获取自 Pandas DataFrame 中上次出现以来的天数？

假设我有一个 Pandas DataFramedf:

Date      Value
01/01/17  0
01/02/17  0
01/03/17  1
01/04/17  0
01/05/17  0
01/06/17  0
01/07/17  1
01/08/17  0
01/09/17  0

对于每一行，我想有效地计算自上次出现以来的天数Value=1.

So that df:

Date      Value    Last_Occurence
01/01/17  0        NaN
01/02/17  0        NaN
01/03/17  1        0
01/04/17  0        1
01/05/17  0        2
01/06/17  0        3
01/07/17  1        0
01/08/17  0        1
01/09/17  0        2

我可以做一个循环：

for i in range(0, len(df)):
    last = np.where(df.loc[0:i,'Value']==1)
    df.loc[i, 'Last_Occurence'] = i-last

但对于非常大的数据集来说，它似乎效率非常低，并且可能无论如何都是不正确的。

这是 NumPy 方法 -

def intervaled_cumsum(a, trigger_val=1, start_val = 0, invalid_specifier=-1):
    out = np.ones(a.size,dtype=int)    
    idx = np.flatnonzero(a==trigger_val)
    if len(idx)==0:
        return np.full(a.size,invalid_specifier)
    else:
        out[idx[0]] = -idx[0] + 1
        out[0] = start_val
        out[idx[1:]] = idx[:-1] - idx[1:] + 1
        np.cumsum(out, out=out)
        out[:idx[0]] = invalid_specifier
        return out

很少有示例在数组数据上运行来展示涵盖触发器和起始值的各种场景的用法：

In [120]: a
Out[120]: array([0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0])

In [121]: p1 = intervaled_cumsum(a, trigger_val=1, start_val=0)
     ...: p2 = intervaled_cumsum(a, trigger_val=1, start_val=1)
     ...: p3 = intervaled_cumsum(a, trigger_val=0, start_val=0)
     ...: p4 = intervaled_cumsum(a, trigger_val=0, start_val=1)
     ...: 

In [122]: np.vstack(( a, p1, p2, p3, p4 ))
Out[122]: 
array([[ 0,  1,  1,  1,  0,  0,  1,  0,  0,  1,  1,  1,  1,  1,  0],
       [-1,  0,  0,  0,  1,  2,  0,  1,  2,  0,  0,  0,  0,  0,  1],
       [-1,  1,  1,  1,  2,  3,  1,  2,  3,  1,  1,  1,  1,  1,  2],
       [ 0,  1,  2,  3,  0,  0,  1,  0,  0,  1,  2,  3,  4,  5,  0],
       [ 1,  2,  3,  4,  1,  1,  2,  1,  1,  2,  3,  4,  5,  6,  1]])

用它来解决我们的案例：

df['Last_Occurence'] = intervaled_cumsum(df.Value.values)

示例输出 -

In [181]: df
Out[181]: 
       Date  Value  Last_Occurence
0  01/01/17      0              -1
1  01/02/17      0              -1
2  01/03/17      1               0
3  01/04/17      0               1
4  01/05/17      0               2
5  01/06/17      0               3
6  01/07/17      1               0
7  01/08/17      0               1
8  01/09/17      0               2

运行时测试

方法 -

# @Scott Boston's soln
def pandas_groupby(df):
    mask = df.Value.cumsum().replace(0,False).astype(bool)
    return df.assign(Last_Occurance=df.groupby(df.Value.astype(bool).\
                                    cumsum()).cumcount().where(mask))

# Proposed in this post
def numpy_based(df):
    df['Last_Occurence'] = intervaled_cumsum(df.Value.values)

时间安排 -

In [33]: df = pd.DataFrame((np.random.rand(10000000)>0.7).astype(int), columns=[['Value']])

In [34]: %timeit pandas_groupby(df)
1 loops, best of 3: 1.06 s per loop

In [35]: %timeit numpy_based(df)
10 loops, best of 3: 103 ms per loop

In [36]: df = pd.DataFrame((np.random.rand(100000000)>0.7).astype(int), columns=[['Value']])

In [37]: %timeit pandas_groupby(df)
1 loops, best of 3: 11.1 s per loop

In [38]: %timeit numpy_based(df)
1 loops, best of 3: 1.03 s per loop