Python pandas.core.dtypes.common._NS_DTYPE Examples

def _hash_categories(categories, ordered=True):
        from pandas.core.util.hashing import (
            hash_array, _combine_hash_arrays, hash_tuples
        )
        from pandas.core.dtypes.common import is_datetime64tz_dtype, _NS_DTYPE

        if len(categories) and isinstance(categories[0], tuple):
            # assumes if any individual category is a tuple, then all our. ATM
            # I don't really want to support just some of the categories being
            # tuples.
            categories = list(categories)  # breaks if a np.array of categories
            cat_array = hash_tuples(categories)
        else:
            if categories.dtype == 'O':
                types = [type(x) for x in categories]
                if not len(set(types)) == 1:
                    # TODO: hash_array doesn't handle mixed types. It casts
                    # everything to a str first, which means we treat
                    # {'1', '2'} the same as {'1', 2}
                    # find a better solution
                    hashed = hash((tuple(categories), ordered))
                    return hashed

            if is_datetime64tz_dtype(categories.dtype):
                # Avoid future warning.
                categories = categories.astype(_NS_DTYPE)

            cat_array = hash_array(np.asarray(categories), categorize=False)
        if ordered:
            cat_array = np.vstack([
                cat_array, np.arange(len(cat_array), dtype=cat_array.dtype)
            ])
        else:
            cat_array = [cat_array]
        hashed = _combine_hash_arrays(iter(cat_array),
                                      num_items=len(cat_array))
        return np.bitwise_xor.reduce(hashed) 

def _where_compat(mask, arr1, arr2):
    if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE:
        new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8'))
        return new_vals.view(_NS_DTYPE)

    if arr1.dtype == _NS_DTYPE:
        arr1 = tslib.ints_to_pydatetime(arr1.view('i8'))
    if arr2.dtype == _NS_DTYPE:
        arr2 = tslib.ints_to_pydatetime(arr2.view('i8'))

    return np.where(mask, arr1, arr2) 

def _unpickle_array(bytes):
    arr = read_array(BytesIO(bytes))

    # All datetimes should be stored as M8[ns].  When unpickling with
    # numpy1.6, it will read these as M8[us].  So this ensures all
    # datetime64 types are read as MS[ns]
    if is_datetime64_dtype(arr):
        arr = arr.view(_NS_DTYPE)

    return arr 

def _hash_categories(categories, ordered=True):
        from pandas.core.util.hashing import (
            hash_array, _combine_hash_arrays, hash_tuples
        )
        from pandas.core.dtypes.common import is_datetime64tz_dtype, _NS_DTYPE

        if len(categories) and isinstance(categories[0], tuple):
            # assumes if any individual category is a tuple, then all our. ATM
            # I don't really want to support just some of the categories being
            # tuples.
            categories = list(categories)  # breaks if a np.array of categories
            cat_array = hash_tuples(categories)
        else:
            if categories.dtype == 'O':
                types = [type(x) for x in categories]
                if not len(set(types)) == 1:
                    # TODO: hash_array doesn't handle mixed types. It casts
                    # everything to a str first, which means we treat
                    # {'1', '2'} the same as {'1', 2}
                    # find a better solution
                    hashed = hash((tuple(categories), ordered))
                    return hashed

            if is_datetime64tz_dtype(categories.dtype):
                # Avoid future warning.
                categories = categories.astype(_NS_DTYPE)

            cat_array = hash_array(np.asarray(categories), categorize=False)
        if ordered:
            cat_array = np.vstack([
                cat_array, np.arange(len(cat_array), dtype=cat_array.dtype)
            ])
        else:
            cat_array = [cat_array]
        hashed = _combine_hash_arrays(iter(cat_array),
                                      num_items=len(cat_array))
        return np.bitwise_xor.reduce(hashed) 

def _where_compat(mask, arr1, arr2):
    if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE:
        new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8'))
        return new_vals.view(_NS_DTYPE)

    if arr1.dtype == _NS_DTYPE:
        arr1 = tslib.ints_to_pydatetime(arr1.view('i8'))
    if arr2.dtype == _NS_DTYPE:
        arr2 = tslib.ints_to_pydatetime(arr2.view('i8'))

    return np.where(mask, arr1, arr2) 

def _unpickle_array(bytes):
    arr = read_array(BytesIO(bytes))

    # All datetimes should be stored as M8[ns].  When unpickling with
    # numpy1.6, it will read these as M8[us].  So this ensures all
    # datetime64 types are read as MS[ns]
    if is_datetime64_dtype(arr):
        arr = arr.view(_NS_DTYPE)

    return arr 

def _where_compat(mask, arr1, arr2):
    if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE:
        new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8'))
        return new_vals.view(_NS_DTYPE)

    if arr1.dtype == _NS_DTYPE:
        arr1 = tslib.ints_to_pydatetime(arr1.view('i8'))
    if arr2.dtype == _NS_DTYPE:
        arr2 = tslib.ints_to_pydatetime(arr2.view('i8'))

    return np.where(mask, arr1, arr2) 

def _unpickle_array(bytes):
    arr = read_array(BytesIO(bytes))

    # All datetimes should be stored as M8[ns].  When unpickling with
    # numpy1.6, it will read these as M8[us].  So this ensures all
    # datetime64 types are read as MS[ns]
    if is_datetime64_dtype(arr):
        arr = arr.view(_NS_DTYPE)

    return arr