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.
Answers:
Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.
Method 1
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()
Method 2
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:
Method 3
Use matplotlib.pyplot.hlines:
- Plot multiple horizontal lines by passing a
listto theyparameter. ycan be passed as a single location:y=40ycan be passed as multiple locations:y=[39, 40, 41]- Also
matplotlib.axes.Axes.hlinesfor the object oriented api.- If you’re a plotting a figure with something like
fig, ax = plt.subplots(), then replaceplt.hlinesorplt.axhlinewithax.hlinesorax.axhline, respectively.
- If you’re a plotting a figure with something like
matplotlib.pyplot.axhline&matplotlib.axes.Axes.axhlinecan 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()
Time Series Axis
xminandxmaxwill accept a date like'2020-09-10'ordatetime(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 aDatetimeIndex.
- Using
- The date column on the axis must be a
datetime dtype. If using pandas, then usepd.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.DataReaderdoesn’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 barplots are usually 0 indexed, regardless of the axis labels, so select
xminandxmaxbased 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')
Method 4
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));
Method 5
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!
Method 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.
Method 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()
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