DOC: Remove pandas. prefix from See Also section in Series
When referencing other functions in docstrings `See Also` section, `pandas` is assumed to be the default project. So, this would be correct:
```
def shape():
    """
    See Also
    --------
    numpy.ndim : Number of dimensions property in numpy.
    DataFrame.ndim : Number of dimensions property in pandas DataFrame.
```

and this would be incorrect:
```
def shape():
    """
    See Also
    --------
    numpy.ndim : Number of dimensions property in numpy.
    pandas.DataFrame.ndim : Number of dimensions property in pandas DataFrame.
```

Note the `pandas.` before `DataFrame.ndim`, which is not required and adds noise.

To get all the cases in which this happens in `Series`, we can use:
```
$ ./scripts/validate_docstrings.py --prefix=pandas.Series --errors=SA05
pandas.Series.astype: pandas.to_datetime in `See Also` section does not need `pandas` prefix, use to_datetime instead.
pandas.Series.astype: pandas.to_timedelta in `See Also` section does not need `pandas` prefix, use to_timedelta instead.
pandas.Series.astype: pandas.to_numeric in `See Also` section does not need `pandas` prefix, use to_numeric instead.
pandas.Series.infer_objects: pandas.to_datetime in `See Also` section does not need `pandas` prefix, use to_datetime instead.
pandas.Series.infer_objects: pandas.to_timedelta in `See Also` section does not need `pandas` prefix, use to_timedelta instead.
pandas.Series.infer_objects: pandas.to_numeric in `See Also` section does not need `pandas` prefix, use to_numeric instead.
pandas.Series.convert_objects: pandas.to_datetime in `See Also` section does not need `pandas` prefix, use to_datetime instead.
pandas.Series.convert_objects: pandas.to_timedelta in `See Also` section does not need `pandas` prefix, use to_timedelta instead.
pandas.Series.convert_objects: pandas.to_numeric in `See Also` section does not need `pandas` prefix, use to_numeric instead.
pandas.Series.agg: pandas.Series.apply in `See Also` section does not need `pandas` prefix, use Series.apply instead.
pandas.Series.agg: pandas.Series.transform in `See Also` section does not need `pandas` prefix, use Series.transform instead.
pandas.Series.aggregate: pandas.Series.apply in `See Also` section does not need `pandas` prefix, use Series.apply instead.
pandas.Series.aggregate: pandas.Series.transform in `See Also` section does not need `pandas` prefix, use Series.transform instead.
pandas.Series.pipe: pandas.DataFrame.apply in `See Also` section does not need `pandas` prefix, use DataFrame.apply instead.
pandas.Series.pipe: pandas.DataFrame.applymap in `See Also` section does not need `pandas` prefix, use DataFrame.applymap instead.
pandas.Series.pipe: pandas.Series.map in `See Also` section does not need `pandas` prefix, use Series.map instead.
pandas.Series.all: pandas.Series.all in `See Also` section does not need `pandas` prefix, use Series.all instead.
pandas.Series.all: pandas.DataFrame.any in `See Also` section does not need `pandas` prefix, use DataFrame.any instead.
pandas.Series.between: pandas.Series.gt in `See Also` section does not need `pandas` prefix, use Series.gt instead.
pandas.Series.between: pandas.Series.lt in `See Also` section does not need `pandas` prefix, use Series.lt instead.
pandas.Series.cummax: pandas.core.window.Expanding.max in `See Also` section does not need `pandas` prefix, use core.window.Expanding.max instead.
pandas.Series.cummin: pandas.core.window.Expanding.min in `See Also` section does not need `pandas` prefix, use core.window.Expanding.min instead.
pandas.Series.cumprod: pandas.core.window.Expanding.prod in `See Also` section does not need `pandas` prefix, use core.window.Expanding.prod instead.
pandas.Series.cumsum: pandas.core.window.Expanding.sum in `See Also` section does not need `pandas` prefix, use core.window.Expanding.sum instead.
pandas.Series.factorize: pandas.cut in `See Also` section does not need `pandas` prefix, use cut instead.
pandas.Series.factorize: pandas.unique in `See Also` section does not need `pandas` prefix, use unique instead.
pandas.Series.quantile: pandas.core.window.Rolling.quantile in `See Also` section does not need `pandas` prefix, use core.window.Rolling.quantile instead.
pandas.Series.unique: pandas.unique in `See Also` section does not need `pandas` prefix, use unique instead.
pandas.Series.duplicated: pandas.Index.duplicated in `See Also` section does not need `pandas` prefix, use Index.duplicated instead.
pandas.Series.duplicated: pandas.DataFrame.duplicated in `See Also` section does not need `pandas` prefix, use DataFrame.duplicated instead.
pandas.Series.duplicated: pandas.Series.drop_duplicates in `See Also` section does not need `pandas` prefix, use Series.drop_duplicates instead.
pandas.Series.head: pandas.DataFrame.tail in `See Also` section does not need `pandas` prefix, use DataFrame.tail instead.
pandas.Series.isin: pandas.DataFrame.isin in `See Also` section does not need `pandas` prefix, use DataFrame.isin instead.
pandas.Series.rename: pandas.Series.rename_axis in `See Also` section does not need `pandas` prefix, use Series.rename_axis instead.
pandas.Series.rename_axis: pandas.Series.rename in `See Also` section does not need `pandas` prefix, use Series.rename instead.
pandas.Series.rename_axis: pandas.DataFrame.rename in `See Also` section does not need `pandas` prefix, use DataFrame.rename instead.
pandas.Series.rename_axis: pandas.Index.rename in `See Also` section does not need `pandas` prefix, use Index.rename instead.
pandas.Series.set_axis: pandas.DataFrame.rename_axis in `See Also` section does not need `pandas` prefix, use DataFrame.rename_axis instead.
pandas.Series.tail: pandas.DataFrame.head in `See Also` section does not need `pandas` prefix, use DataFrame.head instead.
pandas.Series.filter: pandas.DataFrame.loc in `See Also` section does not need `pandas` prefix, use DataFrame.loc instead.
pandas.Series.append: pandas.concat in `See Also` section does not need `pandas` prefix, use concat instead.
pandas.Series.dt.to_period: pandas.PeriodIndex in `See Also` section does not need `pandas` prefix, use PeriodIndex instead.
pandas.Series.dt.to_period: pandas.DatetimeIndex.to_pydatetime in `See Also` section does not need `pandas` prefix, use DatetimeIndex.to_pydatetime instead.
pandas.Series.dt.strftime: pandas.to_datetime in `See Also` section does not need `pandas` prefix, use to_datetime instead.
pandas.Series.str.get_dummies: pandas.get_dummies in `See Also` section does not need `pandas` prefix, use get_dummies instead.
pandas.Series.to_csv: pandas.read_csv in `See Also` section does not need `pandas` prefix, use read_csv instead.
pandas.Series.to_csv: pandas.to_excel in `See Also` section does not need `pandas` prefix, use to_excel instead.
pandas.Series.to_excel: pandas.read_excel in `See Also` section does not need `pandas` prefix, use read_excel instead.
pandas.Series.to_excel: pandas.ExcelWriter in `See Also` section does not need `pandas` prefix, use ExcelWriter instead.
pandas.Series.to_sql: pandas.read_sql in `See Also` section does not need `pandas` prefix, use read_sql instead.
pandas.Series.to_json: pandas.read_json in `See Also` section does not need `pandas` prefix, use read_json instead.
pandas.Series.as_matrix: pandas.DataFrame.values in `See Also` section does not need `pandas` prefix, use DataFrame.values instead.
pandas.Series.from_csv: pandas.read_csv in `See Also` section does not need `pandas` prefix, use read_csv instead.
```

In this issue we should fix all these cases, and check that the previous command don't report any case.