@@ -6155,12 +6155,20 @@ def bfill(
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method = "bfill" , axis = axis , inplace = inplace , limit = limit , downcast = downcast
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)
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- _shared_docs [
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- "replace"
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- ] = """
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+ @doc (klass = _shared_doc_kwargs ["klass" ])
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+ def replace (
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+ self ,
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+ to_replace = None ,
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+ value = None ,
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+ inplace = False ,
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+ limit = None ,
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+ regex = False ,
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+ method = "pad" ,
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+ ):
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+ """
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Replace values given in `to_replace` with `value`.
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- Values of the %( klass)s are replaced with other values dynamically.
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+ Values of the { klass} are replaced with other values dynamically.
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This differs from updating with ``.loc`` or ``.iloc``, which require
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you to specify a location to update with some value.
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@@ -6192,19 +6200,19 @@ def bfill(
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- Dicts can be used to specify different replacement values
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for different existing values. For example,
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- ``{'a': 'b', 'y': 'z'}`` replaces the value 'a' with 'b' and
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+ ``{{ 'a': 'b', 'y': 'z'} }`` replaces the value 'a' with 'b' and
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'y' with 'z'. To use a dict in this way the `value`
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parameter should be `None`.
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- For a DataFrame a dict can specify that different values
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should be replaced in different columns. For example,
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- ``{'a': 1, 'b': 'z'}`` looks for the value 1 in column 'a'
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+ ``{{ 'a': 1, 'b': 'z'} }`` looks for the value 1 in column 'a'
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and the value 'z' in column 'b' and replaces these values
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with whatever is specified in `value`. The `value` parameter
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should not be ``None`` in this case. You can treat this as a
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special case of passing two lists except that you are
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specifying the column to search in.
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- For a DataFrame nested dictionaries, e.g.,
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- ``{'a': {'b': np.nan}}``, are read as follows: look in column
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+ ``{{ 'a': {{ 'b': np.nan}} }}``, are read as follows: look in column
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'a' for the value 'b' and replace it with NaN. The `value`
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parameter should be ``None`` to use a nested dict in this
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way. You can nest regular expressions as well. Note that
@@ -6237,7 +6245,7 @@ def bfill(
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string. Alternatively, this could be a regular expression or a
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list, dict, or array of regular expressions in which case
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`to_replace` must be ``None``.
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- method : {'pad', 'ffill', 'bfill', `None`}
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+ method : {{ 'pad', 'ffill', 'bfill', `None`} }
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The method to use when for replacement, when `to_replace` is a
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scalar, list or tuple and `value` is ``None``.
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@@ -6246,7 +6254,7 @@ def bfill(
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Returns
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-------
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- %( klass)s
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+ { klass}
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Object after replacement.
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Raises
@@ -6272,8 +6280,8 @@ def bfill(
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See Also
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--------
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- %( klass)s .fillna : Fill NA values.
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- %( klass)s .where : Replace values based on boolean condition.
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+ { klass} .fillna : Fill NA values.
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+ { klass} .where : Replace values based on boolean condition.
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Series.str.replace : Simple string replacement.
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Notes
@@ -6305,9 +6313,9 @@ def bfill(
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4 4
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dtype: int64
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- >>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
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+ >>> df = pd.DataFrame({{ 'A': [0, 1, 2, 3, 4],
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... 'B': [5, 6, 7, 8, 9],
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- ... 'C': ['a', 'b', 'c', 'd', 'e']})
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+ ... 'C': ['a', 'b', 'c', 'd', 'e']}} )
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>>> df.replace(0, 5)
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A B C
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0 5 5 a
@@ -6344,23 +6352,23 @@ def bfill(
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**dict-like `to_replace`**
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- >>> df.replace({0: 10, 1: 100})
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+ >>> df.replace({{ 0: 10, 1: 100} })
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A B C
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0 10 5 a
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1 100 6 b
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2 2 7 c
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3 3 8 d
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4 4 9 e
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- >>> df.replace({'A': 0, 'B': 5}, 100)
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+ >>> df.replace({{ 'A': 0, 'B': 5} }, 100)
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A B C
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0 100 100 a
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1 1 6 b
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2 2 7 c
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3 3 8 d
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4 4 9 e
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- >>> df.replace({'A': {0: 100, 4: 400}})
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+ >>> df.replace({{ 'A': {{ 0: 100, 4: 400}} }})
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A B C
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0 100 5 a
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1 1 6 b
@@ -6370,15 +6378,15 @@ def bfill(
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**Regular expression `to_replace`**
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- >>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'],
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- ... 'B': ['abc', 'bar', 'xyz']})
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+ >>> df = pd.DataFrame({{ 'A': ['bat', 'foo', 'bait'],
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+ ... 'B': ['abc', 'bar', 'xyz']}} )
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>>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
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A B
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0 new abc
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1 foo new
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2 bait xyz
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- >>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True)
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+ >>> df.replace({{ 'A': r'^ba.$'}} , {{ 'A': 'new'} }, regex=True)
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A B
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0 new abc
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1 foo bar
@@ -6390,7 +6398,7 @@ def bfill(
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1 foo new
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2 bait xyz
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- >>> df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'})
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+ >>> df.replace(regex={{ r'^ba.$': 'new', 'foo': 'xyz'} })
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A B
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0 new abc
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1 xyz new
@@ -6406,9 +6414,9 @@ def bfill(
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the data types in the `to_replace` parameter must match the data
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type of the value being replaced:
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- >>> df = pd.DataFrame({'A': [True, False, True],
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- ... 'B': [False, True, False]})
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- >>> df.replace({'a string': 'new value', True: False}) # raises
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+ >>> df = pd.DataFrame({{ 'A': [True, False, True],
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+ ... 'B': [False, True, False]}} )
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+ >>> df.replace({{ 'a string': 'new value', True: False} }) # raises
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Traceback (most recent call last):
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...
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TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str'
@@ -6427,7 +6435,7 @@ def bfill(
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``s.replace({'a': None})`` is equivalent to
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``s.replace(to_replace={'a': None}, value=None, method=None)``:
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- >>> s.replace({'a': None})
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+ >>> s.replace({{ 'a': None} })
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0 10
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1 None
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2 None
@@ -6450,17 +6458,6 @@ def bfill(
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4 b
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dtype: object
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"""
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-
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- @Appender (_shared_docs ["replace" ] % _shared_doc_kwargs )
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- def replace (
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- self ,
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- to_replace = None ,
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- value = None ,
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- inplace = False ,
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- limit = None ,
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- regex = False ,
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- method = "pad" ,
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- ):
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if not (
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is_scalar (to_replace )
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or isinstance (to_replace , pd .Series )
@@ -8239,17 +8236,29 @@ def ranker(data):
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return ranker (data )
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- _shared_docs [
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- "align"
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- ] = """
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+ @doc (** _shared_doc_kwargs )
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+ def align (
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+ self ,
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+ other ,
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+ join = "outer" ,
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+ axis = None ,
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+ level = None ,
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+ copy = True ,
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+ fill_value = None ,
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+ method = None ,
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+ limit = None ,
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+ fill_axis = 0 ,
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+ broadcast_axis = None ,
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+ ):
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+ """
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Align two objects on their axes with the specified join method.
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Join method is specified for each axis Index.
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Parameters
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----------
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other : DataFrame or Series
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- join : {'outer', 'inner', 'left', 'right'}, default 'outer'
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+ join : {{ 'outer', 'inner', 'left', 'right'} }, default 'outer'
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axis : allowed axis of the other object, default None
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Align on index (0), columns (1), or both (None).
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level : int or level name, default None
@@ -8261,7 +8270,7 @@ def ranker(data):
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fill_value : scalar, default np.NaN
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Value to use for missing values. Defaults to NaN, but can be any
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"compatible" value.
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- method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
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+ method : {{ 'backfill', 'bfill', 'pad', 'ffill', None} }, default None
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Method to use for filling holes in reindexed Series:
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- pad / ffill: propagate last valid observation forward to next valid.
@@ -8274,32 +8283,18 @@ def ranker(data):
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be partially filled. If method is not specified, this is the
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maximum number of entries along the entire axis where NaNs will be
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filled. Must be greater than 0 if not None.
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- fill_axis : %( axes_single_arg)s , default 0
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+ fill_axis : { axes_single_arg} , default 0
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Filling axis, method and limit.
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- broadcast_axis : %( axes_single_arg)s , default None
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+ broadcast_axis : { axes_single_arg} , default None
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Broadcast values along this axis, if aligning two objects of
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different dimensions.
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Returns
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-------
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- (left, right) : (%( klass)s , type of other)
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+ (left, right) : ({ klass} , type of other)
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Aligned objects.
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"""
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- @Appender (_shared_docs ["align" ] % _shared_doc_kwargs )
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- def align (
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- self ,
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- other ,
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- join = "outer" ,
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- axis = None ,
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- level = None ,
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- copy = True ,
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- fill_value = None ,
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- method = None ,
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- limit = None ,
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- fill_axis = 0 ,
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- broadcast_axis = None ,
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- ):
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method = missing .clean_fill_method (method )
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if broadcast_axis == 1 and self .ndim != other .ndim :
@@ -8836,9 +8831,11 @@ def mask(
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errors = errors ,
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)
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- _shared_docs [
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- "shift"
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- ] = """
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+ @doc (klass = _shared_doc_kwargs ["klass" ])
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+ def shift (
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+ self : FrameOrSeries , periods = 1 , freq = None , axis = 0 , fill_value = None
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+ ) -> FrameOrSeries :
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+ """
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Shift index by desired number of periods with an optional time `freq`.
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When `freq` is not passed, shift the index without realigning the data.
@@ -8855,7 +8852,7 @@ def mask(
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If `freq` is specified then the index values are shifted but the
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data is not realigned. That is, use `freq` if you would like to
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extend the index when shifting and preserve the original data.
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- axis : {0 or 'index', 1 or 'columns', None}, default None
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+ axis : {{ 0 or 'index', 1 or 'columns', None} }, default None
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Shift direction.
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fill_value : object, optional
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The scalar value to use for newly introduced missing values.
@@ -8868,7 +8865,7 @@ def mask(
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Returns
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-------
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- %( klass)s
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+ { klass}
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Copy of input object, shifted.
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See Also
@@ -8881,9 +8878,9 @@ def mask(
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Examples
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--------
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- >>> df = pd.DataFrame({'Col1': [10, 20, 15, 30, 45],
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+ >>> df = pd.DataFrame({{ 'Col1': [10, 20, 15, 30, 45],
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... 'Col2': [13, 23, 18, 33, 48],
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- ... 'Col3': [17, 27, 22, 37, 52]})
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+ ... 'Col3': [17, 27, 22, 37, 52]}} )
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>>> df.shift(periods=3)
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Col1 Col2 Col3
@@ -8909,11 +8906,6 @@ def mask(
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3 10 13 17
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4 20 23 27
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"""
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-
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- @Appender (_shared_docs ["shift" ] % _shared_doc_kwargs )
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- def shift (
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- self : FrameOrSeries , periods = 1 , freq = None , axis = 0 , fill_value = None
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- ) -> FrameOrSeries :
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if periods == 0 :
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return self .copy ()
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