本文整理汇总了Python中pandas.core.series.Series.fillna方法的典型用法代码示例。如果您正苦于以下问题：Python Series.fillna方法的具体用法？Python Series.fillna怎么用？Python Series.fillna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.series.Series的用法示例。
在下文中一共展示了Series.fillna方法的9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: pad
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import fillna [as 别名]
def pad(self, limit=None):
"""
Forward fill the values.
Parameters
----------
limit : integer, optional
limit of how many values to fill
See Also
--------
Series.pad
DataFrame.pad
Series.fillna
DataFrame.fillna
"""
return self._fill('ffill', limit=limit) 示例2: backfill
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import fillna [as 别名]
def backfill(self, limit=None):
"""
Backward fill the values.
Parameters
----------
limit : integer, optional
limit of how many values to fill
See Also
--------
Series.backfill
DataFrame.backfill
Series.fillna
DataFrame.fillna
"""
return self._fill('bfill', limit=limit) 示例3: pad
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import fillna [as 别名]
def pad(self, limit=None):
"""
Forward fill the values
Parameters
----------
limit : integer, optional
limit of how many values to fill
See Also
--------
Series.pad
DataFrame.pad
Series.fillna
DataFrame.fillna
"""
return self._fill('ffill', limit=limit) 示例4: backfill
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import fillna [as 别名]
def backfill(self, limit=None):
"""
Backward fill the values
Parameters
----------
limit : integer, optional
limit of how many values to fill
See Also
--------
Series.backfill
DataFrame.backfill
Series.fillna
DataFrame.fillna
"""
return self._fill('bfill', limit=limit) 示例5: _transform_fast
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import fillna [as 别名]
def _transform_fast(self, result, obj):
"""
Fast transform path for aggregations
"""
# if there were groups with no observations (Categorical only?)
# try casting data to original dtype
cast = (self.size().fillna(0) > 0).any()
# for each col, reshape to to size of original frame
# by take operation
ids, _, ngroup = self.grouper.group_info
output = []
for i, _ in enumerate(result.columns):
res = algorithms.take_1d(result.iloc[:, i].values, ids)
if cast:
res = self._try_cast(res, obj.iloc[:, i])
output.append(res)
return DataFrame._from_arrays(output, columns=result.columns,
index=obj.index) 示例6: _transform_should_cast
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import fillna [as 别名]
def _transform_should_cast(self, func_nm):
"""
Parameters:
-----------
func_nm: str
The name of the aggregation function being performed
Returns:
--------
bool
Whether transform should attempt to cast the result of aggregation
"""
return (self.size().fillna(0) > 0).any() and (
func_nm not in base.cython_cast_blacklist) 示例7: _transform_should_cast
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import fillna [as 别名]
def _transform_should_cast(self, func_nm):
"""
Parameters:
-----------
func_nm: str
The name of the aggregation function being performed
Returns:
--------
bool
Whether transform should attempt to cast the result of aggregation
"""
return (self.size().fillna(0) > 0).any() and (func_nm not in
_cython_cast_blacklist) 示例8: pad
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import fillna [as 别名]
def pad(self, limit=None):
"""
Forward fill the values
Parameters
----------
limit : integer, optional
limit of how many values to fill
See Also
--------
Series.fillna
DataFrame.fillna
"""
return self.apply(lambda x: x.ffill(limit=limit)) 示例9: backfill
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import fillna [as 别名]
def backfill(self, limit=None):
"""
Backward fill the values
Parameters
----------
limit : integer, optional
limit of how many values to fill
See Also
--------
Series.fillna
DataFrame.fillna
"""
return self.apply(lambda x: x.bfill(limit=limit))