假设我有一个 Pandas DataFrame df:
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 Value=1 以来的天数。
所以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，它似乎效率很低，而且可能也不对。
请您参考如下方法:
这是一个 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
在数组数据上运行的示例很少，以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]])
用它来解决我们的问题:
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 -
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
