'B':np.random.rand(10)})# 直接使用DataFrame绘制图表df.plot(kind='line')plt.title('DataFrame Line Plot')plt.show()在上述示例中，我们使用Pandas的DataFrame创建了两个随机数据列，然后直接调用.plot()方法进行绘图。示例：Series可视化Pandas的Series也可以轻松
Python plot multiple lines from dataframe You can plot multiple lines from the data provided by aDataframein python using matplotlib. You can do it by specifying different columns of the dataframe as the x and y-axis parameters in the matplotlib.pyplot.plot() function. You can select columns ...
importmatplotlib.pyplotaspltimportnumpyasnp# 创建数据x=np.linspace(0,10,100)y1=np.sin(x)y2=np.exp(x)# 创建图表和第一个Y轴fig,ax1=plt.subplots()# 绘制第一条线line1=ax1.plot(x,y1,color='blue',label='Sin(x)')ax1.set_xlabel('X axis - how2matplotlib.com')ax1.set_ylabel('Y1 ...
Plot Multiple Lines Often, you’ll want to compare multiple datasets byplotting several lineson the same graph: import matplotlib.pyplot as plt import numpy as np # Create sample data x = np.arange(0, 10, 0.1) y1 = np.sin(x) y2 = np.cos(x) # Plot multiple lines plt.plot(x, y1...
Matplotlib Python 中设置线条样式可以通过在 Matplotlib plot 中通过设置 matplotlib.pyplot.plot() 方法...
上面的代码段可用于创建折线图。在这里Pandas Dataframe已被用于执行基本数据操作。在读取和处理输入数据集之后,使用plt.plot()绘制x轴上的Year和在y轴上构建的属性数的折线图。 2.Bar Plot 条形图显示具有与其表示的值成比例的矩形高度或长度条的分类数据。
在接下来会给plot()添加两个参数,第一个参数作为折线图的x轴数据,第二个参数作为折线图的y轴参数。 first_twelve = unrate[0:12] #通过切片[0:12]取出DataFrame类型的变量unrate的前12行数据 #fiest_twelve数据是由12行的“DATE”和“VALUE”组成的 ...
>>> plot('xlabel', 'ylabel', data=obj) All indexable objects are supported. This could e.g. be a `dict`, a `pandas.DataFrame` or a structured numpy array. **Plotting multiple sets of data** There are various ways to plot multiple sets of data. ...
//www.delftstack.com/zh/howto/matplotlib/fill-between-multiple-lines-matplotlib/ 评论 In [32]: x=np.arange(0,5,0.02) y1=2-2*x y2=6-x y3=8-4*x y4=np.minimum(y2,y3) plt.plot(x,y1,color="red",label="2-2x") plt.plot(x,y2,color="blue",label="6-x") plt.plot(x,y3...
Source dataframe plot()takes an optional argument 'ax' which allows you to reuse an Axis to plot multiple lines Save plot to file Instead of callingplt.show(), callplt.savefig('outputfile.png'): importmatplotlib.pyplotaspltimportpandasaspddf.plot(kind='bar',x='name',y='age')# the plot...