ax2.pie(data2, labels=data2_label, radius=1, colors=inner_colors, autopct='%1.1f%%', labeldistance=0.4, pctdistance=0.9,wedgeprops=dict(width=0.6, edgecolor='w')) plt.show() 2 箱线图 2.1 参数说明 matplotlib绘制箱线图通过boxplot()方法实现,主要参数如下: boxplot()方法返回值是一个dict,...
Matplot++ is a graphics library for data visualization that provides interactive plotting, means for exporting plots in high-quality formats for scientific publications, a compact syntax consistent with similar libraries, dozens of plot categories with specialized algorithms, multiple coding styles, and ...
for j, data in enumerate(valid_loader): inputs, labels = data outputs = net(inputs) loss = criterion(outputs, labels) _, predicted = torch.max(outputs.data, 1) total_val += labels.size(0) correct_val += (predicted == labels).squeeze().sum().numpy() loss_val += loss.item() ...
setp(ax, xticklabels=['1st', '2nd']) plt.xlabel('data set') plt.ylabel('value') plt.title('Boxplot') # tag: matplotlib_13 # title: Boxplot for two data sets # size: 70 Out[24]: Text(0.5,1,'Boxplot') In [25]: from matplotlib.patches import Polygon def func(x): ...
import matplotlib.pyplot as plt labels = ['C++', 'Python', 'Go', 'Ruby', 'JavaScript'] price = [50, 60, 90, 100, 150] sns.barplot(x = price, y = labels) plt.show() Output Put a legend A legend in data visualization is a small box that exists in any one corner of the gr...
Matplotlib allows the coders to add tiitle and labels to the axes using the title() , xlabel() , and ylabel() functions. Matplotlib ships with a number of predefined styles in order to create beautiful charts and plots. Besides plotting line charts, the coders can also plot bar charts ...
mpldatacursor包可以为matplotlib提供交互式的数据光标(弹出式注释框)。 它的典型功能是: 鼠标左键单击图表数据元素时会弹出文本框显示最近的数据元素的坐标值。 鼠标右键单击文本框取消显示数据光标。 按d键时切换显示\关闭数据光标。 安装 如果matplotlib版本低于3.3可以直接使用pip安装 ...
[:,1:])# Create a 3x5 matrix of subplots.fig, axes = plt.subplots(3,5, figsize=(18,11))# Create padding around subplots to make the axis labels readable.fig.tight_layout(pad=2.0)# Loop over the columns of the DataFrame and create a box plot for each one.foriinrange(len(...
importmatplotlib.pyplotasplt labels=result.keys()sizes=result.values()plt.pie(sizes,labels=labels,autopct='%1.1f%%')plt.axis('equal')plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 运行以上代码将会生成一个饼状图,展示了字符串中每个字母的出现频率。
importmatplotlib.pyplotasplt# 饼状图数据labels=date_counts.keys()sizes=date_counts.values()# 绘制饼状图plt.pie(sizes,labels=labels,autopct='%1.1f%%')plt.axis('equal')plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 运行代码后,我们将得到一个饼状图,它显示了每个日期的文章数量占比。