## Question

Asked By – Dun Peal

My numpy arrays use `np.nan`

to designate missing values. As I iterate over the data set, I need to detect such missing values and handle them in special ways.

Naively I used `numpy.isnan(val)`

, which works well unless `val`

isn’t among the subset of types supported by `numpy.isnan()`

. For example, missing data can occur in string fields, in which case I get:

```
>>> np.isnan('some_string')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Not implemented for this type
```

Other than writing an expensive wrapper that catches the exception and returns `False`

, is there a way to handle this elegantly and efficiently?

**Now we will see solution for issue: Efficiently checking if arbitrary object is NaN in Python / numpy / pandas? **

## Answer

`pandas.isnull()`

(also `pd.isna()`

, in newer versions) checks for missing values in both numeric and string/object arrays. From the documentation, it checks for:

NaN in numeric arrays, None/NaN in object arrays

Quick example:

```
import pandas as pd
import numpy as np
s = pd.Series(['apple', np.nan, 'banana'])
pd.isnull(s)
Out[9]:
0 False
1 True
2 False
dtype: bool
```

The idea of using `numpy.nan`

to represent missing values is something that `pandas`

introduced, which is why `pandas`

has the tools to deal with it.

Datetimes too (if you use `pd.NaT`

you won’t need to specify the dtype)

```
In [24]: s = Series([Timestamp('20130101'),np.nan,Timestamp('20130102 9:30')],dtype='M8[ns]')
In [25]: s
Out[25]:
0 2013-01-01 00:00:00
1 NaT
2 2013-01-02 09:30:00
dtype: datetime64[ns]``
In [26]: pd.isnull(s)
Out[26]:
0 False
1 True
2 False
dtype: bool
```

This question is answered By – Marius

**This answer is collected from stackoverflow and reviewed by FixPython community admins, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 **