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
但是对于极大的数据集来说它似乎非常低效，而且可能无论如何都是不对的。
最佳答案
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
很少有示例在数组数据上运行以展示覆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]])
使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——
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
关于python - 从 Pandas DataFrame 上次出现以来的天数？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/44420846/