在给定的图上画一条水平线

小开

最佳答案

你是对的，我认为[0,len(xs)]把你弄糊涂了。你会想要重用原始的x轴变量xs，并将其与另一个具有相同长度的numpy数组绘制在一起。

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()

希望这能解决问题!

小开

使用axhline(一条水平轴线)。例如，在y = 0.5处绘制一条水平线:

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

小开

对于那些总是忘记axhline命令的人来说，下面是一个很好的和简单的方法

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

在你的例子中xs = x和y = 40。如果len(x)很大，那么这将变得低效，您应该真正使用axhline

小开

如果你想在坐标轴上画一条水平线，你也可以尝试ax.hlines()方法。你需要在数据坐标中指定y位置和xmin和xmax(即你在x轴上的实际数据范围)。示例代码片段如下:

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()

上面的代码片段将在坐标轴y=0.2处绘制一条水平线。水平线开始于x=4，结束于x=20。生成的图像为:

小开

除了这里投票最多的答案之外，还可以在pandas的DataFrame上调用plot后链接axhline。

import pandas as pd


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

小开

你可以使用plt.grid来画一条水平线。

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()

小开

使用`matplotlib.pyplot.hlines`:

这些方法适用于用seaborn和pandas.DataFrame.plot生成的图，它们都使用matplotlib。
通过将list传递给y参数来绘制多条水平线。
y可以作为单个位置传递:y=40
y可以作为多个位置传递
还有matplotlib.axes.Axes.hlines用于面向对象的api。
- 如果你正在用类似fig, ax = plt.subplots()的东西绘制图形，那么分别用ax.hlines或ax.axhline替换plt.hlines或plt.axhline。
matplotlib.pyplot.axhline,matplotlib.axes.Axes.axhline只能绘制单个位置(例如y=40)
请参阅回答中包含.vlines的垂直线

`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()

海波恩轴水平图

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数字级图

必须遍历每个轴

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)

时间序列轴

xmin和xmax将接受类似'2020-09-10'或datetime(2020, 9, 10)的日期
- 使用from datetime import datetime
- xmin=datetime(2020, 9, 10), xmax=datetime(2020, 9, 10) + timedelta(days=3)
- 给定date = df.index[9]， xmin=date, xmax=date + pd.Timedelta(days=3)，其中索引为DatetimeIndex。
轴上的日期列必须是datetime dtype。如果使用熊猫，则使用pd.to_datetime。对于数组或列表，分别参考将numpy字符串数组转换为datetime或转换日期时间列表为日期python。

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()

如果web.DataReader不起作用，则采样时间序列数据。

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和histogram

从零开始的

xmin

xmax

ax.get_xticklabels()将显示位置和标签。

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')