本文整理汇总了Python中pandas.core.api.Series.setdefault(date,{})[column]方法的典型用法代码示例。如果您正苦于以下问题：Python Series.setdefault(date,{})[column]方法的具体用法？Python Series.setdefault(date,{})[column]怎么用？Python Series.setdefault(date,{})[column]使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pandas.core.api.Series的用法示例。
在下文中一共展示了Series.setdefault(date,{})[column]方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: percentileRank
# 需要导入模块: from pandas.core.api import Series [as 别名]
# 或者: from pandas.core.api.Series import setdefault(date,{})[column] [as 别名]
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
See also
--------
scipy.stats.percentileofscore
Returns
-------
TimeSeries or DataFrame, depending on input
"""
from scipy.stats 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