.309147, 2.492, 0.946, 0.486536, 0.382539, -0.482, -0.208923,
    0.981166, 0.499, 0.022, 0.747333, -0.045, 0.27304, -1.386, 0.654258, 
    -0.43931, -2.012764, -0.387, -0.730, 0.812032, -0.229, -0.286, -0.293,
    -0.483649, 0.232185, -0.027, 0.142, 0.173, -0.618, 0.393, 0.534, 0.804,
    -0.867, 0.776, 0.342, 0.797, 0.550, -0.215, 0.706, -0.973] 

targets = [-0.007, -0.029, -0.025, -0.0119, -0.0719, -0.1283, -0.1077, -0.0844, 
    -0.0474, -0.0419, -0.016, 0.0613, 0.0949, 0.0553, 0.0353, 0.0173, 0.0467,
    0.0562, 0.0523, -0.0032, 0.0548, 0.0245, 0.0372, 0.0404, 0.0388, 0.0703,
    0.0203, -0.0078, -0.0102, 0.0151, -0.0048, -0.0027, 0.0215, -0.0063, -0.0216,
    -0.0618, -0.0172, 0.0212, -0.0203, -0.006, 0.0438, 0.0642, 0.0365, 0.0124,
    -0.0332, -0.064, 0.0061, -0.0007, -0.0242, -0.036] 
 
#scatter plot using x and y points.  c stands for color.  s stands for size 
pl.scatter(observations, targets, c='red', s=5.5) 
 
max_feature_float = max(observations) 
horizontal_line_start_position = 0 
num_dots_on_horizontal_line = 20  
xs = np.linspace(horizontal_line_start_position,max_feature_float, 
    num_dots_on_horizontal_line) 
horiz_line_data = np.array([0 for i in range(len(xs))]) 
pl.plot(xs, horiz_line_data, 'b--') 
 
max_tars_float = max(targets) 
#make a vertical green line starting at (x=0,y=0) and going to (x=0, y=2) ) 
pl.plot((0, 0), (0, max_tars_float), 'g-') 
 
#define the feature and targets axis legend names and title. 
pl.xlabel("observation") 
pl.ylabel("target") 
pl.title('Scatterplot of target against observation.') 
pl.show()