本文整理匯總了Python中pandas.core.frame.DataFrame.fillna方法的典型用法代碼示例。如果您正苦於以下問題：Python DataFrame.fillna方法的具體用法？Python DataFrame.fillna怎麽用？Python DataFrame.fillna使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pandas.core.frame.DataFrame的用法示例。
在下文中一共展示了DataFrame.fillna方法的9個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: pad
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import fillna [as 別名]
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
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import fillna [as 別名]
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
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import fillna [as 別名]
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
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import fillna [as 別名]
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
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import fillna [as 別名]
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
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import fillna [as 別名]
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
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import fillna [as 別名]
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
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import fillna [as 別名]
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
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import fillna [as 別名]
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))