Python 熊猫：将列添加到末尾的多索引数据帧

有人能帮我在多索引数据框中添加一列吗

我有以下多索引数据帧：

                    price     
sym     i_date
MSFT    2017-04-04  100.78    
        2017-04-05  100.03    
        2017-04-06  100.76    
        2017-04-07  100.76    

AAPL    2017-04-04  144.77      
        2017-04-05  144.02
        2017-04-06  143.66
        2017-04-07  143.66

我想在价格列之后添加一列，这是价格的自然对数：

                    price      ln price
sym     i_date
MSFT    2017-04-04  100.78       <ln (100.78)>
        2017-04-05  100.03       <ln (100.03)>
        2017-04-06  100.76       <ln (100.76)>
        2017-04-07  100.76       <ln (100.76)>

AAPL    2017-04-04  144.77       <ln (144.77)>
        2017-04-05  144.02       <ln (144.02)>
        2017-04-06  143.66       <ln (143.66)>
        2017-04-07  143.66       <ln (143.66)>

只需分配lnPrice列，因为它使用Price，所以它将作为分层列添加。对于循环，甚至是

.loc

或

.ix

调用，都不需要

。要演示以下内容，请运行一个
pivot_表
，该表可以重现您的多索引/分层列结构：

import numpy as np
import pandas as pd
from datetime import datetime, timedelta

# MOCK DATA OF 5 U.S. FREIGHT RAILROADS' NORMAL DISTRIBUTION POSITIVE PRICES FOR 10 DAYS
df = pd.DataFrame({'Company': 10*['UNP', 'BNI', 'CSX', 'NSC', 'KSU'],
                   'Date': [datetime(2017, 4, 15) - timedelta(days=i)
                            for i in range(10) for j in range(5)],
                   'Price': abs(np.random.randn(50))})

print(df.head(15))
#    Company       Date     Price
# 0      UNP 2017-04-15  0.229032
# 1      BNI 2017-04-15  0.706309
# 2      CSX 2017-04-15  0.461901
# 3      NSC 2017-04-15  0.710630
# 4      KSU 2017-04-15  0.059535
# 5      UNP 2017-04-14  1.809960
# 6      BNI 2017-04-14  0.842595
# 7      CSX 2017-04-14  1.068346
# 8      NSC 2017-04-14  0.159422
# 9      KSU 2017-04-14  1.537328
# 10     UNP 2017-04-13  0.043753
# 11     BNI 2017-04-13  0.231418
# 12     CSX 2017-04-13  0.739565
# 13     NSC 2017-04-13  1.917282
# 14     KSU 2017-04-13  0.677055

pvtdf = pd.pivot_table(df, index=['Company', 'Date'], values=['Price'], aggfunc=sum)

print(pvtdf.head(15))
#                        Price
# Company Date                
# BNI     2017-04-06  1.422330
#         2017-04-07  0.871719
#         2017-04-08  0.955532
#         2017-04-09  0.990747
#         2017-04-10  0.944047
#         2017-04-11  0.069089
#         2017-04-12  0.707484
#         2017-04-13  1.368786
#         2017-04-14  0.034902
#         2017-04-15  0.462375
# CSX     2017-04-06  0.676962
#         2017-04-07  1.528759
#         2017-04-08  0.038463
#         2017-04-09  0.387486
#         2017-04-10  0.652780

pvtdf['lnPrice'] = np.log(pvtdf['Price'])

print(pvtdf.head(15))
#                        Price   lnPrice
# Company Date                          
# BNI     2017-04-06  1.422330  0.352297
#         2017-04-07  0.871719 -0.137288
#         2017-04-08  0.955532 -0.045487
#         2017-04-09  0.990747 -0.009296
#         2017-04-10  0.944047 -0.057579
#         2017-04-11  0.069089 -2.672364
#         2017-04-12  0.707484 -0.346040
#         2017-04-13  1.368786  0.313924
#         2017-04-14  0.034902 -3.355199
#         2017-04-15  0.462375 -0.771380
# CSX     2017-04-06  0.676962 -0.390141
#         2017-04-07  1.528759  0.424456
#         2017-04-08  0.038463 -3.258052
#         2017-04-09  0.387486 -0.948075
#         2017-04-10  0.652780 -0.426515

您可以将该值设置为：

代码：

df['ln price'] = np.log(df['price'])

df = pd.read_fwf(StringIO(u"""
    sym     i_date      price
    MSFT    2017-04-04  100.78    
    MSFT    2017-04-05  100.03    
    MSFT    2017-04-06  100.76    
    MSFT    2017-04-07  100.76    
    AAPL    2017-04-04  144.77      
    AAPL    2017-04-05  144.02
    AAPL    2017-04-06  143.66
    AAPL    2017-04-07  143.66"""),
                 header=1).set_index(['sym', 'i_date'])

df['ln price'] = np.log(df['price'])

print(df)

                  price  ln price
sym  i_date                      
MSFT 2017-04-04  100.78  4.612940
     2017-04-05  100.03  4.605470
     2017-04-06  100.76  4.612741
     2017-04-07  100.76  4.612741
AAPL 2017-04-04  144.77  4.975146
     2017-04-05  144.02  4.969952
     2017-04-06  143.66  4.967449
     2017-04-07  143.66  4.967449

测试代码：

df['ln price'] = np.log(df['price'])

df = pd.read_fwf(StringIO(u"""
    sym     i_date      price
    MSFT    2017-04-04  100.78    
    MSFT    2017-04-05  100.03    
    MSFT    2017-04-06  100.76    
    MSFT    2017-04-07  100.76    
    AAPL    2017-04-04  144.77      
    AAPL    2017-04-05  144.02
    AAPL    2017-04-06  143.66
    AAPL    2017-04-07  143.66"""),
                 header=1).set_index(['sym', 'i_date'])

df['ln price'] = np.log(df['price'])

print(df)

                  price  ln price
sym  i_date                      
MSFT 2017-04-04  100.78  4.612940
     2017-04-05  100.03  4.605470
     2017-04-06  100.76  4.612741
     2017-04-07  100.76  4.612741
AAPL 2017-04-04  144.77  4.975146
     2017-04-05  144.02  4.969952
     2017-04-06  143.66  4.967449
     2017-04-07  143.66  4.967449

结果：

df['ln price'] = np.log(df['price'])

df = pd.read_fwf(StringIO(u"""
    sym     i_date      price
    MSFT    2017-04-04  100.78    
    MSFT    2017-04-05  100.03    
    MSFT    2017-04-06  100.76    
    MSFT    2017-04-07  100.76    
    AAPL    2017-04-04  144.77      
    AAPL    2017-04-05  144.02
    AAPL    2017-04-06  143.66
    AAPL    2017-04-07  143.66"""),
                 header=1).set_index(['sym', 'i_date'])

df['ln price'] = np.log(df['price'])

print(df)

                  price  ln price
sym  i_date                      
MSFT 2017-04-04  100.78  4.612940
     2017-04-05  100.03  4.605470
     2017-04-06  100.76  4.612741
     2017-04-07  100.76  4.612741
AAPL 2017-04-04  144.77  4.975146
     2017-04-05  144.02  4.969952
     2017-04-06  143.66  4.967449
     2017-04-07  143.66  4.967449

谢谢。如果我想添加另一个只需要使用该股票/公司价格的列（比如移动平均线），我该如何添加它？我想出来了。我不得不使用groupby['stk_sym']，而没有多重索引。谢谢。如果我想添加另一个只需要使用该股票/公司价格的列（比如移动平均线），我该如何添加它？我想出来了。我不得不在没有多重索引的情况下使用groupby['stk_sym']。