Python股票数据可视化代码详解
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import numpy as np
import pandas as pd
from pandas_datareader import data
import datetime as dt
数据准备
'''
获取国内股票数据的方式是:“股票代码”+“对应股市”(港股为.hk,A股为.ss)
例如腾讯是港股是:0700.hk
'''
#字典:6家公司的股票
# gafataDict={'谷歌':'GOOG','亚马逊':'AMZN','Facebook':'FB', '苹果':'AAPL','阿里巴巴':'BABA','腾讯':'0700.hk'}
'''
定义函数
函数功能:计算股票涨跌幅=(现在股价-买入价格)/买入价格
输入参数:column是收盘价这一列的数据
返回数据:涨跌幅
'''
def change(column):
# 买入价格
buyPrice=column[0]
# 现在股价
curPrice=column[column.size-1]
priceChange=(curPrice-buyPrice)/buyPrice
# 判断股票是上涨还是下跌
if priceChange>0:
print('股票累计上涨=',round(priceChange*100,2),'%')
elif priceChange==0:
print('股票无变化=',round(priceChange*100,2)*100,'%')
else:
print('股票累计下跌=',round(priceChange*100,2)*100,'%')
# 返回数据
return priceChange
'''
三星电子
每日股票价位信息
Open:开盘价
High:最高加
Low:最低价
Close:收盘价
Volume:成交量
因雅虎连接不到,仅以三星作为获取数据示例
'''
sxDf = data.DataReader('005930', 'naver', start='2021-01-01', end='2022-01-01')
sxDf.head()
Open | High | Low | Close | Volume | |
---|---|---|---|---|---|
Date | |||||
2021-01-04 | 81000 | 84400 | 80200 | 83000 | 38655276 |
2021-01-05 | 81600 | 83900 | 81600 | 83900 | 35335669 |
2021-01-06 | 83300 | 84500 | 82100 | 82200 | 42089013 |
2021-01-07 | 82800 | 84200 | 82700 | 82900 | 32644642 |
2021-01-08 | 83300 | 90000 | 83000 | 88800 | 59013307 |
sxDf.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 248 entries, 2021-01-04 to 2021-12-30
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Open 248 non-null object
1 High 248 non-null object
2 Low 248 non-null object
3 Close 248 non-null object
4 Volume 248 non-null object
dtypes: object(5)
memory usage: 11.6+ KB
sxDf.iloc[:,0:4]=sxDf.iloc[:,0:4].astype('float')
sxDf.iloc[:,-1]=sxDf.iloc[:,-1].astype('int')
sxDf.info()
<class 'pandas.core.frame.DataFrame'>DatetimeIndex: 248 entries, 2021-01-04 to 2021-12-30Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Open 248 non-null float64 1 High 248 non-null float64 2 Low 248 non-null float64 3 Close 248 non-null float64 4 Volume 248 non-null int32 dtypes: float64(4), int32(1)memory usage: 10.7 KB<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 248 entries, 2021-01-04 to 2021-12-30
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Open 248 non-null float64
1 High 248 non-null float64
2 Low 248 non-null float64
3 Close 248 non-null float64
4 Volume 248 non-null int32
dtypes: float64(4), int32(1)
memory usage: 10.7 KB
阿里巴巴
# 读取数据
AliDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\学习\Untitled Folder\阿里巴巴2017年股票数据.xlsx',index_col='Date')
AliDf.tail()
Open | High | Low | Close | Adj Close | Volume | |
---|---|---|---|---|---|---|
Date | ||||||
2017-12-22 | 175.839996 | 176.660004 | 175.039993 | 176.289993 | 176.289993 | 12524700 |
2017-12-26 | 174.550003 | 175.149994 | 171.729996 | 172.330002 | 172.330002 | 12913800 |
2017-12-27 | 172.289993 | 173.869995 | 171.729996 | 172.970001 | 172.970001 | 10152300 |
2017-12-28 | 173.039993 | 173.529999 | 171.669998 | 172.300003 | 172.300003 | 9508100 |
2017-12-29 | 172.279999 | 173.669998 | 171.199997 | 172.429993 | 172.429993 | 9704600 |
# 查看基本信息及数据类型
AliDf.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 251 entries, 2017-01-03 to 2017-12-29
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Open 251 non-null float64
1 High 251 non-null float64
2 Low 251 non-null float64
3 Close 251 non-null float64
4 Adj Close 251 non-null float64
5 Volume 251 non-null int64
dtypes: float64(5), int64(1)
memory usage: 13.7 KB
# 计算涨跌幅
AliChange=change(AliDf['Close'])
股票累计上涨= 94.62 %
'''增加一列累计增长百分比'''
#一开始的股价
Close1=AliDf['Close'][0]
# # .apply(lambda x: format(x, '.2%'))
AliDf['sum_pct_change']=AliDf['Close'].apply(lambda x: (x-Close1)/Close1)
AliDf['sum_pct_change'].tail()
Date
2017-12-22 0.989729
2017-12-26 0.945034
2017-12-27 0.952257
2017-12-28 0.944695
2017-12-29 0.946162
Name: sum_pct_change, dtype: float64
谷歌
# 读取数据
GoogleDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\学习\Untitled Folder\谷歌2017年股票数据.xlsx',index_col='Date')
GoogleDf.tail()
Open | High | Low | Close | Adj Close | Volume | |
---|---|---|---|---|---|---|
Date | ||||||
2017-12-22 | 1061.109985 | 1064.199951 | 1059.439941 | 1060.119995 | 1060.119995 | 755100 |
2017-12-26 | 1058.069946 | 1060.119995 | 1050.199951 | 1056.739990 | 1056.739990 | 760600 |
2017-12-27 | 1057.390015 | 1058.369995 | 1048.050049 | 1049.369995 | 1049.369995 | 1271900 |
2017-12-28 | 1051.599976 | 1054.750000 | 1044.770020 | 1048.140015 | 1048.140015 | 837100 |
2017-12-29 | 1046.719971 | 1049.699951 | 1044.900024 | 1046.400024 | 1046.400024 | 887500 |
# 计算涨跌幅
GoogleChange=change(GoogleDf['Close'])
股票累计上涨= 33.11 %
'''增加一列累计增长百分比'''
#一开始的股价
Close1=GoogleDf['Close'][0]
# # .apply(lambda x: format(x, '.2%'))
GoogleDf['sum_pct_change']=GoogleDf['Close'].apply(lambda x: (x-Close1)/Close1)
GoogleDf['sum_pct_change'].tail()
Date
2017-12-22 0.348513
2017-12-26 0.344213
2017-12-27 0.334839
2017-12-28 0.333274
2017-12-29 0.331061
Name: sum_pct_change, dtype: float64
苹果
# 读取数据
AppleDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\学习\Untitled Folder\苹果2017年股票数据.xlsx',index_col='Date')
AppleDf.tail()
Open | High | Low | Close | Adj Close | Volume | |
---|---|---|---|---|---|---|
Date | ||||||
2017-12-22 | 174.679993 | 175.419998 | 174.500000 | 175.009995 | 174.299362 | 16349400 |
2017-12-26 | 170.800003 | 171.470001 | 169.679993 | 170.570007 | 169.877396 | 33185500 |
2017-12-27 | 170.100006 | 170.779999 | 169.710007 | 170.600006 | 169.907272 | 21498200 |
2017-12-28 | 171.000000 | 171.850006 | 170.479996 | 171.080002 | 170.385315 | 16480200 |
2017-12-29 | 170.520004 | 170.589996 | 169.220001 | 169.229996 | 168.542831 | 25999900 |
# 计算涨跌幅
AppleChange=change(AppleDf['Close'])
股票累计上涨= 45.7 %
'''增加一列累计增长百分比'''
#一开始的股价
Close1=AppleDf['Close'][0]
# # .apply(lambda x: format(x, '.2%'))
AppleDf['sum_pct_change']=AppleDf['Close'].apply(lambda x: (x-Close1)/Close1)
AppleDf['sum_pct_change'].tail()
Date
2017-12-22 0.506758
2017-12-26 0.468532
2017-12-27 0.468790
2017-12-28 0.472923
2017-12-29 0.456995
Name: sum_pct_change, dtype: float64
腾讯
# 读取数据
TencentDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\学习\Untitled Folder\腾讯2017年股票数据.xlsx',index_col='Date')
TencentDf.tail()
Open | High | Low | Close | Adj Close | Volume | |
---|---|---|---|---|---|---|
Date | ||||||
2017-12-22 | 403.799988 | 405.799988 | 400.799988 | 405.799988 | 405.799988 | 16146080 |
2017-12-27 | 405.799988 | 407.799988 | 401.000000 | 401.200012 | 401.200012 | 16680601 |
2017-12-28 | 404.000000 | 408.200012 | 402.200012 | 408.200012 | 408.200012 | 11662053 |
2017-12-29 | 408.000000 | 408.000000 | 403.399994 | 406.000000 | 406.000000 | 16601658 |
2018-01-02 | 406.000000 | 406.000000 | 406.000000 | 406.000000 | 406.000000 | 0 |
# 读取数据
TencentDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\学习\Untitled Folder\腾讯2017年股票数据.xlsx',index_col='Date')
TencentDf.tail()
Open | High | Low | Close | Adj Close | Volume | |
---|---|---|---|---|---|---|
Date | ||||||
2017-12-22 | 403.799988 | 405.799988 | 400.799988 | 405.799988 | 405.799988 | 16146080 |
2017-12-27 | 405.799988 | 407.799988 | 401.000000 | 401.200012 | 401.200012 | 16680601 |
2017-12-28 | 404.000000 | 408.200012 | 402.200012 | 408.200012 | 408.200012 | 11662053 |
2017-12-29 | 408.000000 | 408.000000 | 403.399994 | 406.000000 | 406.000000 | 16601658 |
2018-01-02 | 406.000000 | 406.000000 | 406.000000 | 406.000000 | 406.000000 | 0 |
# 计算涨跌幅
TencentChange=change(TencentDf['Close'])
股票累计上涨= 114.36 %
'''增加一列累计增长百分比'''
#一开始的股价
Close1=TencentDf['Close'][0]
# # .apply(lambda x: format(x, '.2%'))
TencentDf['sum_pct_change']=TencentDf['Close'].apply(lambda x: (x-Close1)/Close1)
TencentDf['sum_pct_change'].tail()
Date
2017-12-22 1.142555
2017-12-27 1.118268
2017-12-28 1.155227
2017-12-29 1.143611
2018-01-02 1.143611
Name: sum_pct_change, dtype: float64
亚马逊
# 读取数据
AmazonDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\学习\Untitled Folder\亚马逊2017年股票数据.xlsx',index_col='Date')
AmazonDf.tail()
Open | High | Low | Close | Adj Close | Volume | |
---|---|---|---|---|---|---|
Date | ||||||
2017-12-22 | 1172.079956 | 1174.619995 | 1167.829956 | 1168.359985 | 1168.359985 | 1585100 |
2017-12-26 | 1168.359985 | 1178.319946 | 1160.550049 | 1176.760010 | 1176.760010 | 2005200 |
2017-12-27 | 1179.910034 | 1187.290039 | 1175.609985 | 1182.260010 | 1182.260010 | 1867200 |
2017-12-28 | 1189.000000 | 1190.099976 | 1184.380005 | 1186.099976 | 1186.099976 | 1841700 |
2017-12-29 | 1182.349976 | 1184.000000 | 1167.500000 | 1169.469971 | 1169.469971 | 2688400 |
# 计算涨跌幅
AmazonChange=change(AmazonDf['Close'])
股票累计上涨= 55.17 %
'''增加一列累计增长百分比'''
#一开始的股价
Close1=AmazonDf['Close'][0]
# # .apply(lambda x: format(x, '.2%'))
AmazonDf['sum_pct_change']=AmazonDf['Close'].apply(lambda x: (x-Close1)/Close1)
AmazonDf['sum_pct_change'].tail()
Date
2017-12-22 0.550228
2017-12-26 0.561373
2017-12-27 0.568671
2017-12-28 0.573766
2017-12-29 0.551700
Name: sum_pct_change, dtype: float64
# 读取数据
FacebookDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\学习\Untitled Folder\Facebook2017年股票数据.xlsx',index_col='Date')
FacebookDf.tail()
Open | High | Low | Close | Adj Close | Volume | |
---|---|---|---|---|---|---|
Date | ||||||
2017-12-22 | 177.139999 | 177.529999 | 176.229996 | 177.199997 | 177.199997 | 8509500 |
2017-12-26 | 176.630005 | 177.000000 | 174.669998 | 175.990005 | 175.990005 | 8897300 |
2017-12-27 | 176.550003 | 178.440002 | 176.259995 | 177.619995 | 177.619995 | 9496100 |
2017-12-28 | 177.949997 | 178.940002 | 177.679993 | 177.919998 | 177.919998 | 12220800 |
2017-12-29 | 178.000000 | 178.850006 | 176.460007 | 176.460007 | 176.460007 | 10261500 |
# 计算涨跌幅
FacebookChange=change(FacebookDf['Close'])
股票累计上涨= 51.0 %
'''增加一列每日增长百分比'''
# .pct_change()返回变化百分比,第一行因没有可对比的,返回Nan,填充为0
FacebookDf['pct_change']=FacebookDf['Close'].pct_change(1).fillna(0)
FacebookDf['pct_change'].head()
Date
2017-01-03 0.000000
2017-01-04 0.015660
2017-01-05 0.016682
2017-01-06 0.022707
2017-01-09 0.012074
Name: pct_change, dtype: float64
'''增加一列累计增长百分比'''
#一开始的股价
Close1=FacebookDf['Close'][0]
# .apply(lambda x: format(x, '.2%'))
FacebookDf['sum_pct_change']=FacebookDf['Close'].apply(lambda x: (x-Close1)/Close1)
FacebookDf['sum_pct_change'].tail()
Date
2017-12-22 0.516344
2017-12-26 0.505990
2017-12-27 0.519938
2017-12-28 0.522506
2017-12-29 0.510012
Name: sum_pct_change, dtype: float64
数据可视化
import matplotlib.pyplot as plt
# 查看成交量与股价之间的关系
fig=plt.figure(figsize=(10,5))
AliDf.plot(x='Volume',y='Close',kind='scatter')
plt.xlabel('成交量')
plt.ylabel('股价')
plt.title('成交量与股价之间的关系')
plt.show()
<Figure size 720x360 with 0 Axes>
# 查看各个参数之间的相关性,与股价与成交量之间呈中度相关
AliDf.corr()
Open | High | Low | Close | Adj Close | Volume | sum_pct_change | |
---|---|---|---|---|---|---|---|
Open | 1.000000 | 0.999281 | 0.998798 | 0.998226 | 0.998226 | 0.424686 | 0.998226 |
High | 0.999281 | 1.000000 | 0.998782 | 0.999077 | 0.999077 | 0.432467 | 0.999077 |
Low | 0.998798 | 0.998782 | 1.000000 | 0.999249 | 0.999249 | 0.401456 | 0.999249 |
Close | 0.998226 | 0.999077 | 0.999249 | 1.000000 | 1.000000 | 0.415801 | 1.000000 |
Adj Close | 0.998226 | 0.999077 | 0.999249 | 1.000000 | 1.000000 | 0.415801 | 1.000000 |
Volume | 0.424686 | 0.432467 | 0.401456 | 0.415801 | 0.415801 | 1.000000 | 0.415801 |
sum_pct_change | 0.998226 | 0.999077 | 0.999249 | 1.000000 | 1.000000 | 0.415801 | 1.000000 |
查看各个公司的股价平均值
AliDf['Close'].mean()
141.79179260159364
'''数据准备'''
# 计算每家公司的收盘价平均值
Close_mean={'Alibaba':AliDf['Close'].mean(),
'Google':GoogleDf['Close'].mean(),
'Apple':AppleDf['Close'].mean(),
'Tencent':TencentDf['Close'].mean(),
'Amazon':AmazonDf['Close'].mean(),
'Facebook':FacebookDf['Close'].mean()}
CloseMeanSer=pd.Series(Close_mean)
CloseMeanSer.sort_values(ascending=False,inplace=True)
'''绘制柱状图'''
# 创建画板
fig=plt.figure(figsize=(10,5))
# 绘图
CloseMeanSer.plot(kind='bar')
# 设置x、y轴标签及
plt.xlabel('公司')
plt.ylabel('股价平均值(美元)')
plt.title('2017年各公司股价平均值')
# 设置y周标签刻度
plt.yticks(np.arange(0,1100,100))
# 显示y轴网格
plt.grid(True,axis='y')
# 显示图像
plt.show()
亚马逊和谷歌的平均股价很高,远远超过其他4家,但是仅看平均值并不能代表什么,下面从分布和走势方面查看
查看各公司股价分布情况
'''数据准备'''
# 将6家公司的收盘价整合到一起
CloseCollectDf=pd.concat([AliDf['Close'],
GoogleDf['Close'],
AppleDf['Close'],
TencentDf['Close'],
AmazonDf['Close'],
FacebookDf['Close']],axis=1)
CloseCollectDf.columns=['Alibaba','Google','Apple','Tencent','Amazon','Facebook']
'''绘制箱型图'''
# 创建画板
fig=plt.figure(figsize=(20,10))
fig.suptitle('2017年各公司股价分布',fontsize=18)
# 子图1
ax1=plt.subplot(121)
CloseCollectDf.plot(ax=ax1,kind='box')
plt.xlabel('公司')
plt.ylabel('股价(美元)')
plt.title('2017年各公司股价分布')
plt.grid(True,axis='y')
# 因谷歌和亚马逊和两外四家的差别较大,分开查看,
# 子图2
ax2=plt.subplot(222)
CloseCollectDf[['Google','Amazon']].plot(ax=ax2,kind='box')
# 设置x、y轴标签及
plt.ylabel('股价(美元)')
plt.title('2017年谷歌和亚马逊股价分布')
# 设置y周标签刻度
# plt.yticks(np.arange(0,1300,100))
# 显示y轴网格
plt.grid(True,axis='y')
# 子图3
ax3=plt.subplot(224)
CloseCollectDf[['Alibaba','Apple','Tencent','Facebook']].plot(ax=ax3,kind='box')
# 设置x、y轴标签及
plt.xlabel('公司')
plt.ylabel('股价(美元)')
plt.title('2017年阿里、苹果、腾讯、Facebook股价分布')
# 设置y周标签刻度
# plt.yticks(np.arange(0,1300,100))
# 显示y轴网格
plt.grid(True,axis='y')
plt.subplot
# 显示图像
plt.show()
从箱型图看,谷歌和亚马逊的股价分布较广,且中位数偏上,腾讯股价最为集中,波动最小,相对稳定。
股价走势对比
# 创建画板并设置大小,constrained_layout=True设置自动调整子图之间间距
fig=plt.figure(figsize=(15,10),constrained_layout=True)
# ax=plt.subplots(2,1,sharex=True)
fig.suptitle('股价走势对比',fontsize=18)
'''绘制图像1 '''
ax1=plt.subplot(211)
plt.plot(AliDf.index,AliDf['Close'],label='Alibaba')
plt.plot(GoogleDf.index,GoogleDf['Close'],label='Google')
plt.plot(AppleDf.index,AppleDf['Close'],label='Apple')
plt.plot(TencentDf.index,TencentDf['Close'],label='Tencent')
plt.plot(AmazonDf.index,AmazonDf['Close'],label='Amazon')
plt.plot(FacebookDf.index,FacebookDf['Close'],label='Facebook')
# # 设置xy轴标签
plt.xlabel('时间')
plt.ylabel('股价')
# 设置
# plt.title('股价走势对比')
# 图例显示位置、大小
plt.legend(loc='upper left',fontsize=12)
# 设置x,y轴间隔,设置旋转角度,以免重叠
plt.xticks(AliDf.index[::10],rotation=45)
plt.yticks(np.arange(0, 1300, step=100))
# 显示网格
plt.grid(True)
'''绘制图像2'''
ax2=plt.subplot(212)
plt.plot(AliDf.index,AliDf['sum_pct_change'],label='Alibaba')
plt.plot(GoogleDf.index,GoogleDf['sum_pct_change'],label='Google')
plt.plot(AppleDf.index,AppleDf['sum_pct_change'],label='Apple')
plt.plot(TencentDf.index,TencentDf['sum_pct_change'],label='Tencent')
plt.plot(AmazonDf.index,AmazonDf['sum_pct_change'],label='Amazon')
plt.plot(FacebookDf.index,FacebookDf['sum_pct_change'],label='Facebook')
# 设置xy轴标签
plt.xlabel('时间')
plt.ylabel('累计增长率')
# 设置
# plt.title('股价走势对比')
# 图例显示位置、大小
plt.legend(loc='upper left',fontsize=12)
# 设置x,y轴间隔,设置旋转角度,以免重叠
plt.xticks(AliDf.index[::10],rotation=45)
plt.yticks(np.arange(0, 1.2, step=0.1))
# 显示网格
plt.grid(True)
# 调整子图间距,subplots_adjust(left=None, bottom=None, right=None, top=None,wspace=None, hspace=None)
# 显示图像
plt.show()
可以看出,在2017年间,亚马逊和谷歌的股价虽然偏高,涨幅却不如阿里巴巴和腾讯。
总结
观察以上图形,可以得出一下结果:
1、2017年谷歌和亚马逊股价偏高,波动较大,但其涨幅并不高;
2、2017年阿里巴巴和腾讯的股价平均值相对较小,股价波动比较小,其涨幅却很高,分别达到了94.62%和114.36%。
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