First, make function.
def squeeze_nan(x):
original_columns = x.index.tolist()
squeezed = x.dropna()
squeezed.index = [original_columns[n] for n in range(squeezed.count())]
return squeezed.reindex(original_columns, fill_value=np.nan)
Second, apply the function.
df.apply(squeeze_nan, axis=1)
You can also try axis=0 and .[::-1] to squeeze nan to any direction.
[EDIT]
@Mxracer888 you want this?
def squeeze_nan(x, hold):
if x.name not in hold:
original_columns = x.index.tolist()
squeezed = x.dropna()
squeezed.index = [original_columns[n] for n in range(squeezed.count())]
return squeezed.reindex(original_columns, fill_value=np.nan)
else:
return x
df.apply(lambda x: squeeze_nan(x, ['B']), axis=1)