plot( 'x', 'y3', data=df, marker='', color='olive', linewidth=2, linestyle='dashed', label="toto") plt.legend() (9)强调感兴趣线条 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import matplotlib.pyplot as plt import numpy as np import pandas as pd # Make a data frame df=...
There are two different ways to remove a legend from seaborn’s line plot. Method 1: Using the legend parameter: The lineplot() comes with a legend parameter that is set to True. We can use the False keyword as value to disable the legend in a plot. Here is a code snippet showing h...
axhline非常适合这种情况: importmatplotlib.pyplotaspltimportnumpyasnp np.random.seed(42)data=np.random.randn(100)mean=np.mean(data)fig,ax=plt.subplots()ax.plot(data,label='Data')ax.axhline(y=mean,color='r',linestyle='--',label='Mean')ax.legend()ax.set_title('Data with Mean Line -...
最简单的方法是使用plt.text()函数在垂直线旁边添加文本: importmatplotlib.pyplotaspltimportnumpyasnp x=np.linspace(0,10,100)y=np.sin(x)*np.exp(-0.1*x)plt.figure(figsize=(10,6))plt.plot(x,y,label='Damped sine wave')# 添加带标签的垂直线line=plt.axvline(x=5,color='r',linestyle='-...
import numpy as np import matplotlib.pyplot as plt t = np.arange(0.0, 3.0, 0.01) s = np.sin(2.5 * np.pi * t) plt.plot(t, s) plt.xlabel('time (s)') plt.ylabel('voltage (mV)') plt.title('Sine Wave') plt.grid(True) ...
importseabornassns# 使用 Seaborn 设置调色板sns.set_palette("husl")plt.figure(figsize=(10,6))plt.plot(x,y,label='Sine Wave')plt.plot(x,y2,label='Cosine Wave')# 配置图表plt.title('Sine and Cosine Waves with Seaborn Colors')plt.xlabel('X-axis')plt.ylabel('Y-axis')plt.legend()plt...
import matplotlib.pyplot as plt import numpy as np plt.figure(figsize=(7, 4)) ax = plt.subplot(121) ax.set_aspect(1) plt.plot(np.arange(10)) plt.xlabel('this is a xlabel\n(with newlines!)') plt.ylabel('this is vertical\ntest', multialignment='center') plt.text(2, 7, 'this...
观察:plt.legend &plt.annotate两个函数的用法;持续更新中 matplotlib注释 importnumpyasnpimportmatplotlib.pyplotaspltx=np.arange(-10,11,1)y=x**2plt.plot(x,y)plt.annotate(‘this is the bottom’,xy=(0,1),xytext=(0,20),arrowprops=dict(facecolor=‘ ...
# Data Visualization using Python # Controling line width import numpy as np import matplotlib.pyplot as plt x1 = np.linspace(0.0, 2.0) x2 = np.linspace(0.0, 2.0) y1 = np.sin(2 * np.pi * x1) * np.exp(-x1) y2 = np.cos(2 * np.pi * x2) plt.plot(x1, y1, 'b') plt...
import matplotlib.pyplot as plt import numpy as np import pandas as pd df=pd.DataFrame({'x': range(1,11), 'y': np.random.randn(10) }) plt.plot( 'x', 'y', data=df, color='skyblue') plt.show() (5)设置线条透明度 plt.plot( 'x', 'y', data=df, color='skyblue', alpha...