Python types.is_integer方法代碼示例

# 需要導入模塊: from pandas.api import types [as 別名]
# 或者: from pandas.api.types import is_integer [as 別名]
def test_conversions(data_missing):

    # astype to object series
    df = pd.DataFrame({'A': data_missing})
    result = df['A'].astype('object')
    expected = pd.Series(np.array([np.nan, 1], dtype=object), name='A')
    tm.assert_series_equal(result, expected)

    # convert to object ndarray
    # we assert that we are exactly equal
    # including type conversions of scalars
    result = df['A'].astype('object').values
    expected = np.array([np.nan, 1], dtype=object)
    tm.assert_numpy_array_equal(result, expected)

    for r, e in zip(result, expected):
        if pd.isnull(r):
            assert pd.isnull(e)
        elif is_integer(r):
            # PY2 can be int or long
            assert r == e
            assert is_integer(e)
        else:
            assert r == e
            assert type(r) == type(e) 

# 需要導入模塊: from pandas.api import types [as 別名]
# 或者: from pandas.api.types import is_integer [as 別名]
def _random_state(state=None):
    """
    Helper function for processing random_state arguments.

    Parameters
    ----------
    state : int, np.random.RandomState, None.
        If receives an int, passes to np.random.RandomState() as seed.
        If receives an np.random.RandomState object, just returns object.
        If receives `None`, returns np.random.
        If receives anything else, raises an informative ValueError.
        Default None.

    Returns
    -------
    np.random.RandomState
    """

    if types.is_integer(state):
        return np.random.RandomState(state)
    elif isinstance(state, np.random.RandomState):
        return state
    elif state is None:
        return np.random
    else:
        raise ValueError("random_state must be an integer, a numpy "
                         "RandomState, or None") 

# 需要導入模塊: from pandas.api import types [as 別名]
# 或者: from pandas.api.types import is_integer [as 別名]
def get_meta(
    columns, dtype=None, index_columns=None, index_names=None, default_dtype=np.object
):  # pragma: no cover
    """
    Extracted and modified from pandas/io/parsers.py :
        _get_empty_meta (BSD licensed).

    """
    columns = list(columns)

    # Convert `dtype` to a defaultdict of some kind.
    # This will enable us to write `dtype[col_name]`
    # without worrying about KeyError issues later on.
    if not isinstance(dtype, dict):
        # if dtype == None, default will be default_dtype.
        dtype = defaultdict(lambda: dtype or default_dtype)
    else:
        # Save a copy of the dictionary.
        _dtype = dtype.copy()
        dtype = defaultdict(lambda: default_dtype)

        # Convert column indexes to column names.
        for k, v in six.iteritems(_dtype):
            col = columns[k] if is_integer(k) else k
            dtype[col] = v

    if index_columns is None or index_columns is False:
        index = pd.Index([])
    else:
        data = [pd.Series([], dtype=dtype[name]) for name in index_names]
        if len(data) == 1:
            index = pd.Index(data[0], name=index_names[0])
        else:
            index = pd.MultiIndex.from_arrays(data, names=index_names)
        index_columns.sort()
        for i, n in enumerate(index_columns):
            columns.pop(n - i)

    col_dict = {col_name: pd.Series([], dtype=dtype[col_name]) for col_name in columns}

    return pd.DataFrame(col_dict, columns=columns, index=index) 

# 需要導入模塊: from pandas.api import types [as 別名]
# 或者: from pandas.api.types import is_integer [as 別名]
def _rename_chroms(grp, rename_dict, h5opts):
    chroms = get(grp["chroms"]).set_index("name")
    n_chroms = len(chroms)
    new_names = np.array(
        chroms.rename(rename_dict).index.values, dtype=CHROM_DTYPE
    )  # auto-adjusts char length

    del grp["chroms/name"]
    grp["chroms"].create_dataset(
        "name", shape=(n_chroms,), dtype=new_names.dtype, data=new_names, **h5opts
    )

    bins = get(grp["bins"])
    n_bins = len(bins)
    idmap = dict(zip(new_names, range(n_chroms)))
    if is_categorical(bins["chrom"]) or is_integer(bins["chrom"]):
        chrom_ids = bins["chrom"].cat.codes
        chrom_dtype = h5py.special_dtype(enum=(CHROMID_DTYPE, idmap))
        del grp["bins/chrom"]
        try:
            grp["bins"].create_dataset(
                "chrom", shape=(n_bins,), dtype=chrom_dtype, data=chrom_ids, **h5opts
            )
        except ValueError:
            # If HDF5 enum header would be too large,
            # try storing chrom IDs as raw int instead
            chrom_dtype = CHROMID_DTYPE
            grp["bins"].create_dataset(
                "chrom", shape=(n_bins,), dtype=chrom_dtype, data=chrom_ids, **h5opts
            )