首页 > 编程语言> > 美国大学生数学建模竞赛绘图总结 - 使用Python matplotlib和pandas (以2021年美赛ICM D题为例)

美国大学生数学建模竞赛绘图总结 - 使用Python matplotlib和pandas (以2021年美赛ICM D题为例)

2021-02-10 15:57:09 作者：互联网

零. 写在前面

刚刚参加完的美赛，笔者最大的感受是，强行把自己的部分从数学建模做成了文学建模 [手动狗头]

好吧，本篇文章主要不是数学建模竞赛本身，而是总结一下笔者在准备以及参赛过程中使用的绘图程序，主要目的是给自己做个总结以及实现对队友许下代码开源的承诺。所以希望读者不要关注代码逻辑部分，只关注绘图。而且，该文的代码很多只是 by-products，并非最终论文中呈现的结果，故仅供参考。

其实，选择D题，很大原因是笔者对音乐的欣赏与喜爱（虽然并不十分了解这一领域）。总之，再次感谢艺术，感谢她带给人心灵的慰藉！

一. 成功运行本文代码的准备

除了需要正确配置IDE，准备好编程环境（个人喜欢用Pycharm+Anaconda），以及安装诸如matplotlib，pandas，numpy，networkx，wordcloud之类的packages外，还需要在您的py程序文件夹下提前准备好以下csv文件，毕竟这是一道有大量数据支撑的题。

这些csv文件可由百度网盘取得：https://pan.baidu.com/s/14Felkve8aowKhfOQ4rRYrw
密码：kkp2

同时分享2021年MCM_ICM的所有赛题：
https://pan.baidu.com/s/1QkpXwCLID0mUhQsiisZVTw
密码：cwyd

二. 代码部分

1. 赛前准备的基础程序

以下程序绘制了直方图，散点图，折线图，CUSUM图：

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure()
ax1 = fig.add_subplot(2,2,1)
ax2 = fig.add_subplot(2,2,2)
ax3 = fig.add_subplot(2,2,3)
ax4 = fig.add_subplot(2,2,4)

ax1.hist(np.random.randn(100), bins=20, color='g', alpha=0.5)

ax2.scatter(np.arange(30),np.arange(30)+3*np.random.randn(30),alpha=0.5)

ax3.plot(np.random.randn(50).cumsum(),"r--",label = 'Default',alpha=0.5)
ax3.legend(loc='best')

ax4.plot(np.random.randn(50).cumsum(),'darkgoldenrod',drawstyle = 'steps-post', label='step-post',alpha=0.5)

ticks = ax4.set_xticks([0,10,20,30,40,50])
labels = ax4.set_xticklabels(['one','two','three','four','five','six'],rotation=30,\
                             fontsize='small')
props = {
    'title':'A Example',
    'xlabel':'stages'
}
ax4.set(**props)

plt.show()

运行效果如下：
在这里插入图片描述
以下程序绘制了各种类型的柱状图：

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(2,2)
data = pd.Series(np.random.rand(16),index=list('abcdefghijklmnop'))
data.plot.bar(ax=axes[0][0], color='darkslateblue', alpha=0.7)
data.plot.barh(ax=axes[0][1], color='tomato',alpha=0.7)

df = pd.DataFrame(np.random.rand(6,4),\
                  index=['one','two','three','four','five','six'],\
                  columns=pd.Index(['A','B','C','D'],name='Genus'))
print(df)
df.plot.bar(ax=axes[1][0])

df.plot.barh(ax=axes[1][1],stacked=True,alpha=0.5)
plt.show()

运行效果如下：
在这里插入图片描述

2. 网络图Network

以下程序使用NetworkX，同时需要数据集 influence_data_subset2.csv ：

import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('influence_data_subset2.csv')

Graphtype = nx.Graph()
G = nx.from_pandas_edgelist(df, 'influencer_name','follower_name',create_using=nx.DiGraph())
# nx.draw(G, node_color='skyblue', with_labels=True,node_size=1000, edge_color='b', width=1)
nx.draw(G,pos = nx.circular_layout(G), node_color='tomato', with_labels=True,node_size=1000,font_size =10,edge_color='gray', width=0.5,alpha=0.9)

# plt.savefig('6_Network_of_all_great_artists.png')
plt.show()

运行结果如下：
在这里插入图片描述

3. 水平柱状图 barh

以下程序根据各个节点的度中心度 (Degree Centrality)（具体参考NetworkX官方文档），在排序后用水平柱状图展示：

import matplotlib.pyplot as plt
import pandas as pd

plt.figure(figsize=(10,6))
dict = {'Pop/Rock': 2.052631578947368, 'Electronic': 1.2105263157894737, 'Reggae': 0.8421052631578947, 'Jazz': 1.6842105263157894, 'Country': 1.4210526315789473, 'Comedy/Spoken': 0.894736842105263, 'R&B;': 1.526315789473684, 'Classical': 1.263157894736842, 'Latin': 1.4210526315789473, 'Vocal': 1.789473684210526, 'Folk': 1.3684210526315788, 'Easy Listening': 0.9473684210526315, 'International': 1.631578947368421, 'Avant-Garde': 0.7368421052631579, 'Blues': 1.1052631578947367, 'Stage & Screen': 1.2105263157894737, 'New Age': 1.3157894736842104, 'Religious': 0.8421052631578947, 'Unknown': 0.2631578947368421, "Children's": 0.3684210526315789}
xx = []
yy = []
print(xx)
for i in sorted(dict,key=dict.__getitem__):
    xx.append(i)
    yy.append(dict[i])
plt.barh(y=xx,width=yy,color='turquoise',alpha=0.9)
plt.xticks(rotation=30)
plt.xlabel("Degree Centrality")
# plt.savefig('3_Network_of_all_genres_Degree_Centrality.png')
plt.show()

运行结果如下：
在这里插入图片描述

4. 词云图 wordcloud

以下代码使用词云图寻找出频度最高的10位artists（结果只找到了9位），同时需要数据集 influence_data.csv ：

from wordcloud import WordCloud
import matplotlib.pyplot as plt
import csv
import pandas as pd

df = pd.read_csv('influence_data.csv')

text = ""

for i in range(42769):
    print(i)
    text = text+df.values[i][1].replace(" ", "")+" "

# show only 10 words in the wordcloud .
wordcloud = WordCloud(width=480,  height=480, max_words=10).generate(text)

# plot the WordCloud image
plt.figure()
plt.imshow(wordcloud,  interpolation="bilinear")
plt.axis("off")
plt.margins(x=0, y=0)
plt.savefig("5_The_word_cloud_of_outstanding_artists.png")
plt.show()

运行结果如下：
在这里插入图片描述

5. 散点图 scatter

以下代码寻找R&B, Jazz, Country之间各种characteristic的区别，同时需要数据集data_by_artist_1.csv ：

在import pandas as pd
import matplotlib.pyplot as plt
import math

data_by_artist = pd.read_csv('data_by_artist_1.csv')
plt.figure(figsize=(12,9))
label = ['Vocal', 'NULL', 'Country', 'Vocal', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Classical', 'Jazz', 'Vocal', 'Vocal', 'Pop/Rock', 'International', 'Classical', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Reggae', 'Vocal', 'Pop/Rock', 'R&B;', 'R&B;', 'Classical', 'Pop/Rock', 'Country', 'Pop/Rock', 'Jazz', 'Jazz', 'Latin', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'Latin', 'R&B;', 'Vocal', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Vocal', 'Stage & Screen', 'NULL', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'R&B;', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'International', 'Pop/Rock', 'Vocal', 'Pop/Rock', 'Pop/Rock', 'Avant-Garde', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'International', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Vocal', 'Latin', 'Jazz', 'Pop/Rock', 'Country', 'Country', 'Jazz', 'Jazz', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Easy Listening', 'Easy Listening', 'Pop/Rock', 'Pop/Rock', 'Country', 'Blues', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Latin', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Jazz', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'International', 'Pop/Rock', 'R&B;', 'Country', 'Vocal', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Latin', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'Latin', 'Vocal', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Classical', 'Country', 'R&B;', 'Latin', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Vocal', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Reggae', 'Vocal', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Folk', 'R&B;', 'Jazz', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Reggae', 'Pop/Rock', 'Stage & Screen', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Easy Listening', 'Blues', 'Religious', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Blues', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'R&B;', 'Pop/Rock', 'Vocal', 'Pop/Rock', 'Pop/Rock', 'Vocal', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Folk', 'Pop/Rock', 'Jazz', 'Vocal', 'Jazz', 'Latin', 'Jazz', 'Vocal', 'Classical', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Country', 'Easy Listening', 'Electronic', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Country', 'Folk', 'Jazz', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Blues', 'Jazz', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Folk', 'R&B;', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Easy Listening', 'Pop/Rock', 'New Age', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Pop/Rock', 'Vocal', 'Pop/Rock', 'Latin', 'Pop/Rock', 'Jazz', 'Easy Listening', 'Latin', 'Jazz', 'Pop/Rock', 'R&B;', 'Country', 'Country', 'Jazz', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Vocal', 'Pop/Rock', 'NULL', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Folk', 'R&B;', 'R&B;', 'Latin', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Latin', 'R&B;', 'Country', 'Pop/Rock', 'Vocal', 'Pop/Rock', 'R&B;', 'Jazz', 'Vocal', 'Latin', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Latin', 'Latin', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Country', 'Jazz', 'Country', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Country', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'R&B;', 'Vocal', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Easy Listening', 'Pop/Rock', 'Latin', 'Pop/Rock', 'R&B;', 'NULL', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'Vocal', 'Vocal', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Folk', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Electronic', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Country', 'Country', 'Pop/Rock', 'Pop/Rock', 'Blues', 'Folk', 'Stage & Screen', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Reggae', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Latin', 'Pop/Rock', 'Stage & Screen', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Country', 'Latin', 'Pop/Rock', 'Vocal', 'R&B;', 'Pop/Rock', 'Stage & Screen', 'Pop/Rock', 'R&B;', 'Jazz', 'Country', 'Latin', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Blues', 'Stage & Screen', 'Vocal', 'Pop/Rock', 'Country', 'Pop/Rock', 'Vocal', 'Pop/Rock', 'R&B;', 'Latin', 'Country', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'International', 'R&B;', 'Pop/Rock', 'R&B;', 'NULL', 'Country', 'Pop/Rock', 'Country', 'R&B;', 'Comedy/Spoken', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'New Age', 'Pop/Rock', 'Country', 'Country', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'R&B;', 'Latin', 'R&B;', 'Country', 'Country', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'Latin', 'Jazz', 'Vocal', 'Stage & Screen', 'Vocal', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Country', 'Folk', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Latin', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Latin', 'Pop/Rock', 'Pop/Rock', 'Electronic', 'Pop/Rock', 'Stage & Screen', 'Pop/Rock', 'R&B;', 'R&B;', 'Pop/Rock', 'Stage & Screen', 'Country', 'R&B;', 'Pop/Rock', 'Folk', 'R&B;', 'Pop/Rock', 'Comedy/Spoken', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'NULL', 'Country', 'Pop/Rock', 'Latin', 'R&B;', 'Easy Listening', 'Pop/Rock', 'NULL', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Jazz', 'Jazz', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'Vocal', 'Pop/Rock', 'R&B;', 'Country', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Country', 'R&B;', 'Pop/Rock', 'Vocal', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Country', 'Pop/Rock', 'R&B;', 'Latin', 'Jazz', 'Reggae', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Latin', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'R&B;', 'Jazz', 'Pop/Rock', 'Vocal', 'Latin', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Religious', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Country', 'Pop/Rock', 'Vocal', 'Stage & Screen', 'Latin', 'Latin', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Comedy/Spoken', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'R&B;', 'Blues', 'Pop/Rock', 'Reggae', 'R&B;', 'R&B;', 'Pop/Rock', 'Electronic', 'Pop/Rock', 'NULL', 'R&B;', 'Latin', 'Jazz', 'Pop/Rock', 'Folk', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Country', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Country', 'Reggae', 'Pop/Rock', 'Latin', 'Classical', 'Country', 'R&B;', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Folk', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'R&B;', 'Blues', 'Reggae', 'NULL', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Country', 'Pop/Rock', 'Latin', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Latin', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Country', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Blues', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Jazz', 'Country', 'Pop/Rock', 'R&B;', 'R&B;', 'Pop/Rock', 'Electronic', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'Vocal', 'Country', 'Pop/Rock', 'Jazz', 'Country', 'Pop/Rock', 'R&B;', 'International', 'Pop/Rock', 'Stage & Screen', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'Pop/Rock', 'Country', 'Vocal', 'Jazz', 'Pop/Rock', 'Vocal', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'Vocal', 'R&B;', 'Electronic', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Religious', 'Pop/Rock', 'Pop/Rock', 'Religious', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Latin', 'Pop/Rock', 'Electronic', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Electronic', 'Pop/Rock', 'Pop/Rock', 'NULL', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Latin', 'Pop/Rock', 'Latin', 'Country', 'Jazz', 'Country', 'Pop/Rock', 'Pop/Rock', 'Avant-Garde', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'Vocal', 'R&B;', 'Jazz', 'Pop/Rock', 'Latin', 'R&B;', 'Pop/Rock', 'R&B;', 'Reggae', 'Latin', 'Pop/Rock', 'Country', 'Jazz', 'Latin', 'Latin', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Blues', 'Pop/Rock', 'Latin', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Folk', 'Country', 'Latin', 'Pop/Rock', 'Pop/Rock', 'Folk', 'Vocal', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'R&B;', 'R&B;', 'Pop/Rock', 'R&B;', 'R&B;', 'Country', 'Pop/Rock', 'Pop/Rock', 'Vocal', 'Country', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Pop/Rock', 'R&B;', 'Country', 'Pop/Rock', 'NULL', 'Blues', 'Pop/Rock', 'International', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Stage & Screen', 'Country', 'Country', 'Latin', 'NULL', 'Jazz', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Electronic', 'Jazz', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Latin', 'Jazz', 'Pop/Rock', 'Latin', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 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'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Electronic', 'Electronic', 'Pop/Rock', 'Electronic', 'Pop/Rock', 'Electronic', 'Country', 'Religious', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'NULL', 'Pop/Rock', 'R&B;', 'Electronic', 'R&B;', 'R&B;', 'Pop/Rock', 'Reggae', 'Pop/Rock', 'Country', 'Pop/Rock', 'Electronic', 'Pop/Rock', 'R&B;', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Country', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Electronic', 'R&B;', 'Country', 'R&B;', 'Pop/Rock', 'NULL', 'Country', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'Pop/Rock', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Pop/Rock', 'Country', 'R&B;', 'Pop/Rock', 'R&B;', 'Jazz', 'R&B;', 'Pop/Rock', 'Pop/Rock', 'NULL', 'NULL', 'Country', 'Jazz', 'Pop/Rock', 'International', 'Vocal', 'NULL', 'Vocal', 'Jazz', 'Country', 'Electronic', 'NULL', 'Pop/Rock', 'Jazz', 'Pop/Rock', 'NULL', 'New Age', 'NULL', 'Jazz', 'Pop/Rock', 'R&B;', 'Latin', 'Jazz', 'Latin', 'Latin', 'Pop/Rock', 'International', 'Jazz', 'Pop/Rock', 'Stage & Screen', 'Pop/Rock', 'Country', 'Pop/Rock', 'Jazz', 'Electronic', 'Reggae', 'NULL', 'Jazz', 'International', 'Country', 'International', 'NULL', 'Comedy/Spoken', 'NULL', 'Electronic', 'R&B;', 'R&B;', 'Jazz', 'Latin', 'Pop/Rock', 'NULL', 'Pop/Rock', 'Pop/Rock', 'Jazz', 'Country', 'Comedy/Spoken', 'Pop/Rock', 'Avant-Garde', 'Blues', 'Pop/Rock', 'Electronic', 'Electronic', 'NULL', 'Jazz', 'Religious', 'Electronic', 'Pop/Rock', 'NULL', 'Latin', 'Jazz', 'Country', 'Electronic', 'R&B;', 'Religious', 'Country', 'Pop/Rock', 'Pop/Rock', 'Vocal', 'Latin', 'R&B;', 'Folk', 'Folk', 'International', 'International', 'Electronic', 'Vocal', 'R&B;', 'Vocal', 'Jazz', 'Pop/Rock', 'Electronic', 'Latin', 'Pop/Rock', 'NULL', 'Vocal', 'Vocal', 'Comedy/Spoken', 'Electronic', 'Pop/Rock', 'Comedy/Spoken', 'Jazz', 'NULL', 'R&B;', 'International', 'R&B;', 'Stage & Screen', 'Pop/Rock', 'Jazz', 'Comedy/Spoken', 'Jazz', 'Electronic', 'R&B;', 'Pop/Rock', 'R&B;', 'Stage & Screen', 'Electronic', 'Electronic', 'NULL', 'R&B;', 'Electronic', 'Electronic', 'Pop/Rock', 'Electronic', 'Pop/Rock', 'R&B;', 'R&B;', 'Religious', 'R&B;', 'Electronic', 'R&B;', 'Country', 'R&B;', 'Country', 'Pop/Rock']

y = []
x = []

fig = plt.figure()
ax1 = fig.add_subplot(2,3,1)
ax2 = fig.add_subplot(2,3,2)
ax3 = fig.add_subplot(2,3,3)
ax4 = fig.add_subplot(2,3,4)
ax5 = fig.add_subplot(2,3,5)
ax6 = fig.add_subplot(2,3,6)
# ax7 = fig.add_subplot(2,4,8)

props = {
    'title':'danceability'
}
ax1.set(**props)

props = {
    'title':'energy'
}
ax2.set(**props)

props = {
    'title':'valence'
}
ax3.set(**props)

props = {
    'title':'tempo'
}
ax4.set(**props)

props = {
    'title':'loudness'
}
ax5.set(**props)

props = {
    'title':'key'
}
ax6.set(**props)


for i in range(len(label)):
    print(i)
    if label[i] == 'R&B;':
        g1 = ax1.scatter(i,data_by_artist.values[i][2],color='red',alpha=0.7)
        ax2.scatter(i,data_by_artist.values[i][3],color='red',alpha=0.7)
        ax3.scatter(i, data_by_artist.values[i][4], color='red', alpha=0.7)
        ax4.scatter(i,data_by_artist.values[i][5],color='red',alpha=0.7)
        ax5.scatter(i,data_by_artist.values[i][6],color='red',alpha=0.7)
        ax6.scatter(i, data_by_artist.values[i][8], color='red', alpha=0.7)

    elif label[i] == 'Jazz':
        g2 = ax1.scatter(i, data_by_artist.values[i][2], color='blue', alpha=0.7)
        ax2.scatter(i, data_by_artist.values[i][3], color='blue', alpha=0.7)
        ax3.scatter(i, data_by_artist.values[i][4], color='blue', alpha=0.7)
        ax4.scatter(i, data_by_artist.values[i][5], color='blue', alpha=0.7)
        ax5.scatter(i, data_by_artist.values[i][6], color='blue', alpha=0.7)
        ax6.scatter(i, data_by_artist.values[i][8], color='blue', alpha=0.7)

    elif label[i] == 'Country':
        g3 = ax1.scatter(i, data_by_artist.values[i][2], color='green', alpha=0.7)
        ax2.scatter(i, data_by_artist.values[i][3], color='green', alpha=0.7)
        ax3.scatter(i, data_by_artist.values[i][4], color='green', alpha=0.7)
        ax4.scatter(i, data_by_artist.values[i][5], color='green', alpha=0.7)
        ax5.scatter(i, data_by_artist.values[i][6], color='green', alpha=0.7)
        ax6.scatter(i, data_by_artist.values[i][8], color='green', alpha=0.7)


# plt.legend(handles=[g1, g2, g3], labels=['R&B;', 'Jazz', 'Country'])
plt.tight_layout()
# plt.savefig("2_Identify_the_most_important_factors_related_to_RICHNESS.png")
plt.show()

运行结果如下：
在这里插入图片描述

6. 棋盘图 checkerboard

可视化genres间的影响程度，这个程序貌似是我原创的（我还没检索过，不过确实是我自己想出来的表示方法）：

import pandas as pd
import matplotlib.pyplot as plt

a = [[2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 584, 0, 166, 0, 6, 0, 2, 0, 0, 10, 0, 0, 34, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 18, 31, 0, 0, 5, 1, 0, 8, 3, 0, 1, 0, 5, 0, 0, 0, 0, 4],
[0, 4, 24, 9, 22015, 30, 284, 10, 37, 0, 95, 130, 47, 11, 501, 37, 518, 166, 83, 25, 75],
[1, 0, 1, 0, 33, 101, 4, 1, 0, 0, 0, 0, 0, 0, 1, 0, 2, 5, 0, 0, 3],
[0, 0, 104, 0, 1624, 4, 3374, 0, 11, 0, 71, 31, 50, 2, 88, 1, 39, 6, 74, 2, 38],
[1, 0, 0, 4, 51, 0, 0, 80, 1, 0, 6, 0, 0, 1, 11, 0, 1, 2, 0, 1, 23],
[0, 2, 11, 2, 116, 0, 12, 0, 81, 0, 20, 37, 1, 2, 5, 12, 0, 11, 1, 5, 10],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 11, 12, 409, 5, 176, 14, 22, 0, 1769, 41, 33, 2, 46, 28, 24, 11, 2, 5, 105],
[0, 0, 0, 5, 103, 0, 8, 1, 41, 0, 37, 402, 0, 0, 7, 3, 1, 0, 1, 1, 8],
[0, 0, 3, 0, 689, 0, 116, 0, 5, 0, 17, 1, 372, 0, 1, 1, 6, 24, 1, 0, 5],
[0, 0, 0, 0, 42, 0, 0, 1, 0, 0, 0, 0, 0, 7, 17, 7, 0, 0, 0, 5, 0],
[0, 0, 0, 0, 297, 0, 11, 1, 0, 0, 0, 1, 0, 0, 371, 26, 0, 0, 0, 1, 0],
[0, 0, 0, 1, 18, 0, 0, 2, 2, 0, 5, 1, 0, 0, 10, 47, 0, 2, 0, 3, 3],
[0, 3, 1, 0, 641, 1, 7, 0, 3, 0, 11, 2, 10, 0, 2, 4, 2496, 101, 6, 0, 7],
[0, 0, 0, 0, 553, 5, 8, 0, 29, 0, 1, 1, 3, 0, 3, 17, 107, 271, 7, 1, 3],
[0, 0, 0, 0, 37, 0, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 3, 155, 0, 4],
[0, 0, 0, 1, 43, 0, 0, 5, 2, 0, 2, 1, 0, 11, 16, 4, 2, 0, 0, 17, 10],
[1, 0, 5, 2, 381, 4, 230, 8, 22, 0, 84, 45, 15, 0, 2, 1, 36, 16, 9, 15, 537]

]

# plt.figure(figsize=(10,10))
for i in range(21):
    for j in range(21):
        if(a[i][j]>100):
            plt.scatter(i,j,color = 'red',s = 80,alpha=0.7)
        elif (a[i][j] > 50):
            plt.scatter(i, j, color='orange', s=50, alpha=0.7)
        elif (a[i][j] > 10):
            plt.scatter(i, j, color='y', s=50, alpha=0.7)
        elif (a[i][j] > 0):
            plt.scatter(i, j, color='green', s=50, alpha=0.7)
        else:
            plt.scatter(i, j, color='black', s=20, alpha=0.7)
            
xxx = []
for i in range(21):
    xxx.append(i)
yyy = xxx

xranges = range(0,21,1)
x_labels=('Children\'s', 'Unknown', 'Reggae', 'Easy Listening', 'Pop/Rock', 'Comedy/Spoken', 'R&B;', 'Stage & Screen', 'International', 'NULL', 'Jazz', 'Latin', 'Blues', 'Avant-Garde', 'Electronic', 'New Age', 'Country', 'Folk', 'Religious', 'Classical', 'Vocal')

plt.yticks(xranges,x_labels)
plt.xticks(xranges,x_labels,rotation = 80,fontsize = 'small')
plt.plot(xxx,yyy)

plt.grid()
# plt.savefig("1_Influence_between_genres_assessed_by_AVG.png")
plt.show()

运行结果如下：
在这里插入图片描述

7. 折线图 plot (单条)

以下代码发现了音乐发展历史与社会背景的重大联系，需要使用数据集data_by_year_1.csv :

import pandas as pd
import matplotlib.pyplot as plt
import math
import numpy as np

data_by_year = pd.read_csv('data_by_year_1.csv')

def norm(x):
    return math.sqrt(data_by_year.values[x][1]**2+data_by_year.values[x][2]**2+ \
    data_by_year.values[x][3] ** 2 +data_by_year.values[x][4]**2+ \
    data_by_year.values[x][5] ** 2 +data_by_year.values[x][6]**2+ \
    data_by_year.values[x][7] ** 2)

def relation(x,y):
    dot = data_by_year.values[x][1]*data_by_year.values[y][1]+ \
          data_by_year.values[x][2]*data_by_year.values[y][2]+ \
          data_by_year.values[x][3] * data_by_year.values[y][3] + \
          data_by_year.values[x][4] * data_by_year.values[y][4] + \
          data_by_year.values[x][5] * data_by_year.values[y][5] + \
          data_by_year.values[x][6] * data_by_year.values[y][6] + \
          data_by_year.values[x][7] * data_by_year.values[y][7]
    if math.fabs((norm(x)*norm(y))-dot)<0.0000001:
        return 0
    else:
        return math.acos(dot/(norm(x)*norm(y)))

def norm1(x):
    return math.sqrt(data_by_year.values[x][1]**2+data_by_year.values[x][2]**2)

xx = []
yy = []
yy1 = []
for i in range(100):
    xx.append(i)
    yy.append(norm(i))
    yy1.append(norm1(i))

# plt.plot(xx,yy)
xranges = range(0,101,20)
xlabels = ('1920','1940','1960','1980','2000','2020')
plt.xticks(xranges,xlabels)
plt.plot(xx,yy1,'r',alpha = 0.9)
plt.xlabel("years",fontsize=14)
plt.ylabel("Norm",fontsize=14)
plt.savefig("2_The_RICHNESS_changing_over_years")
plt.show()

运行结果如下：
在这里插入图片描述

8. 折线图 plot (两条)

以下代码发现energy和loudness变化趋势一致，需要使用数据集data_by_artist_1.csv :

在import pandas as pd
import matplotlib.pyplot as plt
import math
import random

data_by_artist = pd.read_csv('data_by_artist_1.csv')
yy1 = []
yy2 = []
xx = []

for k in range(50):
    i = random.randint(0,5854)
    print(i)
    yy1.append(data_by_artist.values[i][3])
    yy2.append(data_by_artist.values[i][6])
    xx.append(k)

g1 = plt.plot(xx,yy1)
g2 = plt.plot(xx,yy2)
l1 = plt.legend(['Energy', 'Loudness'])
# plt.savefig("3_Random_test_of_the_possible_relation_between_Energy_and_loudness.png")
plt.show()

运行结果如下：
在这里插入图片描述

9. 折线图 plot (6个子图)

以下代码体现characteristics按年变化规律，需要使用数据集full_music_data_popular1.csv :

import pandas as pd
import matplotlib.pyplot as plt
import math

full_music = pd.read_csv('full_music_data_popular1.csv')

fig = plt.figure()
ax1 = fig.add_subplot(2,3,1)
ax2 = fig.add_subplot(2,3,2)
ax3 = fig.add_subplot(2,3,3)
ax4 = fig.add_subplot(2,3,4)
ax5 = fig.add_subplot(2,3,5)
ax6 = fig.add_subplot(2,3,6)
# ax7 = fig.add_subplot(2,4,8)

props = {
    'title':'acousticness'
}
ax1.set(**props)

props = {
    'title':'instrumentalness'
}
ax2.set(**props)

props = {
    'title':'liveness'
}
ax3.set(**props)

props = {
    'title':'speechiness'
}
ax4.set(**props)

props = {
    'title':'duration_ms'
}
ax5.set(**props)

props = {
    'title':'popularity'
}
ax6.set(**props)


x = [1920,1930,1940,1950,1960,1970,1980,1990,2000,2010]
y_sum = [0]*10
y_num = [0]*10
y = [0]*10
for i in range(3414):
    print(i)
    if full_music.values[i][16]>=1950 and full_music.values[i][16]<1960:
        y_num[3] = y_num[3]+1
        y_sum[3] = y_sum[3]+full_music.values[i][9]
    elif full_music.values[i][16]>=1960 and full_music.values[i][16]<1970:
        y_num[4] = y_num[4]+1
        y_sum[4] = y_sum[4]+full_music.values[i][9]
    elif full_music.values[i][16]>=1970 and full_music.values[i][16]<1980:
        y_num[5] = y_num[5]+1
        y_sum[5] = y_sum[5]+full_music.values[i][9]
    elif full_music.values[i][16]>=1980 and full_music.values[i][16]<1990:
        y_num[6] = y_num[6]+1
        y_sum[6] = y_sum[6]+full_music.values[i][9]
    elif full_music.values[i][16]>=1990 and full_music.values[i][16]<2000:
        y_num[7] = y_num[7]+1
        y_sum[7] = y_sum[7]+full_music.values[i][9]
    elif full_music.values[i][16]>=2000 and full_music.values[i][16]<2010:
        y_num[8] = y_num[8]+1
        y_sum[8] = y_sum[8]+full_music.values[i][9]
    elif full_music.values[i][16]>=2010 and full_music.values[i][16]<2020:
        y_num[9] = y_num[9]+1
        y_sum[9] = y_sum[9]+full_music.values[i][9]

for i in range(10):
    if y_num[i] != 0:
        y[i] = y_sum[i]/y_num[i]

ax1.plot(x[3:],y[3:],'go--')
ax1.set_ylim(0,1)


x = [1920,1930,1940,1950,1960,1970,1980,1990,2000,2010]
y_sum = [0]*10
y_num = [0]*10
y = [0]*10
for i in range(3414):
    print(i)
    if full_music.values[i][16]>=1950 and full_music.values[i][16]<1960:
        y_num[3] = y_num[3]+1
        y_sum[3] = y_sum[3]+full_music.values[i][10]
    elif full_music.values[i][16]>=1960 and full_music.values[i][16]<1970:
        y_num[4] = y_num[4]+1
        y_sum[4] = y_sum[4]+full_music.values[i][10]
    elif full_music.values[i][16]>=1970 and full_music.values[i][16]<1980:
        y_num[5] = y_num[5]+1
        y_sum[5] = y_sum[5]+full_music.values[i][10]
    elif full_music.values[i][16]>=1980 and full_music.values[i][16]<1990:
        y_num[6] = y_num[6]+1
        y_sum[6] = y_sum[6]+full_music.values[i][10]
    elif full_music.values[i][16]>=1990 and full_music.values[i][16]<2000:
        y_num[7] = y_num[7]+1
        y_sum[7] = y_sum[7]+full_music.values[i][10]
    elif full_music.values[i][16]>=2000 and full_music.values[i][16]<2010:
        y_num[8] = y_num[8]+1
        y_sum[8] = y_sum[8]+full_music.values[i][10]
    elif full_music.values[i][16]>=2010 and full_music.values[i][16]<2020:
        y_num[9] = y_num[9]+1
        y_sum[9] = y_sum[9]+full_music.values[i][10]

for i in range(10):
    if y_num[i] != 0:
        y[i] = y_sum[i]/y_num[i]

ax2.plot(x[3:],y[3:],'go--')
ax2.set_ylim(0,1)


x = [1920,1930,1940,1950,1960,1970,1980,1990,2000,2010]
y_sum = [0]*10
y_num = [0]*10
y = [0]*10
for i in range(3414):
    print(i)
    if full_music.values[i][16]>=1950 and full_music.values[i][16]<1960:
        y_num[3] = y_num[3]+1
        y_sum[3] = y_sum[3]+full_music.values[i][11]
    elif full_music.values[i][16]>=1960 and full_music.values[i][16]<1970:
        y_num[4] = y_num[4]+1
        y_sum[4] = y_sum[4]+full_music.values[i][11]
    elif full_music.values[i][16]>=1970 and full_music.values[i][16]<1980:
        y_num[5] = y_num[5]+1
        y_sum[5] = y_sum[5]+full_music.values[i][11]
    elif full_music.values[i][16]>=1980 and full_music.values[i][16]<1990:
        y_num[6] = y_num[6]+1
        y_sum[6] = y_sum[6]+full_music.values[i][11]
    elif full_music.values[i][16]>=1990 and full_music.values[i][16]<2000:
        y_num[7] = y_num[7]+1
        y_sum[7] = y_sum[7]+full_music.values[i][11]
    elif full_music.values[i][16]>=2000 and full_music.values[i][16]<2010:
        y_num[8] = y_num[8]+1
        y_sum[8] = y_sum[8]+full_music.values[i][11]
    elif full_music.values[i][16]>=2010 and full_music.values[i][16]<2020:
        y_num[9] = y_num[9]+1
        y_sum[9] = y_sum[9]+full_music.values[i][11]

for i in range(10):
    if y_num[i] != 0:
        y[i] = y_sum[i]/y_num[i]

ax3.plot(x[3:],y[3:],'go--')
ax3.set_ylim(0,1)


x = [1920,1930,1940,1950,1960,1970,1980,1990,2000,2010]
y_sum = [0]*10
y_num = [0]*10
y = [0]*10
for i in range(3414):
    print(i)
    if full_music.values[i][16]>=1950 and full_music.values[i][16]<1960:
        y_num[3] = y_num[3]+1
        y_sum[3] = y_sum[3]+full_music.values[i][12]
    elif full_music.values[i][16]>=1960 and full_music.values[i][16]<1970:
        y_num[4] = y_num[4]+1
        y_sum[4] = y_sum[4]+full_music.values[i][12]
    elif full_music.values[i][16]>=1970 and full_music.values[i][16]<1980:
        y_num[5] = y_num[5]+1
        y_sum[5] = y_sum[5]+full_music.values[i][12]
    elif full_music.values[i][16]>=1980 and full_music.values[i][16]<1990:
        y_num[6] = y_num[6]+1
        y_sum[6] = y_sum[6]+full_music.values[i][12]
    elif full_music.values[i][16]>=1990 and full_music.values[i][16]<2000:
        y_num[7] = y_num[7]+1
        y_sum[7] = y_sum[7]+full_music.values[i][12]
    elif full_music.values[i][16]>=2000 and full_music.values[i][16]<2010:
        y_num[8] = y_num[8]+1
        y_sum[8] = y_sum[8]+full_music.values[i][12]
    elif full_music.values[i][16]>=2010 and full_music.values[i][16]<2020:
        y_num[9] = y_num[9]+1
        y_sum[9] = y_sum[9]+full_music.values[i][12]

for i in range(10):
    if y_num[i] != 0:
        y[i] = y_sum[i]/y_num[i]

ax4.plot(x[3:],y[3:],'go--')
ax4.set_ylim(0,1)


x = [1920,1930,1940,1950,1960,1970,1980,1990,2000,2010]
y_sum = [0]*10
y_num = [0]*10
y = [0]*10
for i in range(3414):
    print(i)
    if full_music.values[i][16]>=1950 and full_music.values[i][16]<1960:
        y_num[3] = y_num[3]+1
        y_sum[3] = y_sum[3]+full_music.values[i][14]
    elif full_music.values[i][16]>=1960 and full_music.values[i][16]<1970:
        y_num[4] = y_num[4]+1
        y_sum[4] = y_sum[4]+full_music.values[i][14]
    elif full_music.values[i][16]>=1970 and full_music.values[i][16]<1980:
        y_num[5] = y_num[5]+1
        y_sum[5] = y_sum[5]+full_music.values[i][14]
    elif full_music.values[i][16]>=1980 and full_music.values[i][16]<1990:
        y_num[6] = y_num[6]+1
        y_sum[6] = y_sum[6]+full_music.values[i][14]
    elif full_music.values[i][16]>=1990 and full_music.values[i][16]<2000:
        y_num[7] = y_num[7]+1
        y_sum[7] = y_sum[7]+full_music.values[i][14]
    elif full_music.values[i][16]>=2000 and full_music.values[i][16]<2010:
        y_num[8] = y_num[8]+1
        y_sum[8] = y_sum[8]+full_music.values[i][14]
    elif full_music.values[i][16]>=2010 and full_music.values[i][16]<2020:
        y_num[9] = y_num[9]+1
        y_sum[9] = y_sum[9]+full_music.values[i][14]

for i in range(10):
    if y_num[i] != 0:
        y[i] = y_sum[i]/y_num[i]

ax5.plot(x[3:],y[3:],'go--')
ax5.set_ylim(0,400000)

x = [1920,1930,1940,1950,1960,1970,1980,1990,2000,2010]
y_sum = [0]*10
y_num = [0]*10
y = [0]*10
for i in range(3414):
    print(i)
    if full_music.values[i][16]>=1950 and full_music.values[i][16]<1960:
        y_num[3] = y_num[3]+1
        y_sum[3] = y_sum[3]+full_music.values[i][15]
    elif full_music.values[i][16]>=1960 and full_music.values[i][16]<1970:
        y_num[4] = y_num[4]+1
        y_sum[4] = y_sum[4]+full_music.values[i][15]
    elif full_music.values[i][16]>=1970 and full_music.values[i][16]<1980:
        y_num[5] = y_num[5]+1
        y_sum[5] = y_sum[5]+full_music.values[i][15]
    elif full_music.values[i][16]>=1980 and full_music.values[i][16]<1990:
        y_num[6] = y_num[6]+1
        y_sum[6] = y_sum[6]+full_music.values[i][15]
    elif full_music.values[i][16]>=1990 and full_music.values[i][16]<2000:
        y_num[7] = y_num[7]+1
        y_sum[7] = y_sum[7]+full_music.values[i][15]
    elif full_music.values[i][16]>=2000 and full_music.values[i][16]<2010:
        y_num[8] = y_num[8]+1
        y_sum[8] = y_sum[8]+full_music.values[i][15]
    elif full_music.values[i][16]>=2010 and full_music.values[i][16]<2020:
        y_num[9] = y_num[9]+1
        y_sum[9] = y_sum[9]+full_music.values[i][15]

for i in range(10):
    if y_num[i] != 0:
        y[i] = y_sum[i]/y_num[i]

ax6.plot(x[3:],y[3:],'go--')
ax6.set_ylim(0,100)

plt.tight_layout()

plt.savefig("4_2_Pop_Rock_except_music_Changing_over_Years.png")
plt.show()

运行结果如下：
在这里插入图片描述

三. 后记

美赛期间写的程序远不止这些，毕竟美赛有整整四天。笔者是一个懒惰者，如果以后有机会再整理吧。其次，笔者深知自己才学浅薄，故是一个不重视比赛结果的人，只是确实相对享受比赛的过程，希望自己的假期有点事干。

标签：Latin,ICM,Python,Country,Jazz,Pop,matplotlib,Rock,NULL
来源： https://blog.csdn.net/qq_45681057/article/details/113782189