C題：The Development Trend of New Energy Electric Vehicles in China中國談新能源電動汽車的發展趨勢
第一問部分：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import seaborn as sns
from statsmodels.tsa.arima.model import ARIMA
from sklearn.preprocessing import StandardScaler
import matplotlib# 使用Times New Roman字體
matplotlib.rcParams['font.family'] = 'Times New Roman'# 繪制折線圖
plt.plot(pd_power['磷酸鐵鋰動力電池裝機量/GWh'], label='Lithium',marker='o')
plt.plot(pd_power['三元電池裝機量/GWh'], label='SanYuan',marker='o')# 添加圖例
plt.legend(loc='upper left')# 設置x軸標簽和標題
plt.xlabel('Year')
plt.title('Installed capacity/GWh')plt.xticks([0,1,2,3,4,5,6],pd_power['年份'])# 顯示圖表
plt.tight_layout()
plt.show()
import matplotlib# 使用Times New Roman字體
matplotlib.rcParams['font.family'] = 'Times New Roman'# 繪制折線圖
plt.plot(year_sale_list[::-1], label='NEEV',marker='o')# 添加圖例
plt.legend(loc='upper left')# 設置x軸標簽和標題
plt.xlabel('time')
plt.title('Sales')plt.xticks([0,1,2,3,4,5,6,7,8],['2015','2016','2017','2018','2019','2020','2021','2022','2023'])# 顯示圖表
plt.tight_layout()
plt.show()
相關性分析如下：
dir = {'sale':year_sale_list[5:1:-1],'subsidy':df_subsidy.iloc[:,2:].sum().values}
dir1 = {'sale':year_sale_list[6::-1],'power':pd_power['磷酸鐵鋰動力電池裝機量/GWh'].values
}
df_corr = pd.DataFrame(dir)
df_cor = pd.DataFrame(dir1)
df_cordf_sale = pd.DataFrame(year_sale_list)# 計算補貼金額與銷售量的相關性
correlation_subsidy = df_corr['sale'].corr(df_corr['subsidy'])
correlation_power = df_cor['sale'].corr(df_cor['power'])
# correlation_tech = df_sale['新能源汽車產銷量'].corr(df_tech['每個項目資金支持（萬元）'])# 輸出相關性結果
correlation_subsidy, correlation_power 后續代碼、講解視頻、論文及其他相關內容，可以點擊下面的群名片哦！
