python - 零填充数据帧正向填充

Question

我正在尝试用零填充数据框，但是我不想触及前导 NaN:

rng = pd.date_range('2016-06-01', periods=9, freq='D')
df = pd.DataFrame({'data': pd.Series([np.nan]*3 + [20, 30, 40] + [np.nan]*3, rng)})

2016-06-01     NaN
2016-06-02     NaN
2016-06-03     NaN
2016-06-04    20.0
2016-06-05    30.0
2016-06-06    40.0
2016-06-07     NaN
2016-06-08     NaN
2016-06-09     NaN

填充/替换后我想要的df是这样的:

pd.DataFrame({'data': pd.Series([np.nan]*3 + [20, 30, 40] + [0.]*3, rng)})

2016-06-01     NaN
2016-06-02     NaN
2016-06-03     NaN
2016-06-04    20.0
2016-06-05    30.0
2016-06-06    40.0
2016-06-07     0.0
2016-06-08     0.0
2016-06-09     0.0

由于 fillna() 只允许值或方法并且 fillna(0) 替换所有 NaN，包括前导，我希望替换可以跳到这里，但是

df.replace([np.nan], 0, method='ffill')

也替换所有 NaN。

如何仅在第一个非 NaN 值之后对多个数据列进行零填充？

Answer 1

你可以使用 last_valid_index() 来做到这一点功能:

In [80]: df
Out[80]:
            data  data1  data2
2016-06-01   NaN    NaN    NaN
2016-06-02   NaN    NaN   10.0
2016-06-03   NaN   20.0   20.0
2016-06-04  20.0   30.0   20.0
2016-06-05   NaN   40.0    NaN
2016-06-06  40.0   30.0   40.0
2016-06-07   NaN    NaN    NaN
2016-06-08   NaN    NaN    NaN
2016-06-09   NaN    NaN    NaN

In [81]: %paste
first_valid_idx = df.apply(lambda x: x.first_valid_index()).to_frame()
df = df.fillna(0)
for ix, r in first_valid_idx.iterrows():
    df.loc[df.index < r[0], ix] = np.nan
## -- End pasted text --

In [82]: df
Out[82]:
            data  data1  data2
2016-06-01   NaN    NaN    NaN
2016-06-02   NaN    NaN   10.0
2016-06-03   NaN   20.0   20.0
2016-06-04  20.0   30.0   20.0
2016-06-05   0.0   40.0    0.0
2016-06-06  40.0   30.0   40.0
2016-06-07   0.0    0.0    0.0
2016-06-08   0.0    0.0    0.0
2016-06-09   0.0    0.0    0.0

In [83]: first_valid_idx
Out[83]:
               0
data  2016-06-04
data1 2016-06-03
data2 2016-06-02

旧答案:

In [38]: df.loc[df.index > df.data.last_valid_index(), 'data'] = 0

In [39]: df
Out[39]:
            data
2016-06-01   NaN
2016-06-02   NaN
2016-06-03   NaN
2016-06-04  20.0
2016-06-05  30.0
2016-06-06  40.0
2016-06-07   0.0
2016-06-08   0.0
2016-06-09   0.0