本文整理匯總了Python中pandas.core.frame.DataFrame.name方法的典型用法代碼示例。如果您正苦於以下問題：Python DataFrame.name方法的具體用法？Python DataFrame.name怎麽用？Python DataFrame.name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.name方法的2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: str_extract
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import name [as 別名]
def str_extract(arr, pat, flags=0):
"""
Find groups in each string using passed regular expression
Parameters
----------
pat : string
Pattern or regular expression
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
Returns
-------
extracted groups : Series (one group) or DataFrame (multiple groups)
Notes
-----
Compare to the string method match, which returns re.match objects.
"""
regex = re.compile(pat, flags=flags)
# just to be safe, check this
if regex.groups == 0:
raise ValueError("This pattern contains no groups to capture.")
elif regex.groups == 1:
def f(x):
if not isinstance(x, compat.string_types):
return None
m = regex.search(x)
if m:
return m.groups()[0] # may be None
else:
return None
else:
empty_row = Series(regex.groups * [None])
def f(x):
if not isinstance(x, compat.string_types):
return empty_row
m = regex.search(x)
if m:
return Series(list(m.groups())) # may contain None
else:
return empty_row
result = arr.apply(f)
result.replace({None: np.nan}, inplace=True)
if regex.groups > 1:
result = DataFrame(result) # Don't rely on the wrapper; name columns.
names = dict(zip(regex.groupindex.values(), regex.groupindex.keys()))
result.columns = [names.get(1 + i, i) for i in range(regex.groups)]
else:
result.name = regex.groupindex.get(0)
return result示例2: str_extract
# 需要導入模塊: from pandas.core.frame import DataFrame [as 別名]
# 或者: from pandas.core.frame.DataFrame import name [as 別名]
def str_extract(arr, pat, flags=0):
"""
Find groups in each string using passed regular expression
Parameters
----------
pat : string
Pattern or regular expression
flags : int, default 0 (no flags)
re module flags, e.g. re.IGNORECASE
Returns
-------
extracted groups : Series (one group) or DataFrame (multiple groups)
Examples
--------
A pattern with one group will return a Series. Non-matches will be NaN.
>>> Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)')
0 1
1 2
2 NaN
dtype: object
A pattern with more than one group will return a DataFrame.
>>> Series(['a1', 'b2', 'c3']).str.extract('([ab])(\d)')
A pattern may contain optional groups.
>>> Series(['a1', 'b2', 'c3']).str.extract('([ab])?(\d)')
Named groups will become column names in the result.
>>> Series(['a1', 'b2', 'c3']).str.extract('(?P<letter>[ab])(?P<digit>\d)')
"""
regex = re.compile(pat, flags=flags)
# just to be safe, check this
if regex.groups == 0:
raise ValueError("This pattern contains no groups to capture.")
elif regex.groups == 1:
def f(x):
if not isinstance(x, compat.string_types):
return None
m = regex.search(x)
if m:
return m.groups()[0] # may be None
else:
return None
else:
empty_row = Series(regex.groups * [None])
def f(x):
if not isinstance(x, compat.string_types):
return empty_row
m = regex.search(x)
if m:
return Series(list(m.groups())) # may contain None
else:
return empty_row
result = arr.apply(f)
result.replace({None: np.nan}, inplace=True)
if regex.groups > 1:
result = DataFrame(result) # Don't rely on the wrapper; name columns.
names = dict(zip(regex.groupindex.values(), regex.groupindex.keys()))
result.columns = [names.get(1 + i, i) for i in range(regex.groups)]
else:
result.name = regex.groupindex.get(0)
return result