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我已经检查了文档。我不明白索引 Pandas DataFrame 的方法。

我想将股票价格的 DataFrame 除以它们各自的初始值，以将不同的股票索引为 100。我想比较它们的表现。DataFrame 如下所示：

>>> IndexPrices
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 157 entries, 1999-12-31 00:00:00 to 2012-12-31 00:00:00
Freq: M
Data columns:
MSCI WORLD :G U$                        148  non-null values
S&P 500 COMPOSITE                       148  non-null values
DAX 30 PERFORMANCE                      148  non-null values
RUSSELL 2000                            148  non-null values
FTSE 100                                148  non-null values
US Treasury Bond Yields 30 Year Bond    148  non-null values
dtypes: float64(6)

我有这样的东西，但它没有让我到任何地方。

IndexPrices.divide(IndexPrices[0:1])
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2 回答 2

18 
In [193]: df
Out[193]:
   A  B  C  D
a  1  8  9  1
b  5  4  3  6
c  4  6  1  3
d  1  0  2  9

In [194]: df.divide(df.ix[0] / 100)
Out[194]:
     A    B           C    D
a  100  100  100.000000  100
b  500   50   33.333333  600
c  400   75   11.111111  300
d  100    0   22.222222  900
于 2012-08-17T14:20:05.537 回答
10

对于新版本的熊猫

In [1]: df
Out[1]:
                   SPY      Google        Gold        Xom
Date
2008-12-31   90.239998  153.250580   86.519997  79.830002
2009-01-02   92.959999  160.060059   86.230003  81.639999
2009-01-05   92.849998  163.412491   84.480003  81.629997
2009-01-06   93.470001  166.406265   85.129997  80.300003
2009-01-07   90.669998  160.403763   82.750000  78.250000
...                ...         ...         ...        ...
2012-12-24  142.350006  353.425262  160.619995  86.919998
2012-12-26  141.750000  353.111450  160.779999  87.070000
2012-12-27  141.559998  351.826263  161.160004  86.860001
2012-12-28  140.029999  348.697998  160.539993  85.099998
2012-12-31  142.410004  352.369232  162.020004  86.550003

[1007 rows x 4 columns]

In [2]: df.iloc[0]
Out[2]:
SPY        90.239998
Google    153.250580
Gold       86.519997
Xom        79.830002
Name: 2008-12-31 00:00:00, dtype: float64

In [3]: df.div(df.iloc[0])
Out[3]:
                 SPY    Google      Gold       Xom
Date
2008-12-31  1.000000  1.000000  1.000000  1.000000
2009-01-02  1.030142  1.044434  0.996648  1.022673
2009-01-05  1.028923  1.066309  0.976422  1.022548
2009-01-06  1.035793  1.085844  0.983934  1.005888
2009-01-07  1.004765  1.046676  0.956426  0.980208
...              ...       ...       ...       ...
2012-12-24  1.577460  2.306192  1.856449  1.088814
2012-12-26  1.570811  2.304144  1.858299  1.090693
2012-12-27  1.568706  2.295758  1.862691  1.088062
2012-12-28  1.551751  2.275345  1.855525  1.066015
2012-12-31  1.578125  2.299301  1.872631  1.084179

[1007 rows x 4 columns]
于 2020-09-07T17:28:51.857 回答