Repository: pandas-dev/pandas
File: pandas/core/series.py (Python)

Code before:
    associated index values-- they need not be the same length. The result
    index will be the sorted union of the two indexes.

    Parameters
    ----------
    data : array-like, Iterable, dict, or scalar value
        Contains data stored in Series. If data is a dict, argument order is
        maintained. Unordered sets are not supported.
    index : array-like or Index (1d)
        Values must be hashable and have the same length as `data`.
        Non-unique index values are allowed. Will default to
        RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like
        and index is None, then the keys in the data are used as the index. If the
        index is not None, the resulting Series is reindexed with the index values.
    dtype : str, numpy.dtype, or ExtensionDtype, optional
        Data type for the output Series. If not specified, this will be
        inferred from `data`.
        See the :ref:`user guide <basics.dtypes>` for more usages.
    name : Hashable, default None
        The name to give to the Series.
    copy : bool, default None
        Whether to copy input data. Only affects array or Series/Index input,
        because for other input (e.g. a list) a new array is created anyway.
        By default, will copy if the input data is a numpy or pandas array.
        For Series/Index input, a shallow copy of the data is made by default.
        Set to False to avoid copying array input, at your own risk (if you
        know the input data won't be modified elsewhere).

    See Also
    --------
    DataFrame : Two-dimensional, size-mutable, potentially heterogeneous tabular data.
    Index : Immutable sequence used for indexing and alignment.

    Notes
    -----
    Please reference the :ref:`User Guide <basics.series>` for more information.

    Examples
    --------
    Constructing Series from a dictionary with an Index specified

    >>> d = {"a": 1, "b": 2, "c": 3}
    >>> ser = pd.Series(data=d, index=["a", "b", "c"])
    >>> ser
    a   1
    b   2
    c   3
    dtype: int64

    The keys of the dictionary match with the Index values, hence the Index
    values have no effect.

Review comment (suggestion):
nit: no comma.


```suggestion
        Set to False to avoid copying array input at your own risk (if you
```

Code after:
    associated index values-- they need not be the same length. The result
    index will be the sorted union of the two indexes.

    Parameters
    ----------
    data : array-like, Iterable, dict, or scalar value
        Contains data stored in Series. If data is a dict, argument order is
        maintained. Unordered sets are not supported.
    index : array-like or Index (1d)
        Values must be hashable and have the same length as `data`.
        Non-unique index values are allowed. Will default to
        RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like
        and index is None, then the keys in the data are used as the index. If the
        index is not None, the resulting Series is reindexed with the index values.
    dtype : str, numpy.dtype, or ExtensionDtype, optional
        Data type for the output Series. If not specified, this will be
        inferred from `data`.
        See the :ref:`user guide <basics.dtypes>` for more usages.
    name : Hashable, default None
        The name to give to the Series.
    copy : bool, default None
        Whether to copy input data, only relevant for array, Series, and Index
        inputs (for other input, e.g. a list, a new array is created anyway).
        Defaults to True for array input and False for Index/Series.
        Even when False for Index/Series, a shallow copy of the data is made.
        Set to False to avoid copying array input at your own risk (if you
        know the input data won't be modified elsewhere).
        Set to True to force copying Series/Index input up front.

    See Also
    --------
    DataFrame : Two-dimensional, size-mutable, potentially heterogeneous tabular data.
    Index : Immutable sequence used for indexing and alignment.

    Notes
    -----
    Please reference the :ref:`User Guide <basics.series>` for more information.

    Examples
    --------
    Constructing Series from a dictionary with an Index specified

    >>> d = {"a": 1, "b": 2, "c": 3}
    >>> ser = pd.Series(data=d, index=["a", "b", "c"])
    >>> ser
    a   1
    b   2
    c   3
    dtype: int64

    The keys of the dictionary match with the Index values, hence the Index