个人数据分析学习公众号：genshang
1import matplotlib.pyplot as plt 2import seaborn as sns 3sns.set_theme(style='whitegrid',palette='deep')
1# 读取标普500指数和富时100指数数据 2df_spx500_and_ftse100 = pd.read_csv('line_chart_data.csv') 3# 预览数据样式 4df_spx500_and_ftse100.head(5)
1# 此时的Date列为字符串，由于下面需要进行日期比较，所以需要将字符格式转为datetime格式 2# 数据处理：字符 >> 日期格式(通过pd.to_datetime()实现) 3df_spx500_and_ftse100['Date'] = pd.to_datetime(df_spx500_and_ftse100['Date']) 4# 预览格式转换后的结果 5df_spx500_and_ftse100.head(5)
1# 设置过滤条件：只显示2010年的指数数据 2year_2010 = (df_spx500_and_ftse100['Date']>='2010-01-01') & (df_spx500_and_ftse100['Date']<='2010-12-31') 3# 生成仅包含2010年数据的新的DataFrame 4df_spx500_and_ftse100_in_2010 = df_spx500_and_ftse100[year_2010] 5# 保证日期升序没错 6df_spx500_and_ftse100_in_2010.sort_values(by='Date',inplace=True) 7# 预览效果 8df_spx500_and_ftse100_in_2010.head(10)
1legend_labels = ['GSPC500','FTSE100'] 2plt.figure(figsize=(20,10)) 3# 分别绘制两条线 4plt.plot(df_spx500_and_ftse100_in_2010['Date'],df_spx500_and_ftse100_in_2010['GSPC500']) 5plt.plot(df_spx500_and_ftse100_in_2010['Date'],df_spx500_and_ftse100_in_2010['FTSE100']) 6# 设置标题 7plt.title('S&P 500 & FTSE 100',fontsize=24, fontweight = 'bold') 8# 设置图例 9plt.legend(labels = legend_labels, loc='best', fontsize=18) 10plt.show()
1import datetime 2from dateutil.relativedelta import relativedelta 3# 生成X轴刻度：每月1号组成的list 4month_1_list = [datetime.date(2010,1,1)] 5for i in range(1,12): 6 month_1_list.append(datetime.date(2010,1,1) + relativedelta(months=+i)) 7month_1_list.append(datetime.date(2010,12,31)) 8# ------------------------------------------------- 9legend_labels = ['GSPC500','FTSE100'] 10plt.figure(figsize=(20,10)) 11plt.plot(df_spx500_and_ftse100_in_2010['Date'],df_spx500_and_ftse100_in_2010['GSPC500'],linewidth=2) 12plt.plot(df_spx500_and_ftse100_in_2010['Date'],df_spx500_and_ftse100_in_2010['FTSE100'],linewidth=2) 13# 自定义X轴刻度：为每月月初(若未特殊指定，则会默认生成轴刻度) 14plt.xticks(ticks=month_1_list,rotation=40,fontsize=14) 15plt.yticks(fontsize=14) 16plt.title('S&P 500 & FTSE 100',fontsize=24, fontweight = 'bold') 17plt.legend(labels = legend_labels, loc='best', fontsize=16) 18plt.show()
