def concat(objs: List): """ Concat the results of partitioned dask task executions. This function guess the types of resulting list, then calls the corresponding native dask concat functions. Parameters ---------- objs: List List of the partitioned dask task execution results, which will be concat. Returns ------- obj: The concat result """ if is_arraylike(objs[0]): res = array_concat(objs, axes=[0]) # TODO: Add concat with args support elif any((is_dataframe_like(objs[0]), is_series_like(objs[0]), is_index_like(objs[0]))): res = df_concat(objs) else: res = objs return res.compute() if is_dask_collection(res) else res
def assert_dask_dtypes(ddf, res, numeric_equal=True): """Check that the dask metadata matches the result. If `numeric_equal`, integer and floating dtypes compare equal. This is useful due to the implicit conversion of integer to floating upon encountering missingness, which is hard to infer statically.""" eq_type_sets = [{"O", "S", "U", "a"}] # treat object and strings alike if numeric_equal: eq_type_sets.append({"i", "f", "u"}) def eq_dtypes(a, b): return any(a.kind in eq_types and b.kind in eq_types for eq_types in eq_type_sets) or (a == b) if not is_dask_collection(res) and is_dataframe_like(res): for col, a, b in pd.concat([ddf._meta.dtypes, res.dtypes], axis=1).itertuples(): assert eq_dtypes(a, b) elif not is_dask_collection(res) and (is_index_like(res) or is_series_like(res)): a = ddf._meta.dtype b = res.dtype assert eq_dtypes(a, b) else: if hasattr(ddf._meta, "dtype"): a = ddf._meta.dtype if not hasattr(res, "dtype"): assert np.isscalar(res) b = np.dtype(type(res)) else: b = res.dtype assert eq_dtypes(a, b) else: assert type(ddf._meta) == type(res)
def index(x): if is_index_like(x): return x try: return x.index.get_level_values(0) except AttributeError: return x.index
def has_known_categories(x): """Returns whether the categories in `x` are known. Parameters ---------- x : Series or CategoricalIndex """ x = getattr(x, "_meta", x) if is_series_like(x): return UNKNOWN_CATEGORIES not in x.cat.categories elif is_index_like(x) and hasattr(x, "categories"): return UNKNOWN_CATEGORIES not in x.categories raise TypeError("Expected Series or CategoricalIndex")
def check_meta(x, meta, funcname=None, numeric_equal=True): """Check that the dask metadata matches the result. If metadata matches, ``x`` is passed through unchanged. A nice error is raised if metadata doesn't match. Parameters ---------- x : DataFrame, Series, or Index meta : DataFrame, Series, or Index The expected metadata that ``x`` should match funcname : str, optional The name of the function in which the metadata was specified. If provided, the function name will be included in the error message to be more helpful to users. numeric_equal : bool, optionl If True, integer and floating dtypes compare equal. This is useful due to panda's implicit conversion of integer to floating upon encountering missingness, which is hard to infer statically. """ eq_types = {"i", "f", "u"} if numeric_equal else set() def equal_dtypes(a, b): if is_categorical_dtype(a) != is_categorical_dtype(b): return False if isinstance(a, str) and a == "-" or isinstance(b, str) and b == "-": return False if is_categorical_dtype(a) and is_categorical_dtype(b): if UNKNOWN_CATEGORIES in a.categories or UNKNOWN_CATEGORIES in b.categories: return True return a == b return (a.kind in eq_types and b.kind in eq_types) or is_dtype_equal( a, b) if not (is_dataframe_like(meta) or is_series_like(meta) or is_index_like(meta)) or is_dask_collection(meta): raise TypeError("Expected partition to be DataFrame, Series, or " "Index, got `%s`" % typename(type(meta))) # Notice, we use .__class__ as opposed to type() in order to support # object proxies see <https://github.com/dask/dask/pull/6981> if x.__class__ != meta.__class__: errmsg = "Expected partition of type `{}` but got `{}`".format( typename(type(meta)), typename(type(x)), ) elif is_dataframe_like(meta): dtypes = pd.concat([x.dtypes, meta.dtypes], axis=1, sort=True) bad_dtypes = [(repr(col), a, b) for col, a, b in dtypes.fillna("-").itertuples() if not equal_dtypes(a, b)] if bad_dtypes: errmsg = "Partition type: `{}`\n{}".format( typename(type(meta)), asciitable(["Column", "Found", "Expected"], bad_dtypes), ) else: check_matching_columns(meta, x) return x else: if equal_dtypes(x.dtype, meta.dtype): return x errmsg = "Partition type: `{}`\n{}".format( typename(type(meta)), asciitable(["", "dtype"], [("Found", x.dtype), ("Expected", meta.dtype)]), ) raise ValueError("Metadata mismatch found%s.\n\n" "%s" % ((" in `%s`" % funcname if funcname else ""), errmsg))