Question

我有大量股票的以下数据集，并使用以下公式计算变化百分比df7['Change']=(df7.Close.pct_change())*100

但是，我想如何修改这个公式，或者写一个新的公式，在第一次股票出现在数据框中时将更改为NaN。

因此，在下面的示例中，AAPL第一次出现在数据中的变化并不是说-83％

      Date        Open        High         Low       Close    Volume  \
 2015-11-02  711.059998  721.619995  705.849976  721.109985   1871100   
 2015-11-03  718.859985  724.650024  714.719971  722.159973   1560800   
 2015-11-04  722.000000  733.099976  721.900024  728.109985   1704600   
 2015-11-05  729.469971  739.479980  729.469971  731.250000   1860400   
 2015-11-06  731.500000  735.409973  727.010010  733.760010   1509600   
 2015-11-09  730.200012  734.710022  719.429993  724.890015   2065600   
 2015-11-10  724.400024  730.590027  718.500000  728.320007   1603900   
 2015-11-02  120.800003  121.360001  119.610001  121.180000  32203300   
 2015-11-03  120.790001  123.489998  120.699997  122.570000  45519000   
 2015-11-04  123.129997  123.820000  121.620003  122.000000  44886100   
 2015-11-05  121.849998  122.690002  120.180000  120.919998  39552700   
 2015-11-06  121.110001  121.809998  120.620003  121.059998  33042300   
 2015-11-09  120.959999  121.809998  120.050003  120.570000  33871400   
 2015-11-10  116.900002  118.070000  116.059998  116.769997  59127900   

   Adj Close  Symbol    Change  
  721.109985   GOOG        NaN  
  722.159973   GOOG   0.145607  
  728.109985   GOOG   0.823919  
  731.250000   GOOG   0.431256  
  733.760010   GOOG   0.343249  
  724.890015   GOOG  -1.208841  
  728.320007   GOOG   0.473174  
  120.663499   AAPL -83.361709  
  122.047573   AAPL   1.147054  
  121.480003   AAPL  -0.465040  
  120.919998   AAPL  -0.885248  
  121.059998   AAPL   0.115779  
  120.570000   AAPL  -0.404756  
  116.769997   AAPL  -3.151699

我使用以下代码生成此信息：

import pandas as pd
from pandas.io.data import DataReader
from datetime import datetime

df7 = pd.DataFrame()
symbols = ['GOOG','AAPL']

for symbol in symbols:
    ClosingPrice = DataReader(symbol, 'yahoo', datetime(2015,11,1), datetime(2015,11,10))
    ClosingPrice = ClosingPrice.reset_index()
    ClosingPrice['Symbol'] = symbol
    df7 = df7.append(ClosingPrice)

任何帮助将不胜感激。谢谢！

Answer 1

您可以使用groupby方法：

df['Change'] = df.groupby('Symbol').Close.pct_change()

In [20]: df
Out[20]:
                  Open        High         Low       Close    Volume  \
Date
2015-11-02  711.059998  721.619995  705.849976  721.109985   1871100
2015-11-03  718.859985  724.650024  714.719971  722.159973   1560800
2015-11-04  722.000000  733.099976  721.900024  728.109985   1704600
2015-11-05  729.469971  739.479980  729.469971  731.250000   1860400
2015-11-06  731.500000  735.409973  727.010010  733.760010   1509600
2015-11-09  730.200012  734.710022  719.429993  724.890015   2065600
2015-11-10  724.400024  730.590027  718.500000  728.320007   1603900
2015-11-02  120.800003  121.360001  119.610001  121.180000  32203300
2015-11-03  120.790001  123.489998  120.699997  122.570000  45519000
2015-11-04  123.129997  123.820000  121.620003  122.000000  44886100
2015-11-05  121.849998  122.690002  120.180000  120.919998  39552700
2015-11-06  121.110001  121.809998  120.620003  121.059998  33042300
2015-11-09  120.959999  121.809998  120.050003  120.570000  33871400
2015-11-10  116.900002  118.070000  116.059998  116.769997  59127900

             Adj_Close Symbol    Change
Date
2015-11-02  721.109985   GOOG       NaN
2015-11-03  722.159973   GOOG  0.001456
2015-11-04  728.109985   GOOG  0.008239
2015-11-05  731.250000   GOOG  0.004313
2015-11-06  733.760010   GOOG  0.003432
2015-11-09  724.890015   GOOG -0.012088
2015-11-10  728.320007   GOOG  0.004732
2015-11-02  120.663499   AAPL       NaN
2015-11-03  122.047573   AAPL  0.011471
2015-11-04  121.480003   AAPL -0.004650
2015-11-05  120.919998   AAPL -0.008852
2015-11-06  121.059998   AAPL  0.001158
2015-11-09  120.570000   AAPL -0.004048
2015-11-10  116.769997   AAPL -0.031517

Python（Pandas）计算变化百分比

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