def _estimateByearMedian(row, buildings, minRank=MIN_RANK):
"""Compute the missing construction year as the median of
all neighbours. Returns the estimated construction year"""
N = _getNeighbours(row["neighbours"], buildings, row.name)
if len(N) >= 1:
estimatedYear = N.bja.median()
if estimatedYear > 0 and not np.isnan(estimatedYear):
year = estimatedYear
else:
year = np.nan
return year
else:
np.nan
def _estimateByearRank(row, buildings, minRank=MIN_RANK):
"""Compute the missing construction year by ranking all
neighbours based on their attributes and euclidean distance.
Returns the estimated construction year"""
N = _getNeighbours(row["neighbours"], buildings, row.name)
if MIN_RANK < 1:
N = N.dropna()
if len(N) >= 1:
if min(N.loc[:,"rank"]) <= minRank:
estimatedYear = N.loc[N.loc[:,"rank"] == min(N.loc[:,"rank"]), "bja"].tolist()
estimatedYear = estimatedYear[0]
if estimatedYear > 0:
year = estimatedYear
else:
year = np.nan
else:
year = np.nan
return year
else:
return np.nan
