Question

我有一个熊猫数据框，其股价数据如下所示：

      ticker       date    open    high     low   close      volume
0        A2M 2015-03-31   0.555   0.595   0.530   0.565   4816294.0
1        A2M 2015-04-30   0.475   0.500   0.475   0.500    531816.0
2        A2M 2015-05-29   0.475   0.475   0.455   0.465   5665854.0
3        A2M 2015-06-30   0.640   0.650   0.630   0.640   1691918.0
4        A2M 2015-07-31   0.750   0.760   0.730   0.735    714927.0
...      ...        ...     ...     ...     ...     ...         ...
45479    ZFX 2008-01-31  10.090  10.490   9.860  10.280   4484500.0
45480    ZFX 2008-02-29  10.650  11.130  10.650  11.130  15525073.0
45481    ZFX 2008-03-31  10.010  10.080   9.920   9.980   4256951.0
45482    ZFX 2008-04-30   9.900  10.190   9.850  10.100   3522569.0
45483    ZFX 2008-05-30   9.750   9.750   9.450   9.500   8270995.0

我的目标是在数据框中包含3、6、9、12个月变化率的列。我开发了以下功能：

#defines the ROC function
def roc (df, roc_periods):
    roc = df['close'] / df['close'].shift(roc_periods) - 1
    return pd.DataFrame(roc)

#defines the periods for the ROC calculations
def roc_periods(df, months):
    for month in months:
        df['{}mo_roc'.format(month)] = roc(df, month)
    return df

#specify the roc periods to calculate
periods = roc_periods(monthly_raw_data, [3, 6, 9, 12])

输出数据帧如下：

      ticker       date    open    high     low   close      volume   3mo_roc  \
0        A2M 2015-03-31   0.555   0.595   0.530   0.565   4816294.0       NaN   
1        A2M 2015-04-30   0.475   0.500   0.475   0.500    531816.0       NaN   
2        A2M 2015-05-29   0.475   0.475   0.455   0.465   5665854.0       NaN   
3        A2M 2015-06-30   0.640   0.650   0.630   0.640   1691918.0  0.132743   
4        A2M 2015-07-31   0.750   0.760   0.730   0.735    714927.0  0.470000   
...      ...        ...     ...     ...     ...     ...         ...       ...   
45479    ZFX 2008-01-31  10.090  10.490   9.860  10.280   4484500.0 -0.382583   
45480    ZFX 2008-02-29  10.650  11.130  10.650  11.130  15525073.0 -0.229224   
45481    ZFX 2008-03-31  10.010  10.080   9.920   9.980   4256951.0 -0.195161   
45482    ZFX 2008-04-30   9.900  10.190   9.850  10.100   3522569.0 -0.017510   
45483    ZFX 2008-05-30   9.750   9.750   9.450   9.500   8270995.0 -0.146451   

        6mo_roc   9mo_roc  12mo_roc  
0           NaN       NaN       NaN  
1           NaN       NaN       NaN  
2           NaN       NaN       NaN  
3           NaN       NaN       NaN  
4           NaN       NaN       NaN  
...         ...       ...       ...  
45479 -0.483677 -0.378852 -0.373171  
45480 -0.340640 -0.367614 -0.334330  
45481 -0.436795 -0.469713 -0.367554  
45482 -0.393393 -0.492717 -0.389728  
45483 -0.342105 -0.437204 -0.460227

问题是我似乎无法使.groupby（）方法正常工作。结果，变化率列会像滚动条一样连续滚动，而不是为每个滚动条计算。我试图在整个代码中放置.groupby()方法，但是收到KeyError: 'ticker'消息。出于询问的目的-我一起删除了对groupby的尝试。

Answer 1

您可以将参数传递给groupby之后应用的函数。只需更改roc_periods即可使用它：

#defines the periods for the ROC calculations
def roc_periods(df, months):
    for month in months:
        df['{}mo_roc'.format(month)] = df.groupby('ticker').apply(roc, month)
    return df

熊猫数据框按功能分组

1 个答案: