Locate first and last non NaN values in a Pandas DataFrame

@behzad.nouri's solution worked perfectly to return the first and last non-NaN values using Series.first_valid_index and Series.last_valid_index, respectively.

Here's some helpful examples.

Series

s = pd.Series([np.NaN, 1, np.NaN, 3, np.NaN], index=list('abcde'))
s

a    NaN
b    1.0
c    NaN
d    3.0
e    NaN
dtype: float64

# first valid index
s.first_valid_index()
# 'b'

# first valid position
s.index.get_loc(s.first_valid_index())
# 1

# last valid index
s.last_valid_index()
# 'd'

# last valid position
s.index.get_loc(s.last_valid_index())
# 3

Alternative solution using notna and idxmax:

# first valid index
s.notna().idxmax()
# 'b'

# last valid index
s.notna()[::-1].idxmax()
# 'd'

DataFrame

df = pd.DataFrame({
    'A': [np.NaN, 1, np.NaN, 3, np.NaN], 
    'B': [1, np.NaN, np.NaN, np.NaN, np.NaN]
})
df

     A    B
0  NaN  1.0
1  1.0  NaN
2  NaN  NaN
3  3.0  NaN
4  NaN  NaN

(first|last)_valid_index isn't defined on DataFrames, but you can apply them on each column using apply.

# first valid index for each column
df.apply(pd.Series.first_valid_index)

A    1
B    0
dtype: int64

# last valid index for each column
df.apply(pd.Series.last_valid_index)

A    3
B    0
dtype: int64

As before, you can also use notna and idxmax. This is slightly more natural syntax.

# first valid index
df.notna().idxmax()

A    1
B    0
dtype: int64

# last valid index
df.notna()[::-1].idxmax()

A    3
B    0
dtype: int64