Pandas - find first non-null value in column

For a series this will return the first no null value:

Creating Series s:

s = pd.Series(index=[2,4,5,6], data=[None, None, 2, None])

which creates this Series:

2    NaN
4    NaN
5    2.0
6    NaN
dtype: float64

You can get the first non-NaN value by using:

s.loc[~s.isnull()].iloc[0]

which returns

2.0

If you on the other hand have a dataframe like this one:

df = pd.DataFrame(index=[2,4,5,6], data=np.asarray([[None, None, 2, None], [1, None, 3, 4]]).transpose(), 
                  columns=['a', 'b'])

which looks like this:

    a       b
2   None    1
4   None    None
5   2       3
6   None    4

you can select per column the first non null value using this (for column a):

df.a.loc[~df.a.isnull()].iloc[0]

or if you want the first row containing no Null values anywhere you can use:

df.loc[~df.isnull().sum(1).astype(bool)].iloc[0]

Which returns:

a    2
b    3
Name: 5, dtype: object

You can use first_valid_index with select by loc:

s = pd.Series([np.nan,2,np.nan])
print (s)
0    NaN
1    2.0
2    NaN
dtype: float64

print (s.first_valid_index())
1

print (s.loc[s.first_valid_index()])
2.0

# If your Series contains ALL NaNs, you'll need to check as follows:

s = pd.Series([np.nan, np.nan, np.nan])
idx = s.first_valid_index()  # Will return None
first_valid_value = s.loc[idx] if idx is not None else None
print(first_valid_value)
None

You can also use get method instead

(Pdb) type(audio_col)
<class 'pandas.core.series.Series'>
(Pdb) audio_col.first_valid_index()
19
(Pdb) audio_col.get(first_audio_idx)
'first-not-nan-value.ogg'