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Python可视化

惜木兮 人气:0
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()
 OpenHighLowCloseVolume
Date     
2021-01-048100084400802008300038655276
2021-01-058160083900816008390035335669
2021-01-068330084500821008220042089013
2021-01-078280084200827008290032644642
2021-01-088330090000830008880059013307
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()
 OpenHighLowCloseAdj CloseVolume
Date      
2017-12-22175.839996176.660004175.039993176.289993176.28999312524700
2017-12-26174.550003175.149994171.729996172.330002172.33000212913800
2017-12-27172.289993173.869995171.729996172.970001172.97000110152300
2017-12-28173.039993173.529999171.669998172.300003172.3000039508100
2017-12-29172.279999173.669998171.199997172.429993172.4299939704600
# 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()
 OpenHighLowCloseAdj CloseVolume
Date      
2017-12-221061.1099851064.1999511059.4399411060.1199951060.119995755100
2017-12-261058.0699461060.1199951050.1999511056.7399901056.739990760600
2017-12-271057.3900151058.3699951048.0500491049.3699951049.3699951271900
2017-12-281051.5999761054.7500001044.7700201048.1400151048.140015837100
2017-12-291046.7199711049.6999511044.9000241046.4000241046.400024887500
# 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()
OpenHighLowCloseAdj CloseVolume
Date
2017-12-22174.679993175.419998174.500000175.009995174.29936216349400
2017-12-26170.800003171.470001169.679993170.570007169.87739633185500
2017-12-27170.100006170.779999169.710007170.600006169.90727221498200
2017-12-28171.000000171.850006170.479996171.080002170.38531516480200
2017-12-29170.520004170.589996169.220001169.229996168.54283125999900
# 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()
 OpenHighLowCloseAdj CloseVolume
Date      
2017-12-22403.799988405.799988400.799988405.799988405.79998816146080
2017-12-27405.799988407.799988401.000000401.200012401.20001216680601
2017-12-28404.000000408.200012402.200012408.200012408.20001211662053
2017-12-29408.000000408.000000403.399994406.000000406.00000016601658
2018-01-02406.000000406.000000406.000000406.000000406.0000000
# 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()
 OpenHighLowCloseAdj CloseVolume
Date      
2017-12-22403.799988405.799988400.799988405.799988405.79998816146080
2017-12-27405.799988407.799988401.000000401.200012401.20001216680601
2017-12-28404.000000408.200012402.200012408.200012408.20001211662053
2017-12-29408.000000408.000000403.399994406.000000406.00000016601658
2018-01-02406.000000406.000000406.000000406.000000406.0000000
# 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()
 OpenHighLowCloseAdj CloseVolume
Date      
2017-12-221172.0799561174.6199951167.8299561168.3599851168.3599851585100
2017-12-261168.3599851178.3199461160.5500491176.7600101176.7600102005200
2017-12-271179.9100341187.2900391175.6099851182.2600101182.2600101867200
2017-12-281189.0000001190.0999761184.3800051186.0999761186.0999761841700
2017-12-291182.3499761184.0000001167.5000001169.4699711169.4699712688400
# 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

Facebook

# 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()
 OpenHighLowCloseAdj CloseVolume
Date      
2017-12-22177.139999177.529999176.229996177.199997177.1999978509500
2017-12-26176.630005177.000000174.669998175.990005175.9900058897300
2017-12-27176.550003178.440002176.259995177.619995177.6199959496100
2017-12-28177.949997178.940002177.679993177.919998177.91999812220800
2017-12-29178.000000178.850006176.460007176.460007176.46000710261500
# 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>

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# 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()
 OpenHighLowCloseAdj CloseVolumesum_pct_change
Open1.0000000.9992810.9987980.9982260.9982260.4246860.998226
High0.9992811.0000000.9987820.9990770.9990770.4324670.999077
Low0.9987980.9987821.0000000.9992490.9992490.4014560.999249
Close0.9982260.9990770.9992491.0000001.0000000.4158011.000000
Adj Close0.9982260.9990770.9992491.0000001.0000000.4158011.000000
Volume0.4246860.4324670.4014560.4158010.4158011.0000000.415801
sum_pct_change0.9982260.9990770.9992491.0000001.0000000.4158011.000000

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轴标签及标题
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()

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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轴标签及标题
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轴标签及标题
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()

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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')
# 设置标题
# 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')
# 设置标题
# 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()

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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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