Getting all rows with NaN value

Question

I have a table with a column that has some NaN values in it:

A   B   C   D
2   3   2   Nan
3   4   5   5
2   3   1   Nan

I'd like to get all rows where D = NaN. How can I do this?

Answer 1

Creating a df for illustration (containing Nan)

In [86]: df =pd.DataFrame({'a':[1,2,3],'b':[3,4,5],'c':[np.nan, 4,5]})

In [87]: df
Out[87]: 
   a  b   c
0  1  3 NaN
1  2  4   4
2  3  5   5

Checking which indices have null for column c

In [88]: pd.isnull(df['c'])
Out[88]: 
0     True
1    False
2    False
Name: c, dtype: bool

Checking which indices dont have null for column c

In [90]: pd.notnull(df['c'])
Out[90]: 
0    False
1     True
2     True
Name: c, dtype: bool

Selecting rows of df where c is not null

In [91]: df[pd.notnull(df['c'])]
Out[91]: 
   a  b  c
1  2  4  4
2  3  5  5

Selecting rows of df where c is null

In [93]: df[pd.isnull(df['c'])]
Out[93]: 
   a  b   c
0  1  3 NaN

Selecting rows of column c of df where c is not null

In [94]: df['c'][pd.notnull(df['c'])]
Out[94]: 
1    4
2    5
Name: c, dtype: float64

Answer 2

For a solution that doesn't involve pandas, you can do something like:

goodind=np.where(np.sum(np.isnan(y),axis=1)==0)[0] #indices of rows non containing nans

(or the negation if you want rows with nan) and use the indices to slice data. I am not sure sum is the best way to combine booleans, but np.any and np.all don't seem to have a axis parameter, so this is the best way I found.