I recently found [dask](http://dask.pydata.org/en/latest/index.html) module that aims to be an easy-to-use python parallel processing module. Big selling point for me is that it works with pandas.

After reading a bit on its manual page, I can't find a way to do this trivially parallelizable task: 

```
ts.apply(func) # for pandas series
df.apply(func, axis = 1) # for pandas DF row apply

```

At the moment, to achieve this in dask, AFAIK,

```
ddf.assign(A=lambda df: df.apply(func, axis=1)).compute() # dask DataFrame

```

which is ugly syntax and is actually slower than outright

```
df.apply(func, axis = 1) # for pandas DF row apply

```

Any suggestion?

Edit: Thanks @MRocklin for the map function. It seems to be slower than plain pandas apply. Is this related to pandas GIL releasing issue or am I doing it wrong?

```
import dask.dataframe as dd
s = pd.Series([10000]*120)
ds = dd.from_pandas(s, npartitions = 3)

def slow_func(k):
    A = np.random.normal(size = k) # k = 10000
    s = 0
    for a in A:
        if a > 0:
            s += 1
        else:
            s -= 1
    return s

s.apply(slow_func) # 0.43 sec
ds.map(slow_func).compute() # 2.04 sec

```