Function make_meta_object

dask/dataframe/backends.py:279–341

Create an empty pandas object containing the desired metadata. Parameters ---------- x : dict, tuple, list, pd.Series, pd.DataFrame, pd.Index, dtype, scalar To create a DataFrame, provide a `dict` mapping of `{name: dtype}`, or an iterable of `(name, dtype)` tuples. To c

(x, index=None)

@make_meta_obj.register(meta_object_types)
def make_meta_object(x, index=None):
    """Create an empty pandas object containing the desired metadata.

    Parameters
    ----------
    x : dict, tuple, list, pd.Series, pd.DataFrame, pd.Index, dtype, scalar
        To create a DataFrame, provide a `dict` mapping of `{name: dtype}`, or
        an iterable of `(name, dtype)` tuples. To create a `Series`, provide a
        tuple of `(name, dtype)`. If a pandas object, names, dtypes, and index
        should match the desired output. If a dtype or scalar, a scalar of the
        same dtype is returned.
    index :  pd.Index, optional
        Any pandas index to use in the metadata. If none provided, a
        `RangeIndex` will be used.

    Examples
    --------

    >>> make_meta_object([('a', 'i8'), ('b', 'O')])
    Empty DataFrame
    Columns: [a, b]
    Index: []
    >>> make_meta_object(('a', 'f8'))
    Series([], Name: a, dtype: float64)
    >>> make_meta_object('i8')
    np.int64(1)
    """

    if is_arraylike(x) and x.shape:
        return x[:0]

    if index is not None:
        index = make_meta_dispatch(index)

    if isinstance(x, dict):
        return pd.DataFrame(
            {c: _empty_series(c, d, index=index) for (c, d) in x.items()}, index=index
        )
    if isinstance(x, tuple) and len(x) == 2:
        return _empty_series(x[0], x[1], index=index)
    elif isinstance(x, Iterable) and not isinstance(x, str):
        if not all(isinstance(i, tuple) and len(i) == 2 for i in x):
            raise ValueError(f"Expected iterable of tuples of (name, dtype), got {x}")
        return pd.DataFrame(
            {c: _empty_series(c, d, index=index) for (c, d) in x},
            columns=[c for c, d in x],
            index=index,
        )
    elif not hasattr(x, "dtype") and x is not None:
        # could be a string, a dtype object, or a python type. Skip `None`,
        # because it is implicitly converted to `dtype('f8')`, which we don't
        # want here.
        try:
            dtype = np.dtype(x)
            return _scalar_from_dtype(dtype)
        except Exception:
            # Continue on to next check
            pass