这是基于卷积的方法，使用np.convolve-
mask = np.isnan(data)
K = np.ones(win_size,dtype=int)
out = np.convolve(np.where(mask,0,data), K)/np.convolve(~mask,K)
如果您正在处理2D数据，我们可以使用Scipy's 2D convolution。
方法-
def original_app(data, win_size):
#Compute mean
result = np.zeros(data.size)
for count in range(data.size):
part_data = data[max(count - (win_size - 1) / 2, 0): \
min(count + (win_size + 1) / 2, data.size)]
mask = np.isfinite(part_data)
if np.sum(mask) != 0:
result[count] = np.sum(part_data[mask]) / np.sum(mask)
else:
result[count] = None
return result
def numpy_app(data, win_size):
mask = np.isnan(data)
K = np.ones(win_size,dtype=int)
out = np.convolve(np.where(mask,0,data), K)/np.convolve(~mask,K)
return out[1:-1] # Slice out the one-extra elems on sides
样品运行-
In [118]: #Construct sample data
...: n = 50
...: n_miss = 20
...: win_size = 3
...: data= np.random.random(50)
...: data[np.random.randint(0,n-1, n_miss)] = np.nan
...:
In [119]: original_app(data, win_size = 3)
Out[119]:
array([ 0.88356487, 0.86829731, 0.85249541, 0.83776219, nan,
nan, 0.61054015, 0.63111926, 0.63111926, 0.65169837,
0.1857301 , 0.58335324, 0.42088104, 0.5384565 , 0.31027752,
0.40768907, 0.3478563 , 0.34089655, 0.55462903, 0.71784816,
0.93195716, nan, 0.41635575, 0.52211653, 0.65053379,
0.76762282, 0.72888574, 0.35250449, 0.35250449, 0.14500637,
0.06997668, 0.22582318, 0.18621848, 0.36320784, 0.19926647,
0.24506199, 0.09983572, 0.47595439, 0.79792941, 0.5982114 ,
0.42389375, 0.28944089, 0.36246113, 0.48088139, 0.71105449,
0.60234163, 0.40012839, 0.45100475, 0.41768466, 0.41768466])
In [120]: numpy_app(data, win_size = 3)
__main__:36: RuntimeWarning: invalid value encountered in divide
Out[120]:
array([ 0.88356487, 0.86829731, 0.85249541, 0.83776219, nan,
nan, 0.61054015, 0.63111926, 0.63111926, 0.65169837,
0.1857301 , 0.58335324, 0.42088104, 0.5384565 , 0.31027752,
0.40768907, 0.3478563 , 0.34089655, 0.55462903, 0.71784816,
0.93195716, nan, 0.41635575, 0.52211653, 0.65053379,
0.76762282, 0.72888574, 0.35250449, 0.35250449, 0.14500637,
0.06997668, 0.22582318, 0.18621848, 0.36320784, 0.19926647,
0.24506199, 0.09983572, 0.47595439, 0.79792941, 0.5982114 ,
0.42389375, 0.28944089, 0.36246113, 0.48088139, 0.71105449,
0.60234163, 0.40012839, 0.45100475, 0.41768466, 0.41768466])
运行时测试-
In [122]: #Construct sample data
...: n = 50000
...: n_miss = 20000
...: win_size = 3
...: data= np.random.random(n)
...: data[np.random.randint(0,n-1, n_miss)] = np.nan
...:
In [123]: %timeit original_app(data, win_size = 3)
1 loops, best of 3: 1.51 s per loop
In [124]: %timeit numpy_app(data, win_size = 3)
1000 loops, best of 3: 1.09 ms per loop
In [125]: import pandas as pd
# @jdehesa's pandas solution
In [126]: %timeit pd.Series(data).rolling(window=3, min_periods=1).mean()
100 loops, best of 3: 3.34 ms per loop
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