[FIXED] Python: pandas.replace doesn't work with np.nan

Issue

I wanted to replace np.nan with None values, but weird behaviour occurred:

>>> import pandas as pd
>>> import numpy as np
>>> s = pd.Series(['cat', 'dog', np.nan, 'rabbit'])
>>> s = s.replace(np.nan, None)
>>> print(s)
0       cat
1       dog
2       dog
3    rabbit
dtype: object

How can np.nan be replaced with 'dog' string? I do not understand. Can you explain it to me, please?

---

I found out that this code works as expected, so the right solution answers are not necessary.

s = s.replace({np.nan: None})

Solution

According to documentation:

>>> s = pd.Series([10, 'a', 'a', 'b', 'a'])

When value=None and to_replace is a scalar, list or tuple, replace uses the method parameter (default ‘pad’) to do the replacement. So this is why the ‘a’ values are being replaced by 10 in rows 1 and 2 and ‘b’ in row 4 in this case. The command s.replace('a', None) is actually equivalent to s.replace(to_replace='a', value=None, method='pad'):

>>> s.replace('a', None)
0    10
1    10
2    10
3     b
4     b
dtype: object

Answered By - Jaroslav Bezděk