在Python中进行了一些更改后复制行

Question

我遇到了一个如何在Python中使用循环函数复制行的问题。我有一个这样的数据集(这是一个熊猫数据框)：

    userId  period  Date    
0   41851   4       1/4/2015    
1   13575   1       1/4/2015    

我想复制第一行3次，每次对于原始行，period列需要减1。直到原始行的period是1。而且每次当我复制它时，我想在日期上添加1个月。所以结果会是这样的：

    userId  period  Date    
0   41851   1       1/4/2015
1   41851   1       2/4/2015
2   41851   1       3/4/2015
3   41851   1       4/4/2015    
4   13575   1       1/4/2015    

有人知道怎么做吗？谢谢!

Answer 1

想法是用Index.repeat和DataFrame.loc重复行，然后用this solution按GroupBy.cumcount添加日期，最后用Series.dt.strftime更改日期时间的格式

def combine64(years, months=1, days=1, weeks=None, hours=None, minutes=None,
              seconds=None, milliseconds=None, microseconds=None, nanoseconds=None):
    years = np.asarray(years) - 1970
    months = np.asarray(months) - 1
    days = np.asarray(days) - 1
    types = ('<M8[Y]', '<m8[M]', '<m8[D]', '<m8[W]', '<m8[h]',
             '<m8[m]', '<m8[s]', '<m8[ms]', '<m8[us]', '<m8[ns]')
    vals = (years, months, days, weeks, hours, minutes, seconds,
            milliseconds, microseconds, nanoseconds)
    return sum(np.asarray(v, dtype=t) for t, v in zip(types, vals)
               if v is not None)

def year(dates):
    "Return an array of the years given an array of datetime64s"
    return dates.astype('M8[Y]').astype('i8') + 1970

def month(dates):
    "Return an array of the months given an array of datetime64s"
    return dates.astype('M8[M]').astype('i8') % 12 + 1

def day(dates):
    "Return an array of the days of the month given an array of datetime64s"
    return (dates - dates.astype('M8[M]')) / np.timedelta64(1, 'D') + 1

df['Date'] = pd.to_datetime(df['Date'])

df1 = df.loc[df.index.repeat(df['period'])]
g = df1.groupby(level=0).cumcount()

start = df1['Date'].values
df1['Date'] = combine64(year(start), months=month(start) + g, 
                           days=day(start))
df1['period'] = 1
df1 = df1.reset_index(drop=True)

df1['Date'] = df1['Date'].dt.strftime('%m/%d/%Y')
print (df1)
   userId  period        Date
0   41851       1  01/04/2015
1   41851       1  02/04/2015
2   41851       1  03/04/2015
3   41851       1  04/04/2015
4   13575       1  01/04/2015