Count NaNs when unicode values present

Question

Good morning all, I have a `pandas` dataframe containing multiple series. For a given series within the dataframe, the datatypes are unicode, NaN, and int/float. I want to determine the number of NaNs in the series but cannot use the built in `numpy.isnan` method because it cannot safely cast unicode data into a format it can interpret. I have proposed a work around, but I'm wondering if there is a better/more Pythonic way of accomplishing this task. Thanks in advance, Myles ``` import pandas as pd import numpy as np test = pd.Series(data = [NaN, 2, u'string']) np.isnan(test).sum() #Error #Work around test2 = [x for x in test if not(isinstance(x, unicode))] numNaNs = np.isnan(test2).sum() ```

Accepted Answer

Use pandas.isnull:

```
In [24]: test = pd.Series(data = [NaN, 2, u'string'])

In [25]: pd.isnull(test)
Out[25]: 
0     True
1    False
2    False
dtype: bool
```

Note however, that `pd.isnull` also regards `None` as `True`:

```
In [28]: pd.isnull([NaN, 2, u'string', None])
Out[28]: array([ True, False, False,  True], dtype=bool)
```

Count NaNs when unicode values present

Solution

Problem