Question

我有以下时间序列数据帧。我想用之前的值填充缺失的值。但是我只想填充first_valid_index和last_valid索引之间的缺失值。所以我想要填充的列对于每一行都是不同的。我怎样才能做到这一点？

所以，给定这个数据帧。

import numpy as np
import pandas as pd
df = pd.DataFrame([[1, 2 ,3,np.nan,5], [1, 3 , np.nan , 4 , np.nan], [4, np.nan , 7 , np.nan,np.nan]], columns=[2007,2008,2009,2010,2011])

输入数据帧：

    2007    2008    2009    2010    2011
     1       2       3      NaN     5
     1       3       NaN    4       NaN
     4       Nan     7      NaN     NaN

输出数据帧：

2007    2008    2009    2010    2011
 1       2       3        3      5
 1       3       3        4      NaN
 4       4       7        NaN    NaN

我想为first_valid_index和last_valid_index创建新列，然后使用 .apply（）但是如何在每行填充不同的列？

def fillMissing(x):
    first_valid = int(x["first_valid"])
    last_valid = int(x["last_valid"])
    for i in range(first_valid,last_valid + 1):
        missing.append(i)
    #What should i do here since the following is not valid 
    #x[missing] = x[missing].fillna(method='ffill', axis=1)


df.apply(fillMissing , axis=1)

Answer 1

您可以使用var_dumping执行此操作，但我更喜欢使用Numpy执行此操作。基本上，使用post来转发填充值，然后将var_dump的值一直掩盖到最后。

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Answer 2

以下是两个完全基于NumPy的版本，灵感来自this post -

def app1(df):
    # Same as in the linked post
    arr = df.values
    m,n = arr.shape
    r = np.arange(n)
    mask = np.isnan(arr)
    idx = np.where(~mask,r,0)
    idx = np.maximum.accumulate(idx,axis=1)
    out = arr[np.arange(m)[:,None], idx]

    # Additional part to keep the trailing NaN islands and output a dataframe
    out[(n - mask[:,::-1].argmin(1))[:,None] <= r] = np.nan
    return pd.DataFrame(out, columns=df.columns)

def app2(df):
    arr = df.values
    m,n = arr.shape

    r = np.arange(m)
    mask = np.isnan(arr)
    idx = np.where(~mask,np.arange(n),0)

    put_idx = n - mask[:,::-1].argmin(1)
    v = put_idx < n
    rv = r[v]
    idx[rv,put_idx[v]] = idx[rv,(put_idx-1)[v]]+1
    idx = np.maximum.accumulate(idx,axis=1)
    out = arr[r[:,None], idx]
    return pd.DataFrame(out, columns=df.columns)

样品运行 -

In [246]: df
Out[246]: 
   2007  2008  2009  2010  2011
0     1   2.0   3.0   NaN   5.0
1     1   3.0   NaN   4.0   NaN
2     4   NaN   7.0   NaN   NaN

In [247]: app1(df)
Out[247]: 
   2007  2008  2009  2010  2011
0   1.0   2.0   3.0   3.0   5.0
1   1.0   3.0   3.0   4.0   NaN
2   4.0   4.0   7.0   NaN   NaN

In [248]: app2(df)
Out[248]: 
   2007  2008  2009  2010  2011
0   1.0   2.0   3.0   3.0   5.0
1   1.0   3.0   3.0   4.0   NaN
2   4.0   4.0   7.0   NaN   NaN

运行时测试较大的df，其中填充了50%个NaN -

In [249]: df = pd.DataFrame(np.random.randint(1,9,(5000,5000)).astype(float))

In [250]: idx = np.random.choice(df.size, df.size//2, replace=0)

In [251]: df.values.ravel()[idx] = np.nan

# @piRSquared's soln
In [252]: %%timeit
     ...: v = df.values
     ...: mask = np.logical_and.accumulate(
     ...:     np.isnan(v)[:, ::-1], axis=1)[:, ::-1]
     ...: df.ffill(axis=1).mask(mask)
1 loop, best of 3: 473 ms per loop

In [253]: %timeit app1(df)
1 loop, best of 3: 353 ms per loop

In [254]: %timeit app2(df)
1 loop, best of 3: 330 ms per loop

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2 个答案: