如果要在轴上绘制水平线，也可以尝试方法。您需要指定 y 位置和 xmin 和 xmax 在数据坐标中（即，您在x轴中的实际数据范围）。示例代码段为：
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()
上面的摘要将在轴上绘制 y = 0.2 的水平线。水平线从 x = 4 开始，以 x = 20 结束。生成的图像为：
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:
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)
” 。
每个轴都必须通过
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)
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()
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.
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)
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.
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')
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()
希望解决问题！
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()
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()
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()
发布评论
评论（7）
使用
axhlineaxhline 线）。例如，这绘制了y = 0.5的水平线：Use
axhline(a horizontal axis line). For example, this plots a horizontal line aty = 0.5:如果要在轴上绘制水平线，也可以尝试 方法。您需要指定
y位置和xmin和xmax在数据坐标中（即，您在x轴中的实际数据范围）。示例代码段为：上面的摘要将在轴上绘制
y = 0.2的水平线。水平线从x = 4开始，以x = 20结束。生成的图像为：If you want to draw a horizontal line in the axes, you might also try
ax.hlines()method. You need to specifyyposition andxminandxmaxin the data coordinate (i.e, your actual data range in the x-axis). A sample code snippet is:The snippet above will plot a horizontal line in the axes at
y=0.2. The horizontal line starts atx=4and ends atx=20. The generated image is:使用
matplotlib.pyplot.pyplot.hlines：seaborn和pandas.dataframe.plot生成的图，两者都使用matplotlib。列表传递到y参数来绘制多个水平线。y可以作为单个位置传递：y = 40y可以作为多个位置传递：y = [39，40 ，41]matplotlib.axes。 axes.hlines对于面向对象的API。Fig> ax = plt.subplots（）之类的图形，则替换plt.hlines或plt.axhline带有ax.hlines或ax.axhline。matplotlib.pyplotlib.pyplot.axhline ;matplotlib.axes.axes.axes.axes.axes.axhline.axhliney = 40），“代码> plt.plot
“在此处输入映像
>
” 。
时间序列轴
xmin和xmax将接受像'2020-09-10'这样的日期代码>或DateTime（2020，9，10）来自DateTime Import DateTimexmin = dateTime（2020，9，10），xmax = dateTime（2020，9，10） + TIMEDELTA（天= 3）date = df.index [9]，xmin = date，xmax = date + pd.timedelta（days = 3），其中索引为a <代码> dateTimeIndex 。dateTime dtype。 If using pandas, then usepd.to_datetime< /a>。有关数组或列表，请参阅将Numpy的字符串转换为dateTime 或 或分别转换为日期python 。web.datareader不起作用。BARPLOT和直方图
Xmin和xmax基于条索引而不是tick标签。ax.get_xticklabels（）将显示位置和标签。Use
matplotlib.pyplot.hlines:seabornandpandas.DataFrame.plot, which both usematplotlib.listto theyparameter.ycan be passed as a single location:y=40ycan be passed as multiple locations:y=[39, 40, 41]matplotlib.axes.Axes.hlinesfor the object oriented api.fig, ax = plt.subplots(), then replaceplt.hlinesorplt.axhlinewithax.hlinesorax.axhline, respectively.matplotlib.pyplot.axhline&matplotlib.axes.Axes.axhlinecan only plot a single location (e.g.y=40).vlinesplt.plotax.plotSeaborn axis-level plot
Seaborn figure-level plot
Time Series Axis
xminandxmaxwill accept a date like'2020-09-10'ordatetime(2020, 9, 10)from datetime import datetimexmin=datetime(2020, 9, 10), xmax=datetime(2020, 9, 10) + timedelta(days=3)date = df.index[9],xmin=date, xmax=date + pd.Timedelta(days=3), where the index is aDatetimeIndex.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.web.DataReaderdoesn't work.Barplot and Histograms
xminandxmaxbased on the bar index, not the tick label.ax.get_xticklabels()will show the locations and labels.除了这里最受欢迎的答案外，在
plotpandas> pandas 'sdataframedataframe 上调用 plot 之后，还可以链条axhline>。In addition to the most upvoted answer here, one can also chain
axhlineafter callingploton apandas'sDataFrame.您是正确的，我认为
[0，Len（XS）]正在抛弃您。您将需要重复使用原始的X轴变量XS，并使用具有带有您的变量的另一个长度的Numpy数组来绘制它。希望解决问题！
You are correct, I think the
[0,len(xs)]is throwing you off. You'll want to reuse the original x-axis variablexsand plot that with another numpy array of the same length that has your variable in it.Hopefully that fixes the problem!
对于那些总是忘记命令
axhline的人来说，这是一种很好而简单的方法，是您的情况下的以下方式
xs = x和y = 40。如果Len（x）很大，那么这将变得降低，您应该真正使用
axhline。A nice and easy way for those people who always forget the command
axhlineis the followingIn your case
xs = xandy = 40.If len(x) is large, then this becomes inefficient and you should really use
axhline.您可以使用
plt.grid绘制水平线。You can use
plt.gridto draw a horizontal line.