from __future__ import absolute_import, division, print_function import math import numbers import re import textwrap from distutils.version import LooseVersion import sys import traceback from contextlib import contextmanager import numpy as np import pandas as pd import pandas.util.testing as tm from pandas.api.types import (is_categorical_dtype, is_scalar, is_sparse, is_period_dtype, is_datetime64tz_dtype) try: from pandas.api.types import is_interval_dtype except ImportError: is_interval_dtype = lambda dtype: False from .extensions import make_array_nonempty from ..base import is_dask_collection from ..compatibility import PY2, Iterator, Mapping from ..core import get_deps from ..local import get_sync from ..utils import asciitable, is_arraylike, Dispatch PANDAS_VERSION = LooseVersion(pd.__version__) PANDAS_GT_0240 = PANDAS_VERSION >= LooseVersion("0.24.0rc1") HAS_INT_NA = PANDAS_GT_0240 def is_integer_na_dtype(t): dtype = getattr(t, 'dtype', t) if HAS_INT_NA: types = ( pd.Int8Dtype, pd.Int16Dtype, pd.Int32Dtype, pd.Int64Dtype, pd.UInt8Dtype, pd.UInt16Dtype, pd.UInt32Dtype, pd.UInt64Dtype, ) else: types = () return isinstance(dtype, types) def shard_df_on_index(df, divisions): """ Shard a DataFrame by ranges on its index Examples -------- >>> df = pd.DataFrame({'a': [0, 10, 20, 30, 40], 'b': [5, 4 ,3, 2, 1]}) >>> df a b 0 0 5 1 10 4 2 20 3 3 30 2 4 40 1 >>> shards = list(shard_df_on_index(df, [2, 4])) >>> shards[0] a b 0 0 5 1 10 4 >>> shards[1] a b 2 20 3 3 30 2 >>> shards[2] a b 4 40 1 >>> list(shard_df_on_index(df, []))[0] # empty case a b 0 0 5 1 10 4 2 20 3 3 30 2 4 40 1 """ if isinstance(divisions, Iterator): divisions = list(divisions) if not len(divisions): yield df else: divisions = np.array(divisions) df = df.sort_index() index = df.index if is_categorical_dtype(index): index = index.as_ordered() indices = index.searchsorted(divisions) yield df.iloc[:indices[0]] for i in range(len(indices) - 1): yield df.iloc[indices[i]: indices[i + 1]] yield df.iloc[indices[-1]:] _META_TYPES = "meta : pd.DataFrame, pd.Series, dict, iterable, tuple, optional" _META_DESCRIPTION = """\ An empty ``pd.DataFrame`` or ``pd.Series`` that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a ``DataFrame``, a ``dict`` of ``{name: dtype}`` or iterable of ``(name, dtype)`` can be provided. Instead of a series, a tuple of ``(name, dtype)`` can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing ``meta`` is recommended. For more information, see ``dask.dataframe.utils.make_meta``. """ def insert_meta_param_description(*args, **kwargs): """Replace `$META` in docstring with param description. If pad keyword is provided, will pad description by that number of spaces (default is 8).""" if not args: return lambda f: insert_meta_param_description(f, **kwargs) f = args[0] indent = " " * kwargs.get('pad', 8) body = textwrap.wrap(_META_DESCRIPTION, initial_indent=indent, subsequent_indent=indent, width=78) descr = '{0}\n{1}'.format(_META_TYPES, '\n'.join(body)) if f.__doc__: if '$META' in f.__doc__: f.__doc__ = f.__doc__.replace('$META', descr) else: # Put it at the end of the parameters section parameter_header = 'Parameters\n%s----------' % indent[4:] first, last = re.split('Parameters\\n[ ]*----------', f.__doc__) parameters, rest = last.split('\n\n', 1) f.__doc__ = '{0}{1}{2}\n{3}{4}\n\n{5}'.format(first, parameter_header, parameters, indent[4:], descr, rest) return f @contextmanager def raise_on_meta_error(funcname=None, udf=False): """Reraise errors in this block to show metadata inference failure. Parameters ---------- funcname : str, optional If provided, will be added to the error message to indicate the name of the method that failed. """ try: yield except Exception as e: exc_type, exc_value, exc_traceback = sys.exc_info() tb = ''.join(traceback.format_tb(exc_traceback)) msg = "Metadata inference failed{0}.\n\n" if udf: msg += ("You have supplied a custom function and Dask is unable to \n" "determine the type of output that that function returns. \n\n" "To resolve this please provide a meta= keyword.\n" "The docstring of the Dask function you ran should have more information.\n\n") msg += ("Original error is below:\n" "------------------------\n" "{1}\n\n" "Traceback:\n" "---------\n" "{2}") msg = msg.format(" in `{0}`".format(funcname) if funcname else "", repr(e), tb) raise ValueError(msg) UNKNOWN_CATEGORIES = '__UNKNOWN_CATEGORIES__' def has_known_categories(x): """Returns whether the categories in `x` are known. Parameters ---------- x : Series or CategoricalIndex """ x = getattr(x, '_meta', x) if isinstance(x, pd.Series): return UNKNOWN_CATEGORIES not in x.cat.categories elif isinstance(x, pd.CategoricalIndex): return UNKNOWN_CATEGORIES not in x.categories raise TypeError("Expected Series or CategoricalIndex") def strip_unknown_categories(x): """Replace any unknown categoricals with empty categoricals. Useful for preventing ``UNKNOWN_CATEGORIES`` from leaking into results. """ if isinstance(x, (pd.Series, pd.DataFrame)): x = x.copy() if isinstance(x, pd.DataFrame): cat_mask = x.dtypes == 'category' if cat_mask.any(): cats = cat_mask[cat_mask].index for c in cats: if not has_known_categories(x[c]): x[c].cat.set_categories([], inplace=True) elif isinstance(x, pd.Series): if is_categorical_dtype(x.dtype) and not has_known_categories(x): x.cat.set_categories([], inplace=True) if (isinstance(x.index, pd.CategoricalIndex) and not has_known_categories(x.index)): x.index = x.index.set_categories([]) elif isinstance(x, pd.CategoricalIndex) and not has_known_categories(x): x = x.set_categories([]) return x def clear_known_categories(x, cols=None, index=True): """Set categories to be unknown. Parameters ---------- x : DataFrame, Series, Index cols : iterable, optional If x is a DataFrame, set only categoricals in these columns to unknown. By default, all categorical columns are set to unknown categoricals index : bool, optional If True and x is a Series or DataFrame, set the clear known categories in the index as well. """ if isinstance(x, (pd.Series, pd.DataFrame)): x = x.copy() if isinstance(x, pd.DataFrame): mask = x.dtypes == 'category' if cols is None: cols = mask[mask].index elif not mask.loc[cols].all(): raise ValueError("Not all columns are categoricals") for c in cols: x[c].cat.set_categories([UNKNOWN_CATEGORIES], inplace=True) elif isinstance(x, pd.Series): if is_categorical_dtype(x.dtype): x.cat.set_categories([UNKNOWN_CATEGORIES], inplace=True) if index and isinstance(x.index, pd.CategoricalIndex): x.index = x.index.set_categories([UNKNOWN_CATEGORIES]) elif isinstance(x, pd.CategoricalIndex): x = x.set_categories([UNKNOWN_CATEGORIES]) return x def _empty_series(name, dtype, index=None): if isinstance(dtype, str) and dtype == 'category': return pd.Series(pd.Categorical([UNKNOWN_CATEGORIES]), name=name, index=index).iloc[:0] return pd.Series([], dtype=dtype, name=name, index=index) make_meta = Dispatch('make_meta') @make_meta.register((pd.Series, pd.DataFrame)) def make_meta_pandas(x, index=None): return x.iloc[:0] @make_meta.register(pd.Index) def make_meta_index(x, index=None): return x[0:0] @make_meta.register(object) 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([('a', 'i8'), ('b', 'O')]) Empty DataFrame Columns: [a, b] Index: [] >>> make_meta(('a', 'f8')) Series([], Name: a, dtype: float64) >>> make_meta('i8') 1 """ if hasattr(x, '_meta'): return x._meta elif is_arraylike(x): return x[:0] if index is not None: index = make_meta(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, (list, tuple)): if not all(isinstance(i, tuple) and len(i) == 2 for i in x): raise ValueError("Expected iterable of tuples of (name, dtype), " "got {0}".format(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 implictly 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 if is_scalar(x): return _nonempty_scalar(x) raise TypeError("Don't know how to create metadata from {0}".format(x)) _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index) meta_nonempty = Dispatch('meta_nonempty') @meta_nonempty.register(object) def meta_nonempty_object(x): """Create a nonempty pandas object from the given metadata. Returns a pandas DataFrame, Series, or Index that contains two rows of fake data. """ if is_scalar(x): return _nonempty_scalar(x) else: raise TypeError("Expected Index, Series, DataFrame, or scalar, " "got {0}".format(type(x).__name__)) @meta_nonempty.register(pd.DataFrame) def meta_nonempty_dataframe(x): idx = meta_nonempty(x.index) data = {i: _nonempty_series(x.iloc[:, i], idx=idx) for i, c in enumerate(x.columns)} res = pd.DataFrame(data, index=idx, columns=np.arange(len(x.columns))) res.columns = x.columns return res @meta_nonempty.register(pd.Index) def _nonempty_index(idx): typ = type(idx) if typ is pd.RangeIndex: return pd.RangeIndex(2, name=idx.name) elif typ in _numeric_index_types: return typ([1, 2], name=idx.name) elif typ is pd.Index: return pd.Index(['a', 'b'], name=idx.name) elif typ is pd.DatetimeIndex: start = '1970-01-01' # Need a non-monotonic decreasing index to avoid issues with # partial string indexing see https://github.com/dask/dask/issues/2389 # and https://github.com/pandas-dev/pandas/issues/16515 # This doesn't mean `_meta_nonempty` should ever rely on # `self.monotonic_increasing` or `self.monotonic_decreasing` try: return pd.date_range(start=start, periods=2, freq=idx.freq, tz=idx.tz, name=idx.name) except ValueError: # older pandas versions data = [start, '1970-01-02'] if idx.freq is None else None return pd.DatetimeIndex(data, start=start, periods=2, freq=idx.freq, tz=idx.tz, name=idx.name) elif typ is pd.PeriodIndex: return pd.period_range(start='1970-01-01', periods=2, freq=idx.freq, name=idx.name) elif typ is pd.TimedeltaIndex: start = np.timedelta64(1, 'D') try: return pd.timedelta_range(start=start, periods=2, freq=idx.freq, name=idx.name) except ValueError: # older pandas versions start = np.timedelta64(1, 'D') data = [start, start + 1] if idx.freq is None else None return pd.TimedeltaIndex(data, start=start, periods=2, freq=idx.freq, name=idx.name) elif typ is pd.CategoricalIndex: if len(idx.categories) == 0: data = pd.Categorical(_nonempty_index(idx.categories), ordered=idx.ordered) else: data = pd.Categorical.from_codes( [-1, 0], categories=idx.categories, ordered=idx.ordered) return pd.CategoricalIndex(data, name=idx.name) elif typ is pd.MultiIndex: levels = [_nonempty_index(l) for l in idx.levels] codes = [[0, 0] for i in idx.levels] try: return pd.MultiIndex(levels=levels, codes=codes, names=idx.names) except TypeError: # older pandas versions return pd.MultiIndex(levels=levels, labels=codes, names=idx.names) raise TypeError("Don't know how to handle index of " "type {0}".format(type(idx).__name__)) _simple_fake_mapping = { 'b': np.bool_(True), 'V': np.void(b' '), 'M': np.datetime64('1970-01-01'), 'm': np.timedelta64(1), 'S': np.str_('foo'), 'a': np.str_('foo'), 'U': np.unicode_('foo'), 'O': 'foo' } def _scalar_from_dtype(dtype): if dtype.kind in ('i', 'f', 'u'): return dtype.type(1) elif dtype.kind == 'c': return dtype.type(complex(1, 0)) elif dtype.kind in _simple_fake_mapping: o = _simple_fake_mapping[dtype.kind] return o.astype(dtype) if dtype.kind in ('m', 'M') else o else: raise TypeError("Can't handle dtype: {0}".format(dtype)) def _nonempty_scalar(x): if isinstance(x, (pd.Timestamp, pd.Timedelta, pd.Period)): return x elif np.isscalar(x): dtype = x.dtype if hasattr(x, 'dtype') else np.dtype(type(x)) return _scalar_from_dtype(dtype) else: raise TypeError("Can't handle meta of type " "'{0}'".format(type(x).__name__)) @meta_nonempty.register(pd.Series) def _nonempty_series(s, idx=None): # TODO: Use register dtypes with make_array_nonempty if idx is None: idx = _nonempty_index(s.index) dtype = s.dtype if is_datetime64tz_dtype(dtype): entry = pd.Timestamp('1970-01-01', tz=dtype.tz) data = [entry, entry] elif is_categorical_dtype(dtype): if len(s.cat.categories): data = [s.cat.categories[0]] * 2 cats = s.cat.categories else: data = _nonempty_index(s.cat.categories) cats = None data = pd.Categorical(data, categories=cats, ordered=s.cat.ordered) elif is_integer_na_dtype(dtype): data = pd.array([1, None], dtype=dtype) elif is_period_dtype(dtype): # pandas 0.24.0+ should infer this to be Series[Period[freq]] freq = dtype.freq data = [pd.Period('2000', freq), pd.Period('2001', freq)] elif is_sparse(dtype): # TODO: pandas <0.24 # Pandas <= 0.23.4: if PANDAS_GT_0240: entry = _scalar_from_dtype(dtype.subtype) else: entry = _scalar_from_dtype(dtype.subtype) data = pd.SparseArray([entry, entry], dtype=dtype) elif is_interval_dtype(dtype): entry = _scalar_from_dtype(dtype.subtype) if PANDAS_GT_0240: data = pd.array([entry, entry], dtype=dtype) else: data = np.array([entry, entry], dtype=dtype) elif type(dtype) in make_array_nonempty._lookup: data = make_array_nonempty(dtype) else: entry = _scalar_from_dtype(dtype) data = np.array([entry, entry], dtype=dtype) return pd.Series(data, name=s.name, index=idx) def is_dataframe_like(df): """ Looks like a Pandas DataFrame """ typ = type(df) return (all(hasattr(typ, name) for name in ('groupby', 'head', 'merge', 'mean')) and all(hasattr(df, name) for name in ('dtypes',)) and not any(hasattr(typ, name) for name in ('value_counts', 'dtype'))) def is_series_like(s): """ Looks like a Pandas Series """ typ = type(s) return (all(hasattr(typ, name) for name in ('groupby', 'head', 'mean')) and all(hasattr(s, name) for name in ('dtype', 'name')) and 'index' not in typ.__name__.lower()) def is_index_like(s): """ Looks like a Pandas Index """ typ = type(s) return (all(hasattr(s, name) for name in ('name', 'dtype')) and 'index' in typ.__name__.lower()) 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 (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`" % type(meta).__name__) if type(x) != type(meta): errmsg = ("Expected partition of type `%s` but got " "`%s`" % (type(meta).__name__, type(x).__name__)) elif is_dataframe_like(meta): kwargs = dict() if PANDAS_VERSION >= '0.23.0': kwargs['sort'] = True dtypes = pd.concat([x.dtypes, meta.dtypes], axis=1, **kwargs) bad_dtypes = [(col, a, b) for col, a, b in dtypes.fillna('-').itertuples() if not equal_dtypes(a, b)] if bad_dtypes: errmsg = ("Partition type: `%s`\n%s" % (type(meta).__name__, asciitable(['Column', 'Found', 'Expected'], bad_dtypes))) elif not np.array_equal(np.nan_to_num(meta.columns), np.nan_to_num(x.columns)): errmsg = ("The columns in the computed data do not match" " the columns in the provided metadata.\n" " %s\n :%s" % (meta.columns, x.columns)) else: return x else: if equal_dtypes(x.dtype, meta.dtype): return x errmsg = ("Partition type: `%s`\n%s" % (type(meta).__name__, asciitable(['', 'dtype'], [('Found', x.dtype), ('Expected', meta.dtype)]))) raise ValueError("Metadata mismatch found%s.\n\n" "%s" % ((" in `%s`" % funcname if funcname else ""), errmsg)) def index_summary(idx, name=None): """Summarized representation of an Index. """ n = len(idx) if name is None: name = idx.__class__.__name__ if n: head = idx[0] tail = idx[-1] summary = ', {} to {}'.format(head, tail) else: summary = '' return "{}: {} entries{}".format(name, n, summary) ############################################################### # Testing ############################################################### def _check_dask(dsk, check_names=True, check_dtypes=True, result=None): import dask.dataframe as dd if hasattr(dsk, 'dask'): if result is None: result = dsk.compute(scheduler='sync') if isinstance(dsk, dd.Index): assert 'Index' in type(result).__name__, type(result) # assert type(dsk._meta) == type(result), type(dsk._meta) if check_names: assert dsk.name == result.name assert dsk._meta.name == result.name if isinstance(result, pd.MultiIndex): assert result.names == dsk._meta.names if check_dtypes: assert_dask_dtypes(dsk, result) elif isinstance(dsk, dd.Series): assert 'Series' in type(result).__name__, type(result) assert type(dsk._meta) == type(result), type(dsk._meta) if check_names: assert dsk.name == result.name, (dsk.name, result.name) assert dsk._meta.name == result.name if check_dtypes: assert_dask_dtypes(dsk, result) _check_dask(dsk.index, check_names=check_names, check_dtypes=check_dtypes, result=result.index) elif isinstance(dsk, dd.DataFrame): assert 'DataFrame' in type(result).__name__, type(result) assert isinstance(dsk.columns, pd.Index), type(dsk.columns) assert type(dsk._meta) == type(result), type(dsk._meta) if check_names: tm.assert_index_equal(dsk.columns, result.columns) tm.assert_index_equal(dsk._meta.columns, result.columns) if check_dtypes: assert_dask_dtypes(dsk, result) _check_dask(dsk.index, check_names=check_names, check_dtypes=check_dtypes, result=result.index) elif isinstance(dsk, dd.core.Scalar): assert (np.isscalar(result) or isinstance(result, (pd.Timestamp, pd.Timedelta))) if check_dtypes: assert_dask_dtypes(dsk, result) else: msg = 'Unsupported dask instance {0} found'.format(type(dsk)) raise AssertionError(msg) return result return dsk def _maybe_sort(a): # sort by value, then index try: if is_dataframe_like(a): if set(a.index.names) & set(a.columns): a.index.names = ['-overlapped-index-name-%d' % i for i in range(len(a.index.names))] a = a.sort_values(by=a.columns.tolist()) else: a = a.sort_values() except (TypeError, IndexError, ValueError): pass return a.sort_index() def assert_eq(a, b, check_names=True, check_dtypes=True, check_divisions=True, check_index=True, **kwargs): if check_divisions: assert_divisions(a) assert_divisions(b) if hasattr(a, 'divisions') and hasattr(b, 'divisions'): at = type(np.asarray(a.divisions).tolist()[0]) # numpy to python bt = type(np.asarray(b.divisions).tolist()[0]) # scalar conversion assert at == bt, (at, bt) assert_sane_keynames(a) assert_sane_keynames(b) a = _check_dask(a, check_names=check_names, check_dtypes=check_dtypes) b = _check_dask(b, check_names=check_names, check_dtypes=check_dtypes) if not check_index: a = a.reset_index(drop=True) b = b.reset_index(drop=True) if hasattr(a, 'to_pandas'): a = a.to_pandas() if hasattr(b, 'to_pandas'): b = b.to_pandas() if isinstance(a, pd.DataFrame): a = _maybe_sort(a) b = _maybe_sort(b) tm.assert_frame_equal(a, b, **kwargs) elif isinstance(a, pd.Series): a = _maybe_sort(a) b = _maybe_sort(b) tm.assert_series_equal(a, b, check_names=check_names, **kwargs) elif isinstance(a, pd.Index): tm.assert_index_equal(a, b, **kwargs) else: if a == b: return True else: if np.isnan(a): assert np.isnan(b) else: assert np.allclose(a, b) return True def assert_dask_graph(dask, label): if hasattr(dask, 'dask'): dask = dask.dask assert isinstance(dask, Mapping) for k in dask: if isinstance(k, tuple): k = k[0] if k.startswith(label): return True raise AssertionError("given dask graph doesn't contain label: {label}" .format(label=label)) def assert_divisions(ddf): if not hasattr(ddf, 'divisions'): return if not getattr(ddf, 'known_divisions', False): return def index(x): if is_index_like(x): return x try: return x.index.get_level_values(0) except AttributeError: return x.index results = get_sync(ddf.dask, ddf.__dask_keys__()) for i, df in enumerate(results[:-1]): if len(df): assert index(df).min() >= ddf.divisions[i] assert index(df).max() < ddf.divisions[i + 1] if len(results[-1]): assert index(results[-1]).min() >= ddf.divisions[-2] assert index(results[-1]).max() <= ddf.divisions[-1] def assert_sane_keynames(ddf): if not hasattr(ddf, 'dask'): return for k in ddf.dask.keys(): while isinstance(k, tuple): k = k[0] assert isinstance(k, (str, bytes)) assert len(k) < 100 assert ' ' not in k if not PY2: assert k.split('-')[0].isidentifier() 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 assert_max_deps(x, n, eq=True): dependencies, dependents = get_deps(x.dask) if eq: assert max(map(len, dependencies.values())) == n else: assert max(map(len, dependencies.values())) <= n def valid_divisions(divisions): """ Are the provided divisions valid? Examples -------- >>> valid_divisions([1, 2, 3]) True >>> valid_divisions([3, 2, 1]) False >>> valid_divisions([1, 1, 1]) False >>> valid_divisions([0, 1, 1]) True >>> valid_divisions(123) False >>> valid_divisions([0, float('nan'), 1]) False """ if not isinstance(divisions, (tuple, list)): return False for i, x in enumerate(divisions[:-2]): if x >= divisions[i + 1]: return False if isinstance(x, numbers.Number) and math.isnan(x): return False for x in divisions[-2:]: if isinstance(x, numbers.Number) and math.isnan(x): return False if divisions[-2] > divisions[-1]: return False return True