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