Join strings from the same column in ´pandas´ using a placeholder condition - python-2.7

Question

I have a series of data that I need to filter.
The df consists of one col. of information that is separated by a row with with value NaN.
I would like to join all of the rows that occur until each NaN in a new column.
For example my data looks something like:
the
car
is
red
NaN
the
house
is
big
NaN
the
room
is
small
My desired result is
B
the car is red
the house is big
the room is small
Thus far, I am approaching this problema by building a function and applying it to each row in my dataframe. See below for my working code example so far.
def joinNan(row):
newRow = []
placeholder = 'NaN'
if row is not placeholder:
newRow.append(row)
if row == placeholder:
return newRow
df['B'] = df.loc[0].apply(joinNan)
For some reason, the first row of my data is being used as the index or column title, hence why I am using 'loc[0]' here instead of a specific column name.
If there is a more straight forward way to approach this directly iterating in the column, I am open for that suggestion too.
For now, I am trying to reach my desired solution and have not found any other similiar case in Stack overflow or the web in general to help me.

Answer 1

I think for test NaNs is necessary use isna, then greate helper Series by cumsum and aggregate join with groupby:
df=df.groupby(df[0].isna().cumsum())[0].apply(lambda x: ' '.join(x.dropna())).to_frame('B')
#for oldier version of pandas
df=df.groupby(df[0].isnull().cumsum())[0].apply(lambda x: ' '.join(x.dropna())).to_frame('B')
Another solution is filter out all NaNs before groupby:
mask = df[0].isna()
#mask = df[0].isnull()
df['g'] = mask.cumsum()
df = df[~mask].groupby('g')[0].apply(' '.join).to_frame('B')