def percentileRank(frame, column=None, kind='mean'):
"""
Return score at percentile for each point in time (cross-section)
Parameters
----------
frame: DataFrame
column: string or Series, optional
Column name or specific Series to compute percentiles for.
If not provided, percentiles are computed for all values at each
point in time. Note that this can take a LONG time.
kind: {'rank', 'weak', 'strict', 'mean'}, optional
This optional parameter specifies the interpretation of the
resulting score:
- "rank": Average percentage ranking of score. In case of
multiple matches, average the percentage rankings of
all matching scores.
- "weak": This kind corresponds to the definition of a cumulative
distribution function. A percentileofscore of 80%
means that 80% of values are less than or equal
to the provided score.
- "strict": Similar to "weak", except that only values that are
strictly less than the given score are counted.
- "mean": The average of the "weak" and "strict" scores, often used in
testing. See
http://en.wikipedia.org/wiki/Percentile_rank
Returns
-------
TimeSeries or DataFrame, depending on input
"""
from pandas.compat.scipy import percentileofscore
fun = lambda xs, score: percentileofscore(remove_na(xs),
score, kind=kind)
results = {}
framet = frame.T
if column is not None:
if isinstance(column, Series):
for date, xs in frame.T.iteritems():
results[date] = fun(xs, column.get(date, NaN))
else:
for date, xs in frame.T.iteritems():
results[date] = fun(xs, xs[column])
results = Series(results)
else:
for column in frame.columns:
for date, xs in framet.iteritems():
results.setdefault(date, {})[column] = fun(xs, xs[column])
results = DataFrame(results).T
return results
def percentileRank(frame, column=None, kind='mean'):
"""
Return score at percentile for each point in time (cross-section)
Parameters
----------
frame: DataFrame
column: string or Series, optional
Column name or specific Series to compute percentiles for.
If not provided, percentiles are computed for all values at each
point in time. Note that this can take a LONG time.
kind: {'rank', 'weak', 'strict', 'mean'}, optional
This optional parameter specifies the interpretation of the
resulting score:
- "rank": Average percentage ranking of score. In case of
multiple matches, average the percentage rankings of
all matching scores.
- "weak": This kind corresponds to the definition of a cumulative
distribution function. A percentileofscore of 80%
means that 80% of values are less than or equal
to the provided score.
- "strict": Similar to "weak", except that only values that are
strictly less than the given score are counted.
- "mean": The average of the "weak" and "strict" scores, often used in
testing. See
http://en.wikipedia.org/wiki/Percentile_rank
Returns
-------
TimeSeries or DataFrame, depending on input
"""
from pandas.compat.scipy import percentileofscore
fun = lambda xs, score: percentileofscore(remove_na(xs),
score, kind=kind)
results = {}
framet = frame.T
if column is not None:
if isinstance(column, Series):
for date, xs in frame.T.iteritems():
results[date] = fun(xs, column.get(date, NaN))
else:
for date, xs in frame.T.iteritems():
results[date] = fun(xs, xs[column])
results = Series(results)
else:
for column in frame.columns:
for date, xs in framet.iteritems():
results.setdefault(date, {})[column] = fun(xs, xs[column])
results = DataFrame(results).T
return results
