matplotlib.pyplot.legend

matplotlib.pyplot.legend是一个函数，用于在图表中添加图例。图例是用于解释图表中各个元素的标签，通常是颜色、线型或符号。使用legend函数可以将这些标签添加到图表中，以便更好地理解和解释图表。该函数可以接受多个参数，包括标签、位置、字体大小等。

相关问题

帮我修改错误import pandas import pandas as pd import np import numpy np_load_old = np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) import matplotlib.pyplot matplotlib.pyplot.rcParams['font.sans-serif'] = 'SimHei' matplotlib.pyplot.rcParams['axes.unicode_minus'] = False data = numpy.load('D:\\temp\\test\\国民经济核算季度数据.npz') name = data['columns'] values = data['values'] matplotlib.pyplot.figure(figsize = (8,7)) matplotlib.pyplot.scatter(values[:,0],values[:,3], marker = 'o',c = '#0099FF') matplotlib.pyplot.scatter(values[:,0],values[:,4], marker = '*',c = '#00FF00') matplotlib.pyplot.scatter(values[:,0],values[:,5], marker = '+',c = '#FF3300') matplotlib.pyplot.xlabel('年份') matplotlib.pyplot.ylabel('生产总值（亿元）') matplotlib.pyplot.xticks(range(0,70,4),values[range(0,70,4),1],rotation=45) matplotlib.pyplot.title('2000-2017年各产业季度生产总值散点图') matplotlib.pyplot.legend(['第一产业','第二产业','第三产业']) matplotlib.pyplot.savefig('D:\\temp\\test\\2022231168许开国c.png') matplotlib.pyplot.show name1 = pandas.DataFrame(list(data['columns'])) values1 = pandas.DataFrame(list(data['values'])) with pd.ExcelWriter('D:\\temp\\test\\2022231201许开国c.xlsx') as writer: name1.to_excel(writer, sheet_name= 'Sheet1', index=False, header=True) values1.to_excel(writer, sheet_name='Sheet2', index=False, header=True)

以下是修改过的代码，其中主要是将numpy和pandas的导入方式进行了修改，并且在matplotlib.pyplot的导入语句中添加了as关键字，以便在使用时不需要使用完整的模块名：

import pandas as pd 
import numpy as np 

np_load_old = np.load 
np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) 

import matplotlib.pyplot as plt 
plt.rcParams['font.sans-serif'] = 'SimHei' 
plt.rcParams['axes.unicode_minus'] = False 

data = np.load('D:\\temp\\test\\国民经济核算季度数据.npz') 
name = data['columns'] 
values = data['values'] 

plt.figure(figsize = (8,7)) 
plt.scatter(values[:,0],values[:,3], marker = 'o',c = '#0099FF') 
plt.scatter(values[:,0],values[:,4], marker = '*',c = '#00FF00') 
plt.scatter(values[:,0],values[:,5], marker = '+',c = '#FF3300') 
plt.xlabel('年份') 
plt.ylabel('生产总值（亿元）') 
plt.xticks(range(0,70,4),values[range(0,70,4),1],rotation=45) 
plt.title('2000-2017年各产业季度生产总值散点图') 
plt.legend(['第一产业','第二产业','第三产业']) 
plt.savefig('D:\\temp\\test\\2022231168许开国c.png') 
plt.show() 

name1 = pd.DataFrame(list(data['columns'])) 
values1 = pd.DataFrame(list(data['values'])) 

with pd.ExcelWriter('D:\\temp\\test\\2022231201许开国c.xlsx') as writer: 
    name1.to_excel(writer, sheet_name= 'Sheet1', index=False, header=True) 
    values1.to_excel(writer, sheet_name='Sheet2', index=False, header=True)

这样的话，你便可以在运行程序时直接使用pd和np作为pandas和numpy的别名，使用plt来代替matplotlib.pyplot。

matplotlib.pyplot

matplotlib.pyplot is a Python library used for creating visualizations such as plots, graphs, and charts. It is a sub-library of the matplotlib library and is typically imported using the `import matplotlib.pyplot as plt` statement.

With matplotlib.pyplot, you can create various types of visualizations such as line plots, scatter plots, bar plots, histograms, and more. The library provides a vast range of customization options to fine-tune the visualizations as per your requirements.

Some commonly used functions in the matplotlib.pyplot library include:

- `plt.plot()` to create a line plot
- `plt.scatter()` to create a scatter plot
- `plt.bar()` to create a bar plot
- `plt.hist()` to create a histogram
- `plt.xlabel()` and `plt.ylabel()` to set the x and y axis labels
- `plt.title()` to set the plot title
- `plt.legend()` to add a legend to the plot
- `plt.show()` to display the plot

Overall, matplotlib.pyplot is a powerful library for data visualization in Python and is widely used by data scientists and analysts.