Replace invalid values with None in Pandas DataFrame

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]:      00  None1     32     23     54     15    -56    -17  None8     9

But I recommend using NaNs rather than None:

In [12]: df.replace('-', np.nan)Out[12]:     00  NaN1    32    23    54    15   -56   -17  NaN8    9

I prefer the solution using replace with a dict because of its simplicity and elegance:

df.replace({'-': None})

You can also have more replacements:

df.replace({'-': None, 'None': None})

And even for larger replacements, it is always obvious and clear what is replaced by what - which is way harder for long lists, in my opinion.

where is probably what you're looking for. So

data=data.where(data=='-', None) 

From the panda docs:

where [returns] an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other).