My data contain columns with empty rows that are read by pandas as `nan`.
I want to create a dictionary of list from this data. However, some list contains `nan` and I want to remove it.

If I use `dropna()` in `data.dropna().to_dict(orient='list')`, this will remove all the rows that contains at least one `nan`, thefore I lose data.

```
Col1 Col2  Col3
a     x     r
b     y     v
c           x
            z

data = pd.read_csv(sys.argv[2], sep = ',')
dict = data.to_dict(orient='list')

Current output:
dict = {Col1: ['a','b','c',nan], Col2: ['x', 'y',nan,nan], Col3: ['r', 'v', 'x', 'z']}

Desire Output:
dict = {Col1: ['a','b','c'], Col2: ['x', 'y'], Col3: ['r', 'v', 'x', 'z']}

```

My goal: get the dictionary of a list, with `nan` remove from the list.