本文整理汇总了Python中pandas.core.common._try_sort方法的典型用法代码示例。如果您正苦于以下问题：Python common._try_sort方法的具体用法？Python common._try_sort怎么用？Python common._try_sort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.common的用法示例。
在下文中一共展示了common._try_sort方法的7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: _iter_data
# 需要导入模块: from pandas.core import common [as 别名]
# 或者: from pandas.core.common import _try_sort [as 别名]
def _iter_data(self, data=None, keep_index=False, fillna=None):
if data is None:
data = self.data
if fillna is not None:
data = data.fillna(fillna)
# TODO: unused?
# if self.sort_columns:
# columns = com._try_sort(data.columns)
# else:
# columns = data.columns
for col, values in data.iteritems():
if keep_index is True:
yield col, values
else:
yield col, values.values 示例2: _sanitize_and_check
# 需要导入模块: from pandas.core import common [as 别名]
# 或者: from pandas.core.common import _try_sort [as 别名]
def _sanitize_and_check(indexes):
kinds = list({type(index) for index in indexes})
if list in kinds:
if len(kinds) > 1:
indexes = [Index(com._try_sort(x))
if not isinstance(x, Index) else
x for x in indexes]
kinds.remove(list)
else:
return indexes, 'list'
if len(kinds) > 1 or Index not in kinds:
return indexes, 'special'
else:
return indexes, 'array' 示例3: _iter_data
# 需要导入模块: from pandas.core import common [as 别名]
# 或者: from pandas.core.common import _try_sort [as 别名]
def _iter_data(self):
from pandas.core.frame import DataFrame
if isinstance(self.data, (Series, np.ndarray)):
yield self.label, np.asarray(self.data)
elif isinstance(self.data, DataFrame):
df = self.data
if self.sort_columns:
columns = com._try_sort(df.columns)
else:
columns = df.columns
for col in columns:
# # is this right?
# empty = df[col].count() == 0
# values = df[col].values if not empty else np.zeros(len(df))
values = df[col].values
yield col, values 示例4: _sanitize_and_check
# 需要导入模块: from pandas.core import common [as 别名]
# 或者: from pandas.core.common import _try_sort [as 别名]
def _sanitize_and_check(indexes):
kinds = list(set([type(index) for index in indexes]))
if list in kinds:
if len(kinds) > 1:
indexes = [Index(com._try_sort(x))
if not isinstance(x, Index) else x
for x in indexes]
kinds.remove(list)
else:
return indexes, 'list'
if len(kinds) > 1 or Index not in kinds:
return indexes, 'special'
else:
return indexes, 'array' 示例5: _iter_data
# 需要导入模块: from pandas.core import common [as 别名]
# 或者: from pandas.core.common import _try_sort [as 别名]
def _iter_data(self, data=None, keep_index=False, fillna=None):
if data is None:
data = self.data
if fillna is not None:
data = data.fillna(fillna)
# TODO: unused?
# if self.sort_columns:
# columns = _try_sort(data.columns)
# else:
# columns = data.columns
for col, values in data.iteritems():
if keep_index is True:
yield col, values
else:
yield col, values.values 示例6: _sanitize_and_check
# 需要导入模块: from pandas.core import common [as 别名]
# 或者: from pandas.core.common import _try_sort [as 别名]
def _sanitize_and_check(indexes):
kinds = list(set([type(index) for index in indexes]))
if list in kinds:
if len(kinds) > 1:
indexes = [Index(com._try_sort(x))
if not isinstance(x, Index) else
x for x in indexes]
kinds.remove(list)
else:
return indexes, 'list'
if len(kinds) > 1 or Index not in kinds:
return indexes, 'special'
else:
return indexes, 'array' 示例7: _init_dict
# 需要导入模块: from pandas.core import common [as 别名]
# 或者: from pandas.core.common import _try_sort [as 别名]
def _init_dict(self, data, index, columns, dtype=None):
# pre-filter out columns if we passed it
if columns is not None:
columns = _ensure_index(columns)
data = dict((k, v) for k, v in compat.iteritems(data)
if k in columns)
else:
columns = Index(_try_sort(list(data.keys())))
if index is None:
index = extract_index(list(data.values()))
sp_maker = lambda x: SparseArray(x, kind=self._default_kind,
fill_value=self._default_fill_value,
copy=True, dtype=dtype)
sdict = {}
for k, v in compat.iteritems(data):
if isinstance(v, Series):
# Force alignment, no copy necessary
if not v.index.equals(index):
v = v.reindex(index)
if not isinstance(v, SparseSeries):
v = sp_maker(v.values)
elif isinstance(v, SparseArray):
v = v.copy()
else:
if isinstance(v, dict):
v = [v.get(i, np.nan) for i in index]
v = sp_maker(v)
sdict[k] = v
# TODO: figure out how to handle this case, all nan's?
# add in any other columns we want to have (completeness)
nan_arr = np.empty(len(index), dtype='float64')
nan_arr.fill(np.nan)
nan_arr = sp_maker(nan_arr)
sdict.update((c, nan_arr) for c in columns if c not in sdict)
return to_manager(sdict, columns, index)