get_data_yahoos - 熊猫数据阅读器

Question

我一直在尝试使用 Pandas Data Reader 导入 Tesla 的财务数据，但出现错误。

import pandas
from pandas_datareader import data as pdr

pdr.get_data_yahoo('TSLR')

错误：AttributeError：“NoneType”对象没有属性“group”

第 2 步：

后来我尝试使用yfinance，我又遇到了一个关于缺少方法的错误

我使用了这个代码：

import pandas
from pandas_datareader import data as pdr
import yfinance as yfin
yfin.pdr_override()

错误：属性错误：模块“yfinance”没有属性“pdr_override”

进一步的步骤：我已经安装了所有库并且它们已更新。

Answer 1

你要么这样做：

import yfinance as yf

data = yf.download('TSLA', start='2020-01-01', end='2024-01-01')
print(data)

给你

[*********************100%***********************]  1 of 1 completed
                  Open        High         Low       Close   Adj Close  \
Date                                                                     
2020-01-02   28.299999   28.713333   28.114000   28.684000   28.684000   
2020-01-03   29.366667   30.266666   29.128000   29.534000   29.534000   
2020-01-06   29.364668   30.104000   29.333332   30.102667   30.102667   
2020-01-07   30.760000   31.441999   30.224001   31.270666   31.270666   
2020-01-08   31.580000   33.232666   31.215334   32.809334   32.809334   
...                ...         ...         ...         ...         ...   
2023-12-22  256.760010  258.220001  251.369995  252.539993  252.539993   
2023-12-26  254.490005  257.970001  252.910004  256.609985  256.609985   
2023-12-27  258.350006  263.339996  257.519989  261.440002  261.440002   
2023-12-28  263.660004  265.130005  252.710007  253.179993  253.179993   
2023-12-29  255.100006  255.190002  247.429993  248.479996  248.479996   

               Volume  
Date                   
2020-01-02  142981500  
2020-01-03  266677500  
2020-01-06  151995000  
2020-01-07  268231500  
2020-01-08  467164500  
...               ...  
2023-12-22   93249800  
2023-12-26   86892400  
2023-12-27  106494400  
2023-12-28  113619900  
2023-12-29  100615300  

[1006 rows x 6 columns]

或者这个：

import yfinance as yf
import pandas_datareader.data as pdr

yf_obj = yf.Ticker('TSLA')
data = yf_obj.history(start='2020-01-01', end='2024-01-01')

data = data[['Open', 'High', 'Low', 'Close', 'Volume']]
data.columns = ['Open', 'High', 'Low', 'Close', 'Volume']

print(data)

这给了你

                                 Open        High         Low       Close  \
Date                                                                        
2020-01-02 00:00:00-05:00   28.299999   28.713333   28.114000   28.684000   
2020-01-03 00:00:00-05:00   29.366667   30.266666   29.128000   29.534000   
2020-01-06 00:00:00-05:00   29.364668   30.104000   29.333332   30.102667   
2020-01-07 00:00:00-05:00   30.760000   31.441999   30.224001   31.270666   
2020-01-08 00:00:00-05:00   31.580000   33.232666   31.215334   32.809334   
...                               ...         ...         ...         ...   
2023-12-22 00:00:00-05:00  256.760010  258.220001  251.369995  252.539993   
2023-12-26 00:00:00-05:00  254.490005  257.970001  252.910004  256.609985   
2023-12-27 00:00:00-05:00  258.350006  263.339996  257.519989  261.440002   
2023-12-28 00:00:00-05:00  263.660004  265.130005  252.710007  253.179993   
2023-12-29 00:00:00-05:00  255.100006  255.190002  247.429993  248.479996   

                              Volume  
Date                                  
2020-01-02 00:00:00-05:00  142981500  
2020-01-03 00:00:00-05:00  266677500  
2020-01-06 00:00:00-05:00  151995000  
2020-01-07 00:00:00-05:00  268231500  
2020-01-08 00:00:00-05:00  467164500  
...                              ...  
2023-12-22 00:00:00-05:00   93249800  
2023-12-26 00:00:00-05:00   86892400  
2023-12-27 00:00:00-05:00  106494400  
2023-12-28 00:00:00-05:00  113619900  
2023-12-29 00:00:00-05:00  100615300  

[1006 rows x 5 columns]