Question

我有下面的数据框，它有大约200家公司的几个股票价值，我正试图找到一种方法来循环并建立一个新的数据框架，其中包括这些公司＆＃39;不同的年度特征

Date       Symbol    Open   High    Low     Close   Volume      Daily Return
2016-01-04  AAPL    102.61  105.37  102.00  105.35  67281190    0.025703
2016-01-05  AAPL    105.75  105.85  102.41  102.71  55790992    0.019960
2016-12-28  AMZN    776.25  780.00  770.50  772.13  3301025     0.009122
2016-12-29  AMZN    772.40  773.40  760.85  765.15  3158299     0.020377

我尝试过不同的方式，我最接近的是：

stocks_features = pd.DataFrame(data=stocks_data.Symbol.unique(), columns = ['Symbol'])
stocks_features['Max_Yearly_Price'] = stocks_data['High'].max()
stocks_features['Min_Yearly_Price'] = stocks_data['Low'].min()
stocks_features

但它给了我所有股票的相同价值：

Symbol  Max_Yearly_Price    Min_Yearly_Price
AAPL         847.21            89.47
AMZN         847.21            89.47

我做错了什么，我怎么能做到这一点？

Answer 1

使用groupby agg

df.groupby('Symbol').agg({'High':'max','Low':'min'}).\
    rename(columns={'High':'Max_Yearly_Price','Low':'Min_Yearly_Price'})
Out[861]: 
        Max_Yearly_Price  Min_Yearly_Price
Symbol                                    
AAPL              105.85            102.00
AMZN              780.00            760.85

Answer 2

温的回答也很棒。我有一种不同的解决方法。我会随着时间解释：

# creates a dictionary of all the symbols and their max values

value_maps = dict(stocks_features.loc[stocks_features.\
groupby('Symbol').High.agg('idxmax')][['Symbol', 'High']].values)

# sets Max_Yearly_Price equal to the symbol

stocks_features['Max_Yearly_Price'] = stocks_features['Symbol']

# replaces the symbol wiht the corresponding value from the dicitonary
stocks_features['Max_Yearly_Price'] = stocks_features['Max_Yearly_Price'].map(value_maps)

# ouput

    Date    Symbol  Open    High    Low Close   Volume  Daily   Return  Max_Yearly_Price
0   2016-01-04  AAPL    102.61  105.37  102.00  105.35  672811900.025703 NaN    105.85
1   2016-01-05  AAPL    105.75  105.85  102.41  102.71  557909920.019960 NaN    105.85
2   2016-12-28  AMZN    776.25  780.00  770.50  772.13  33010250.009122  NaN    780.00
3   2016-12-29  AMZN    772.40  773.40  760.85  765.15  31582990.020377  NaN    780.00

如何在Pandas DataFrame中循环使用唯一的股票代码值？

2 个答案: