仅在单个NaN时进行插值 [英] Interpolate only if single NaN
问题描述
熊猫中是否有办法仅对单个缺失的数据点进行插值?也就是说,如果连续有2个以上的NaN,我想不理会它们.
Is there a way in pandas to interpolate only single missing data points? That is, if there is 2+ consecutive NaN's, I'd like to leave them alone.
因此,例如:
s = pd.Series([1, None, 2, 3, None, None, 4.5])
d.interpolate(limit=1)
给我:
[ 1.0, 1.5, 2.0, 3.0, 3.5, NaN, 4.5 ]
但我想得到
[ 1.0, 1.5, 2.0, 3.0, NaN, NaN, 4.5 ]
如果有帮助,我会列出所有缺少单个值的索引列表.
If it helps, I have a list of the indexes where there are only single missing values.
推荐答案
我的看法是,这将是包含在interpolate
中的强大功能.
就是说,这归结为掩盖存在多个np.nan
的地方.我将在方便的函数中使用一些numpy
逻辑将其包装起来.
My opinion is that this would be a great capability to include in interpolate
.
That said, this boils down to masking the places where more than one np.nan
exist. I'll wrap that up with some numpy
logic in a handy function.
def cnan(s):
v = s.values
k = v.size
n = np.append(np.isnan(v), False)
m = np.empty(k, np.bool8)
m.fill(True)
i = np.where(n[:-1] & n[1:])[0] + np.arange(2)
m[i[i < k]] = False
return m
s.interpolate().where(cnan(s))
0 1.0
1 1.5
2 2.0
3 3.0
4 NaN
5 NaN
6 4.5
dtype: float64
对于那些对使用高级numpy
技术的通用解决方案感兴趣的人
For those interested in a general solution using advanced numpy
techniques
import pandas as pd
import numpy as np
from numpy.lib.stride_tricks import as_strided as strided
def mask_knans(a, x):
a = np.asarray(a)
k = a.size
n = np.append(np.isnan(a), [False] * (x - 1))
m = np.empty(k, np.bool8)
m.fill(True)
s = n.strides[0]
i = np.where(strided(n, (k + 1 - x, x), (s, s)).all(1))[0][:, None]
i = i + np.arange(x)
i = pd.unique(i[i < k])
m[i] = False
return m
演示
demo
a = np.array([1, np.nan, np.nan, np.nan, 3, np.nan, 4, 5, np.nan, np.nan, 6, 7])
print(mask_knans(a, 3))
[ True False False False True True True True True True True True]
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