DataFrame.interpolate() 在尾部缺失数据上进行外推

Question

考虑以下示例，我们设置了一个样本数据集，创建了一个多重索引，然后将数据框进行“展开”，接着执行线性插值，逐行填充数据：

import pandas as pd  # version 0.14.1
import numpy as np  # version 1.8.1

df = pd.DataFrame({'location': ['a', 'b'] * 5,
                   'trees': ['oaks', 'maples'] * 5,
                   'year': range(2000, 2005) * 2,
                   'value': [np.NaN, 1, np.NaN, 3, 2, np.NaN, 5, np.NaN, np.NaN, np.NaN]})
df.set_index(['trees', 'location', 'year'], inplace=True)
df = df.unstack()
df = df.interpolate(method='linear', axis=1)

展开后的数据集看起来是这样的：

                 value                        
year              2000  2001  2002  2003  2004
trees  location                               
maples b           NaN     1   NaN     3   NaN
oaks   a           NaN     5   NaN   NaN     2

作为一种插值方法，我期待的输出是：

                 value                        
year              2000  2001  2002  2003  2004
trees  location                               
maples b           NaN     1     2     3   NaN
oaks   a           NaN     5     4     3     2

但实际上这个方法给出的结果是（注意这个外推的值）：

                 value                        
year              2000  2001  2002  2003  2004
trees  location                               
maples b           NaN     1     2     3     3
oaks   a           NaN     5     4     3     2

有没有办法告诉pandas不要在序列中超出最后一个非缺失值进行外推呢？

编辑：

我还是很希望在pandas中看到这个功能，不过现在我在numpy中实现了这个功能，然后使用df.apply()来修改df。我在pandas中缺少的正是np.interp()中的left和right参数的功能。

def interpolate(a, dec=None):
    """
    :param a: a 1d array to be interpolated
    :param dec: the number of decimal places with which each
                value should be returned
    :return: returns an array of integers or floats
    """

    # default value is the largest number of decimal places in the input array
    if dec is None:
        dec = max_decimal(a)

    # detect array format convert to numpy as necessary
    if type(a) == list:
        t = 'list'
        b = np.asarray(a, dtype='float')
    if type(a) in [pd.Series, np.ndarray]:
        b = a

    # return the row if it's all nan's
    if np.all(np.isnan(b)):
        return a

    # interpolate
    x = np.arange(b.size)
    xp = np.where(~np.isnan(b))[0]
    fp = b[xp]
    interp = np.around(np.interp(x, xp, fp, np.nan, np.nan), decimals=dec)

    # return with proper numerical type formatting
    # check to make sure there aren't nan's before converting to int
    if dec == 0 and np.isnan(np.sum(interp)) == False:
        interp = interp.astype(int)
    if t == 'list':
        return interp.tolist()
    else:
        return interp


# two little helper functions
def count_decimal(i):
    try:
        return int(decimal.Decimal(str(i)).as_tuple().exponent) * -1
    except ValueError:
        return 0


def max_decimal(a):
    m = 0
    for i in a:
        n = count_decimal(i)
        if n > m:
            m = n
    return m

在这个示例数据集上效果很好：

In[1]: df.apply(interpolate, axis=1)
Out[1]:
                 value                        
year              2000  2001  2002  2003  2004
trees  location                               
maples b           NaN     1     2     3   NaN
oaks   a           NaN     5     4     3     2

numpy pandas 数据框缺失值处理多重索引线性插值数据插值外推

DataFrame.interpolate() 在尾部缺失数据上进行外推

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