from __future__ import annotations import math import re import sys import textwrap import traceback from collections.abc import Callable, Iterator, Mapping from contextlib import contextmanager from numbers import Number from typing import TypeVar, overload import numpy as np import pandas as pd from pandas.api.types import is_dtype_equal import dask from dask.base import get_scheduler, is_dask_collection from dask.core import get_deps from dask.dataframe import ( # noqa: F401 register pandas extension types _dtypes, methods, ) from dask.dataframe._compat import tm # noqa: F401 from dask.dataframe.dispatch import ( # noqa : F401 is_categorical_dtype_dispatch, make_meta, make_meta_obj, meta_nonempty, ) from dask.dataframe.extensions import make_scalar from dask.typing import NoDefault, no_default from dask.utils import ( asciitable, is_dataframe_like, is_index_like, is_series_like, typename, ) meta_object_types: tuple[type, ...] = (pd.Series, pd.DataFrame, pd.Index, pd.MultiIndex) try: import scipy.sparse as sp meta_object_types += (sp.spmatrix,) except ImportError: pass def is_integer_na_dtype(t): dtype = getattr(t, "dtype", t) types = ( pd.Int8Dtype, pd.Int16Dtype, pd.Int32Dtype, pd.Int64Dtype, pd.UInt8Dtype, pd.UInt16Dtype, pd.UInt32Dtype, pd.UInt64Dtype, ) return isinstance(dtype, types) def is_float_na_dtype(t): dtype = getattr(t, "dtype", t) types = ( pd.Float32Dtype, pd.Float64Dtype, ) 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 isinstance(index.dtype, pd.CategoricalDtype): 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 (note that the order of the names should match the order of the columns). 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``. """ T = TypeVar("T", bound=Callable) @overload def insert_meta_param_description(func: T) -> T: ... @overload def insert_meta_param_description(pad: int) -> Callable[[T], T]: ... def insert_meta_param_description(*args, **kwargs): """Replace `$META` in docstring with param description. If pad keyword is provided, will pad description by that number of spaces (default is 8).""" if not args: return lambda f: insert_meta_param_description(f, **kwargs) f = args[0] indent = " " * kwargs.get("pad", 8) body = textwrap.wrap( _META_DESCRIPTION, initial_indent=indent, subsequent_indent=indent, width=78 ) descr = "{}\n{}".format(_META_TYPES, "\n".join(body)) if f.__doc__: if "$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__ = "{}{}{}\n{}{}\n\n{}".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(f" in `{funcname}`" if funcname else "", repr(e), tb) raise ValueError(msg) from e 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 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 strip_unknown_categories(x, just_drop_unknown=False): """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]): if just_drop_unknown: x[c].cat.remove_categories(UNKNOWN_CATEGORIES, inplace=True) else: x[c] = x[c].cat.set_categories([]) elif isinstance(x, pd.Series): if isinstance(x.dtype, pd.CategoricalDtype) and not has_known_categories(x): x = x.cat.set_categories([]) 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, dtype_backend=None): """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. dtype_backend : string, optional If set to PyArrow, the categorical dtype is implemented as a PyArrow dictionary """ if dtype_backend == "pyarrow": # Right now Categorical with PyArrow is implemented as dictionary and # categorical accessor is not yet available return x if not is_index_like(x): x = x.copy() if is_dataframe_like(x): 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] = x[c].cat.set_categories([UNKNOWN_CATEGORIES]) elif is_series_like(x): if is_categorical_dtype_dispatch(x.dtype): x = x.cat.set_categories([UNKNOWN_CATEGORIES]) if index and is_categorical_dtype_dispatch(x.index.dtype): x.index = x.index.set_categories([UNKNOWN_CATEGORIES]) elif is_categorical_dtype_dispatch(x.dtype): x = x.set_categories([UNKNOWN_CATEGORIES]) return x def _empty_series(name, dtype, index=None): if isinstance(dtype, str) and dtype == "category": s = pd.Series(pd.Categorical([UNKNOWN_CATEGORIES]), name=name).iloc[:0] if index is not None: s.index = make_meta(index) return s return pd.Series([], dtype=dtype, name=name, index=index) _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.str_("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(f"Can't handle dtype: {dtype}") def _nonempty_scalar(x): if type(x) in make_scalar._lookup: return make_scalar(x) if np.isscalar(x): dtype = x.dtype if hasattr(x, "dtype") else np.dtype(type(x)) return make_scalar(dtype) if x is pd.NA: return pd.NA raise TypeError(f"Can't handle meta of type '{typename(type(x))}'") 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 isinstance(a, pd.CategoricalDtype) != isinstance(b, pd.CategoricalDtype): return False if isinstance(a, str) and a == "-" or isinstance(b, str) and b == "-": return False if isinstance(a, pd.CategoricalDtype) and isinstance(b, pd.CategoricalDtype): 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 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) ) def check_matching_columns(meta, actual): # Need nan_to_num otherwise nan comparison gives False if not np.array_equal(np.nan_to_num(meta.columns), np.nan_to_num(actual.columns)): extra = methods.tolist(actual.columns.difference(meta.columns)) missing = methods.tolist(meta.columns.difference(actual.columns)) if extra or missing: extra_info = f" Extra: {extra}\n Missing: {missing}" else: extra_info = "Order of columns does not match" raise ValueError( "The columns in the computed data do not match" " the columns in the provided metadata\n" f"{extra_info}" ) 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 = f", {head} to {tail}" else: summary = "" return f"{name}: {n} entries{summary}" ############################################################### # Testing ############################################################### def _check_dask(dsk, check_names=True, check_dtypes=True, result=None, scheduler=None): import dask.dataframe as dd if hasattr(dsk, "__dask_graph__"): graph = dsk.__dask_graph__() if hasattr(graph, "validate"): graph.validate() if result is None: result = dsk.compute(scheduler=scheduler) if isinstance(dsk, dd.Index) or is_index_like(dsk._meta): 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) or is_series_like(dsk._meta): 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) or is_dataframe_like(dsk._meta): 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, ) else: if not np.isscalar(result) and not isinstance( result, (pd.Timestamp, pd.Timedelta) ): raise TypeError( "Expected object of type dataframe, series, index, or scalar.\n" " Got: " + str(type(result)) ) if check_dtypes: assert_dask_dtypes(dsk, result) return result return dsk def _maybe_sort(a, check_index: bool): # 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=methods.tolist(a.columns)) else: a = a.sort_values() except (TypeError, IndexError, ValueError): pass return a.sort_index() if check_index else a def _maybe_convert_string(a, b): if pyarrow_strings_enabled(): from dask.dataframe._pyarrow import to_pyarrow_string if isinstance(a, (pd.DataFrame, pd.Series, pd.Index)): a = to_pyarrow_string(a) if isinstance(b, (pd.DataFrame, pd.Series, pd.Index)): b = to_pyarrow_string(b) return a, b def assert_eq_dtypes(a, b): a, b = _maybe_convert_string(a, b) tm.assert_series_equal(a.dtypes.value_counts(), b.dtypes.value_counts()) def assert_eq( a, b, check_names=True, check_dtype=True, check_divisions=True, check_index=True, sort_results=True, scheduler="sync", **kwargs, ): if check_divisions: assert_divisions(a, scheduler=scheduler) assert_divisions(b, scheduler=scheduler) 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_dtype, scheduler=scheduler ) b = _check_dask( b, check_names=check_names, check_dtypes=check_dtype, scheduler=scheduler ) if hasattr(a, "to_pandas"): a = a.to_pandas() if hasattr(b, "to_pandas"): b = b.to_pandas() a, b = _maybe_convert_string(a, b) if isinstance(a, (pd.DataFrame, pd.Series)) and sort_results: a = _maybe_sort(a, check_index) b = _maybe_sort(b, check_index) if not check_index: a = a.reset_index(drop=True) b = b.reset_index(drop=True) if isinstance(a, pd.DataFrame): tm.assert_frame_equal( a, b, check_names=check_names, check_dtype=check_dtype, **kwargs ) elif isinstance(a, pd.Series): tm.assert_series_equal( a, b, check_names=check_names, check_dtype=check_dtype, **kwargs ) elif isinstance(a, pd.Index): tm.assert_index_equal(a, b, exact=check_dtype, **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(f"given dask graph doesn't contain label: {label}") def assert_divisions(ddf, scheduler=None): if not hasattr(ddf, "divisions"): return assert isinstance(ddf.divisions, tuple) 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 get = get_scheduler(scheduler=scheduler, collections=[type(ddf)]) results = get(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 assert k.split("-")[0].isidentifier(), k 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 a, b in pd.concat([ddf._meta.dtypes, res.dtypes], axis=1).itertuples( index=False ): 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((1, 2, 3)) True >>> valid_divisions(123) False >>> valid_divisions([0, float('nan'), 1]) False """ if not isinstance(divisions, (tuple, list)): return False # Cast tuples to lists as `pd.isnull` treats them differently # https://github.com/pandas-dev/pandas/issues/52283 if isinstance(divisions, tuple): divisions = list(divisions) if pd.isnull(divisions).any(): return False for i, x in enumerate(divisions[:-2]): if x >= divisions[i + 1]: return False if isinstance(x, Number) and math.isnan(x): return False for x in divisions[-2:]: if isinstance(x, Number) and math.isnan(x): return False if divisions[-2] > divisions[-1]: return False return True def drop_by_shallow_copy(df, columns, errors="raise"): """Use shallow copy to drop columns in place""" df2 = df.copy(deep=False) if not pd.api.types.is_list_like(columns): columns = [columns] df2.drop(columns=columns, inplace=True, errors=errors) return df2 class AttributeNotImplementedError(NotImplementedError, AttributeError): """NotImplementedError and AttributeError""" def meta_frame_constructor(like): """Return a serial DataFrame constructor Parameters ---------- like : Any series-like, Index-like or dataframe-like object. """ if is_dask_collection(like): try: like = like._meta except AttributeError: raise TypeError(f"{type(like)} not supported by meta_frame_constructor") if is_dataframe_like(like): return like._constructor elif is_series_like(like): return like._constructor_expanddim elif is_index_like(like): return like.to_frame()._constructor else: raise TypeError(f"{type(like)} not supported by meta_frame_constructor") def meta_series_constructor(like): """Return a serial Series constructor Parameters ---------- like : Any series-like, Index-like or dataframe-like object. """ if is_dask_collection(like): try: like = like._meta except AttributeError: raise TypeError(f"{type(like)} not supported by meta_series_constructor") if is_dataframe_like(like): return like._constructor_sliced elif is_series_like(like): return like._constructor elif is_index_like(like): return like.to_frame()._constructor_sliced else: raise TypeError(f"{type(like)} not supported by meta_series_constructor") def get_string_dtype(): """Depending on config setting, we might convert objects to pyarrow strings""" return pd.StringDtype("pyarrow") if pyarrow_strings_enabled() else object def pyarrow_strings_enabled() -> bool: """Config setting to convert objects to pyarrow strings""" convert_string = dask.config.get("dataframe.convert-string") if convert_string is None: from dask.dataframe._pyarrow import check_pyarrow_string_supported try: check_pyarrow_string_supported() convert_string = True except RuntimeError: convert_string = False return convert_string def get_numeric_only_kwargs(numeric_only: bool | NoDefault) -> dict: return {} if numeric_only is no_default else {"numeric_only": numeric_only} def check_numeric_only_valid(numeric_only: bool | NoDefault, name: str) -> dict: if numeric_only is not no_default: return {"numeric_only": numeric_only} elif numeric_only is no_default: return {} raise NotImplementedError( f"numeric_only is not implemented for {name} for pandas < 1.5." )