读取文本文件movies_revenue_starring_1950_2010.txt, 首先按照电影title降序排序,然后分别可视化电影的revenue Starring Actors Popularity 的值,每幅图均包括图例、图标题,x轴刻度值为电影title且斜45°显示(为了显示美观,可以将电影title每隔若干个抽样显示),每幅图曲线颜色分别为红色、绿色;每幅图分别保存为png图片保存,分辨率为400 dpi,png图片命名分别为movies_revenue_1950_2010.png、m
时间: 2023-12-14 17:36:17 浏览: 35
ovies_starring_1950_2010.png、movies_popularity_1950_2010.png。
以下是实现该任务的Python代码:
```python
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# 读取数据
df = pd.read_csv('movies_revenue_starring_1950_2010.txt', sep='\t')
# 按照电影title降序排序
df = df.sort_values(by='Title', ascending=False)
# 抽样显示电影title
n = 10 # 抽样间隔
titles = df['Title'][::n].tolist()
x_ticks = np.arange(0, len(df), n)
# 可视化电影的revenue
plt.figure(figsize=(16, 8))
plt.plot(df['Revenue'], 'r-', label='Revenue')
plt.xticks(x_ticks, titles, rotation=45, ha='right')
plt.legend()
plt.title('Revenue of Movies from 1950 to 2010')
plt.savefig('movies_revenue_1950_2010.png', dpi=400)
# 可视化电影的starring actors
plt.figure(figsize=(16, 8))
plt.plot(df['Starring Actors'], 'g-', label='Starring Actors')
plt.xticks(x_ticks, titles, rotation=45, ha='right')
plt.legend()
plt.title('Starring Actors of Movies from 1950 to 2010')
plt.savefig('movies_starring_1950_2010.png', dpi=400)
# 可视化电影的popularity
plt.figure(figsize=(16, 8))
plt.plot(df['Popularity'], 'b-', label='Popularity')
plt.xticks(x_ticks, titles, rotation=45, ha='right')
plt.legend()
plt.title('Popularity of Movies from 1950 to 2010')
plt.savefig('movies_popularity_1950_2010.png', dpi=400)
```
运行以上代码,即可生成三张分别为movies_revenue_1950_2010.png、movies_starring_1950_2010.png、movies_popularity_1950_2010.png的图像文件。