I am trying to fill a dataframe with zeros, however **I do not want to touch leading NaNs**:

```
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

```

The df I want after filling/replacing is this:

```
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

```

Since `fillna()` only allows value or method and `fillna(0)` replaces **all NaNs, including leading**, I was hoping replace could jump in here, but

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

```

also replaces all NaNs.

**How can I zero fill values only after the first non-NaN value, also with multiple data columns?**