Method 2: Using the dashes parameter: The Seaborn lineplot() has a dashes parameter that also helps set custom lines for the line plot. Here is a code snippet showing how to use it. import seaborn as sns import numpy as np import matplotlib.pyplot as plt import pandas as pd s = 90 g...
importmatplotlib.pyplotaspltimportnumpyasnp# 创建一些示例数据x=np.linspace(0,10,100)y=np.sin(x)# 创建图表plt.figure(figsize=(10,6))plt.plot(x,y,label='sin(x)')# 添加垂直线plt.axvline(x=5,color='r',linestyle='--')plt.title('How to use axvline in Matplotlib - how2matplotlib....
以下是一个使用axvline标注股票价格数据的示例: importmatplotlib.pyplotaspltimportpandasaspdimportnumpyasnp# 创建模拟的股票价格数据dates=pd.date_range(start='2023-01-01',end='2023-12-31',freq='D')prices=100+np.cumsum(np.random.randn(len(dates))*0.5)plt.figure(figsize=(12,6))plt.plot(dates...
import matplotlib.pyplot as plt import numpy as np # 先获取一个图表 fig = plt.figure() # 抛物线 X1 = np.linspace(-5, 5, 50) # -5 ~ 5 之间生成50个点 Y1 = X1 ** 2 + 5 plt.plot(X1, Y1, 'r') # plt.plot(X1, Y1, color='red', label='parabola') # 三角函数 X2 = np...
下面我们上一个完整的代码：import matplotlib.pyplot as pltimport numpy as npx = np.linspace(0.05,20,2000)y = np.sin(x)plt.plot(x,y,ls='--',c='k',lw=2,label='plot figure')plt.legend()plt.axhline(0.5,ls='-.',c='r',lw=1)plt.axvline(7.5,ls='-.',c='r',lw=1)...
(10, 5)) plt.plot(df['date'], df['value'], marker='o') # 添加垂直线 for date in vline_dates: plt.axvline(x=pd.to_datetime(date), color='r', linestyle='--', label=date if date == vline_dates[0] else "") plt.xlabel('Date') plt.ylabel('Value') plt.title('T...
这是因为指定了ymax=1;kdeplot的y-axis的最大值为3e-06。密度曲线在那里,因为vlines的比例,它就是看不到。.vlines要求ymin和ymax是轴上的值。 获取y-ticks的最大值,并将其用作ymax Tested inpython 3.8.11,matplotlib 3.4.3,seaborn 0.11.2
import matplotlib.pyplot as plt import numpy as np # 创建数据 x = np.linspace(0, 10, 100) y = np.sin(x) # 创建图形和轴 fig, ax = plt.subplots() # 绘制正弦曲线 ax.plot(x, y) # 添加两条水平线 ax.axhline(y=0.5, color='r', linestyle='--') ax.axhline(y=-0.5, color=...
# Implementation of matplotlib.pyplot.annotate()# functionimportnumpyasnpimportmatplotlib.pyplotasplt x = np.linspace(0,13,100) plt.rcParams['lines.linewidth'] =2plt.figure() plt.plot(x, np.sin(x), label ='Line1', color ='green', linestyle ="--") ...
importmatplotlib.pyplotaspltimportnumpyasnp np.random.seed(42)data=np.cumsum(np.random.randn(100))threshold=5fig,ax=plt.subplots()ax.plot(data,label='Cumulative Sum')ax.axhline(y=threshold,color='r',linestyle='-.',label='Threshold')ax.axhline(y=-threshold,color='r',linestyle='-.')...