mirror of
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215 lines
6.9 KiB
Python
215 lines
6.9 KiB
Python
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"""
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Test extension array for storing nested data in a pandas container.
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The JSONArray stores lists of dictionaries. The storage mechanism is a list,
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not an ndarray.
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Note
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----
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We currently store lists of UserDicts. Pandas has a few places
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internally that specifically check for dicts, and does non-scalar things
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in that case. We *want* the dictionaries to be treated as scalars, so we
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hack around pandas by using UserDicts.
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"""
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from collections import UserDict, abc
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import itertools
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import numbers
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import random
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import string
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import sys
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from typing import Any, Mapping, Type
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import numpy as np
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from pandas.core.dtypes.common import pandas_dtype
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import pandas as pd
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from pandas.api.extensions import ExtensionArray, ExtensionDtype
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class JSONDtype(ExtensionDtype):
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type = abc.Mapping
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name = "json"
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na_value: Mapping[str, Any] = UserDict()
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@classmethod
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def construct_array_type(cls) -> Type["JSONArray"]:
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"""
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Return the array type associated with this dtype.
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Returns
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-------
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type
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"""
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return JSONArray
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class JSONArray(ExtensionArray):
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dtype = JSONDtype()
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__array_priority__ = 1000
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def __init__(self, values, dtype=None, copy=False):
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for val in values:
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if not isinstance(val, self.dtype.type):
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raise TypeError("All values must be of type " + str(self.dtype.type))
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self.data = values
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# Some aliases for common attribute names to ensure pandas supports
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# these
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self._items = self._data = self.data
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# those aliases are currently not working due to assumptions
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# in internal code (GH-20735)
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# self._values = self.values = self.data
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@classmethod
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def _from_sequence(cls, scalars, dtype=None, copy=False):
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return cls(scalars)
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@classmethod
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def _from_factorized(cls, values, original):
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return cls([UserDict(x) for x in values if x != ()])
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def __getitem__(self, item):
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if isinstance(item, numbers.Integral):
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return self.data[item]
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elif isinstance(item, slice) and item == slice(None):
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# Make sure we get a view
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return type(self)(self.data)
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elif isinstance(item, slice):
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# slice
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return type(self)(self.data[item])
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else:
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item = pd.api.indexers.check_array_indexer(self, item)
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if pd.api.types.is_bool_dtype(item.dtype):
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return self._from_sequence([x for x, m in zip(self, item) if m])
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# integer
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return type(self)([self.data[i] for i in item])
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def __setitem__(self, key, value):
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if isinstance(key, numbers.Integral):
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self.data[key] = value
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else:
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if not isinstance(value, (type(self), abc.Sequence)):
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# broadcast value
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value = itertools.cycle([value])
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if isinstance(key, np.ndarray) and key.dtype == "bool":
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# masking
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for i, (k, v) in enumerate(zip(key, value)):
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if k:
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assert isinstance(v, self.dtype.type)
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self.data[i] = v
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else:
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for k, v in zip(key, value):
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assert isinstance(v, self.dtype.type)
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self.data[k] = v
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def __len__(self) -> int:
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return len(self.data)
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def __eq__(self, other):
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return NotImplemented
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def __ne__(self, other):
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return NotImplemented
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def __array__(self, dtype=None):
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if dtype is None:
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dtype = object
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return np.asarray(self.data, dtype=dtype)
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@property
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def nbytes(self) -> int:
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return sys.getsizeof(self.data)
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def isna(self):
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return np.array([x == self.dtype.na_value for x in self.data], dtype=bool)
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def take(self, indexer, allow_fill=False, fill_value=None):
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# re-implement here, since NumPy has trouble setting
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# sized objects like UserDicts into scalar slots of
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# an ndarary.
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indexer = np.asarray(indexer)
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msg = (
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"Index is out of bounds or cannot do a "
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"non-empty take from an empty array."
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)
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if allow_fill:
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if fill_value is None:
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fill_value = self.dtype.na_value
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# bounds check
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if (indexer < -1).any():
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raise ValueError
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try:
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output = [
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self.data[loc] if loc != -1 else fill_value for loc in indexer
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]
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except IndexError as err:
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raise IndexError(msg) from err
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else:
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try:
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output = [self.data[loc] for loc in indexer]
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except IndexError as err:
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raise IndexError(msg) from err
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return self._from_sequence(output)
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def copy(self):
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return type(self)(self.data[:])
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def astype(self, dtype, copy=True):
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# NumPy has issues when all the dicts are the same length.
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# np.array([UserDict(...), UserDict(...)]) fails,
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# but np.array([{...}, {...}]) works, so cast.
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from pandas.core.arrays.string_ import StringDtype
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dtype = pandas_dtype(dtype)
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# needed to add this check for the Series constructor
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if isinstance(dtype, type(self.dtype)) and dtype == self.dtype:
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if copy:
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return self.copy()
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return self
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elif isinstance(dtype, StringDtype):
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value = self.astype(str) # numpy doesn'y like nested dicts
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return dtype.construct_array_type()._from_sequence(value, copy=False)
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return np.array([dict(x) for x in self], dtype=dtype, copy=copy)
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def unique(self):
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# Parent method doesn't work since np.array will try to infer
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# a 2-dim object.
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return type(self)([dict(x) for x in {tuple(d.items()) for d in self.data}])
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@classmethod
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def _concat_same_type(cls, to_concat):
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data = list(itertools.chain.from_iterable(x.data for x in to_concat))
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return cls(data)
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def _values_for_factorize(self):
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frozen = self._values_for_argsort()
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if len(frozen) == 0:
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# _factorize_array expects 1-d array, this is a len-0 2-d array.
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frozen = frozen.ravel()
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return frozen, ()
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def _values_for_argsort(self):
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# Disable NumPy's shape inference by including an empty tuple...
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# If all the elements of self are the same size P, NumPy will
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# cast them to an (N, P) array, instead of an (N,) array of tuples.
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frozen = [()] + [tuple(x.items()) for x in self]
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return np.array(frozen, dtype=object)[1:]
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def make_data():
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# TODO: Use a regular dict. See _NDFrameIndexer._setitem_with_indexer
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return [
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UserDict(
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[
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(random.choice(string.ascii_letters), random.randint(0, 100))
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for _ in range(random.randint(0, 10))
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]
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)
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for _ in range(100)
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]
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