Replace invalid values with None in Pandas DataFrame

Question

Is there any method to replace values with None in Pandas in Python?

You can use df.replace('pre', 'post') and can replace a value with another, but this can't be done if you want to replace with None value, which if you try, you get a strange result.

So here's an example:

df = DataFrame(['-',3,2,5,1,-5,-1,'-',9]) df.replace('-', 0) 

which returns a successful result.

But,

df.replace('-', None) 

which returns a following result:

0 0   - // this isn't replaced 1   3 2   2 3   5 4   1 5  -5 6  -1 7  -1 // this is changed to `-1`... 8   9 

Why does such a strange result be returned?

Since I want to pour this data frame into MySQL database, I can't put NaN values into any element in my data frame and instead want to put None. Surely, you can first change '-' to NaN and then convert NaN to None, but I want to know why the dataframe acts in such a terrible way.

Tested on pandas 0.12.0 dev on Python 2.7 and OS X 10.8. Python is a pre-installed version on OS X and I installed pandas by using SciPy Superpack script, for your information.

Answer 1

Actually in later versions of pandas this will give a TypeError:

df.replace('-', None) TypeError: If "to_replace" and "value" are both None then regex must be a mapping 

You can do it by passing either a list or a dictionary:

In [11]: df.replace('-', df.replace(['-'], [None]) # or .replace('-', {0: None}) Out[11]:       0 0  None 1     3 2     2 3     5 4     1 5    -5 6    -1 7  None 8     9 

But I recommend using NaNs rather than None:

In [12]: df.replace('-', np.nan) Out[12]:      0 0  NaN 1    3 2    2 3    5 4    1 5   -5 6   -1 7  NaN 8    9