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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.

1 Answer

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by (33.2k points)

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

Hope this answer helps you!

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