How to set a cell to NaN in a pandas dataframe

I'd like to replace bad values in a column of a dataframe by NaN's.

mydata = {'x' : [10, 50, 18, 32, 47, 20], 'y' : ['12', '11', 'N/A', '13', '15', 'N/A']}
df = pd.DataFrame(mydata)

df[df.y == 'N/A']['y'] = np.nan

Though, the last line fails and throws a warning because it's working on a copy of df. So, what's the correct way to handle this? I've seen many solutions with iloc or ix but here, I need to use a boolean condition.

Solution 1:

just use replace:

In [106]:
df.replace('N/A',np.NaN)

Out[106]:
    x    y
0  10   12
1  50   11
2  18  NaN
3  32   13
4  47   15
5  20  NaN

What you're trying is called chain indexing: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

You can use loc to ensure you operate on the original dF:

In [108]:
df.loc[df['y'] == 'N/A','y'] = np.nan
df

Out[108]:
    x    y
0  10   12
1  50   11
2  18  NaN
3  32   13
4  47   15
5  20  NaN

Solution 2:

While using replace seems to solve the problem, I would like to propose an alternative. Problem with mix of numeric and some string values in the column not to have strings replaced with np.nan, but to make whole column proper. I would bet that original column most likely is of an object type

Name: y, dtype: object

What you really need is to make it a numeric column (it will have proper type and would be quite faster), with all non-numeric values replaced by NaN.

Thus, good conversion code would be

pd.to_numeric(df['y'], errors='coerce')

Specify errors='coerce' to force strings that can't be parsed to a numeric value to become NaN. Column type would be

Name: y, dtype: float64

Solution 3:

Most replies here above need to import an external module: import numpy as np

There is a built-in solution into pandas itself: pd.NA, to use like this:

df.replace('N/A', pd.NA)