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sirh/venv/lib/python3.12/site-packages/pandas/io/json/_json.py

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2026-04-29 11:52:03 +02:00
from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from collections import abc
from itertools import islice
from typing import (
TYPE_CHECKING,
Any,
Generic,
Literal,
Self,
TypeVar,
final,
overload,
)
import warnings
import numpy as np
from pandas._config import option_context
from pandas._libs import lib
from pandas._libs.json import (
ujson_dumps,
ujson_loads,
)
from pandas._libs.tslibs import iNaT
from pandas.compat._optional import import_optional_dependency
from pandas.errors import (
AbstractMethodError,
OutOfBoundsDatetime,
)
from pandas.util._decorators import set_module
from pandas.util._validators import check_dtype_backend
from pandas.core.dtypes.common import (
ensure_str,
is_string_dtype,
pandas_dtype,
)
from pandas.core.dtypes.dtypes import PeriodDtype
from pandas import (
ArrowDtype,
DataFrame,
Index,
MultiIndex,
Series,
isna,
notna,
to_datetime,
)
from pandas.core.reshape.concat import concat
from pandas.io._util import arrow_table_to_pandas
from pandas.io.common import (
IOHandles,
dedup_names,
get_handle,
is_potential_multi_index,
stringify_path,
)
from pandas.io.json._normalize import convert_to_line_delimits
from pandas.io.json._table_schema import (
build_table_schema,
parse_table_schema,
set_default_names,
)
from pandas.io.parsers.readers import validate_integer
if TYPE_CHECKING:
from collections.abc import (
Callable,
Hashable,
Mapping,
)
from types import TracebackType
from pandas._typing import (
CompressionOptions,
DtypeArg,
DtypeBackend,
FilePath,
IndexLabel,
JSONEngine,
JSONSerializable,
ReadBuffer,
StorageOptions,
WriteBuffer,
)
from pandas.core.generic import NDFrame
FrameSeriesStrT = TypeVar("FrameSeriesStrT", bound=Literal["frame", "series"])
# interface to/from
@overload
def to_json(
path_or_buf: FilePath | WriteBuffer[str] | WriteBuffer[bytes],
obj: NDFrame,
orient: str | None = ...,
date_format: str = ...,
double_precision: int = ...,
force_ascii: bool = ...,
date_unit: str = ...,
default_handler: Callable[[Any], JSONSerializable] | None = ...,
lines: bool = ...,
compression: CompressionOptions = ...,
index: bool | None = ...,
indent: int = ...,
storage_options: StorageOptions = ...,
mode: Literal["a", "w"] = ...,
) -> None: ...
@overload
def to_json(
path_or_buf: None,
obj: NDFrame,
orient: str | None = ...,
date_format: str = ...,
double_precision: int = ...,
force_ascii: bool = ...,
date_unit: str = ...,
default_handler: Callable[[Any], JSONSerializable] | None = ...,
lines: bool = ...,
compression: CompressionOptions = ...,
index: bool | None = ...,
indent: int = ...,
storage_options: StorageOptions = ...,
mode: Literal["a", "w"] = ...,
) -> str: ...
def to_json(
path_or_buf: FilePath | WriteBuffer[str] | WriteBuffer[bytes] | None,
obj: NDFrame,
orient: str | None = None,
date_format: str = "epoch",
double_precision: int = 10,
force_ascii: bool = True,
date_unit: str = "ms",
default_handler: Callable[[Any], JSONSerializable] | None = None,
lines: bool = False,
compression: CompressionOptions = "infer",
index: bool | None = None,
indent: int = 0,
storage_options: StorageOptions | None = None,
mode: Literal["a", "w"] = "w",
) -> str | None:
if orient in ["records", "values"] and index is True:
raise ValueError(
"'index=True' is only valid when 'orient' is 'split', 'table', "
"'index', or 'columns'."
)
elif orient in ["index", "columns"] and index is False:
raise ValueError(
"'index=False' is only valid when 'orient' is 'split', 'table', "
"'records', or 'values'."
)
elif index is None:
# will be ignored for orient='records' and 'values'
index = True
if lines and orient != "records":
raise ValueError("'lines' keyword only valid when 'orient' is records")
if mode not in ["a", "w"]:
msg = (
f"mode={mode} is not a valid option."
"Only 'w' and 'a' are currently supported."
)
raise ValueError(msg)
if mode == "a" and (not lines or orient != "records"):
msg = (
"mode='a' (append) is only supported when "
"lines is True and orient is 'records'"
)
raise ValueError(msg)
if orient == "table" and isinstance(obj, Series):
obj = obj.to_frame(name=obj.name or "values")
if date_format == "epoch":
# for epoch (numeric) format, convert datetime-likes to the desired
# unit up front, such that the C ObjToJSON code can simply write out
# the integer values without worrying about conversion
if date_unit not in ["s", "ms", "us", "ns"]:
raise ValueError(f"Invalid value '{date_unit}' for option 'date_unit'")
if isinstance(obj, DataFrame):
copied = False
cols = np.nonzero(obj.dtypes.map(lambda dt: dt.kind in ["M", "m"]))[0]
if len(cols):
obj = obj.copy(deep=False)
copied = True
for col in cols:
obj.isetitem(col, obj.iloc[:, col].dt.as_unit(date_unit))
if obj.index.dtype.kind in "Mm":
if not copied:
obj = obj.copy(deep=False)
copied = True
obj.index = Series(obj.index).dt.as_unit(date_unit)
if obj.columns.dtype.kind in "Mm":
if not copied:
obj = obj.copy(deep=False)
copied = True
obj.columns = Series(obj.columns).dt.as_unit(date_unit)
elif isinstance(obj, Series):
if obj.dtype.kind in "Mm":
obj = obj.copy(deep=False)
obj = obj.dt.as_unit(date_unit)
if obj.index.dtype.kind in "Mm":
obj = obj.copy(deep=False)
obj.index = Series(obj.index).dt.as_unit(date_unit)
writer: type[Writer]
if orient == "table" and isinstance(obj, DataFrame):
writer = JSONTableWriter
elif isinstance(obj, Series):
writer = SeriesWriter
elif isinstance(obj, DataFrame):
writer = FrameWriter
else:
raise NotImplementedError("'obj' should be a Series or a DataFrame")
s = writer(
obj,
orient=orient,
date_format=date_format,
double_precision=double_precision,
ensure_ascii=force_ascii,
date_unit=date_unit,
default_handler=default_handler,
index=index,
indent=indent,
).write()
if lines:
s = convert_to_line_delimits(s)
if path_or_buf is not None:
# apply compression and byte/text conversion
with get_handle(
path_or_buf, mode, compression=compression, storage_options=storage_options
) as handles:
handles.handle.write(s)
else:
return s
return None
class Writer(ABC):
_default_orient: str
def __init__(
self,
obj: NDFrame,
orient: str | None,
date_format: str,
double_precision: int,
ensure_ascii: bool,
date_unit: str,
index: bool,
default_handler: Callable[[Any], JSONSerializable] | None = None,
indent: int = 0,
) -> None:
self.obj = obj
if orient is None:
orient = self._default_orient
self.orient = orient
self.date_format = date_format
self.double_precision = double_precision
self.ensure_ascii = ensure_ascii
self.date_unit = date_unit
self.default_handler = default_handler
self.index = index
self.indent = indent
self._format_axes()
def _format_axes(self) -> None:
raise AbstractMethodError(self)
def write(self) -> str:
iso_dates = self.date_format == "iso"
return ujson_dumps(
self.obj_to_write,
orient=self.orient,
double_precision=self.double_precision,
ensure_ascii=self.ensure_ascii,
date_unit=self.date_unit,
iso_dates=iso_dates,
default_handler=self.default_handler,
indent=self.indent,
)
@property
@abstractmethod
def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]:
"""Object to write in JSON format."""
class SeriesWriter(Writer):
_default_orient = "index"
@property
def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]:
if not self.index and self.orient == "split":
return {"name": self.obj.name, "data": self.obj.values}
else:
return self.obj
def _format_axes(self) -> None:
if not self.obj.index.is_unique and self.orient == "index":
raise ValueError(f"Series index must be unique for orient='{self.orient}'")
class FrameWriter(Writer):
_default_orient = "columns"
@property
def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]:
if not self.index and self.orient == "split":
obj_to_write = self.obj.to_dict(orient="split")
del obj_to_write["index"]
else:
obj_to_write = self.obj
return obj_to_write
def _format_axes(self) -> None:
"""
Try to format axes if they are datelike.
"""
if not self.obj.index.is_unique and self.orient in ("index", "columns"):
raise ValueError(
f"DataFrame index must be unique for orient='{self.orient}'."
)
if not self.obj.columns.is_unique and self.orient in (
"index",
"columns",
"records",
):
raise ValueError(
f"DataFrame columns must be unique for orient='{self.orient}'."
)
class JSONTableWriter(FrameWriter):
_default_orient = "records"
def __init__(
self,
obj,
orient: str | None,
date_format: str,
double_precision: int,
ensure_ascii: bool,
date_unit: str,
index: bool,
default_handler: Callable[[Any], JSONSerializable] | None = None,
indent: int = 0,
) -> None:
"""
Adds a `schema` attribute with the Table Schema, resets
the index (can't do in caller, because the schema inference needs
to know what the index is, forces orient to records, and forces
date_format to 'iso'.
"""
super().__init__(
obj,
orient,
date_format,
double_precision,
ensure_ascii,
date_unit,
index,
default_handler=default_handler,
indent=indent,
)
if date_format != "iso":
msg = (
"Trying to write with `orient='table'` and "
f"`date_format='{date_format}'`. Table Schema requires dates "
"to be formatted with `date_format='iso'`"
)
raise ValueError(msg)
self.schema = build_table_schema(obj, index=self.index)
if self.index:
obj = set_default_names(obj)
# NotImplemented on a column MultiIndex
if obj.ndim == 2 and isinstance(obj.columns, MultiIndex):
raise NotImplementedError(
"orient='table' is not supported for MultiIndex columns"
)
# TODO: Do this timedelta properly in objToJSON.c See GH #15137
if ((obj.ndim == 1) and (obj.name in set(obj.index.names))) or len(
obj.columns.intersection(obj.index.names)
):
msg = "Overlapping names between the index and columns"
raise ValueError(msg)
timedeltas = obj.select_dtypes(include=["timedelta"]).columns
copied = False
if len(timedeltas):
obj = obj.copy()
copied = True
obj[timedeltas] = obj[timedeltas].map(lambda x: x.isoformat())
# exclude index from obj if index=False
if not self.index:
self.obj = obj.reset_index(drop=True)
else:
# Convert PeriodIndex to datetimes before serializing
if isinstance(obj.index.dtype, PeriodDtype):
if not copied:
obj = obj.copy(deep=False)
obj.index = obj.index.to_timestamp()
self.obj = obj.reset_index(drop=False)
self.date_format = "iso"
self.orient = "records"
self.index = index
@property
def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]:
return {"schema": self.schema, "data": self.obj}
@overload
def read_json(
path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
*,
orient: str | None = ...,
typ: Literal["frame"] = ...,
dtype: DtypeArg | None = ...,
convert_axes: bool | None = ...,
convert_dates: bool | list[str] = ...,
keep_default_dates: bool = ...,
precise_float: bool = ...,
date_unit: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
lines: bool = ...,
chunksize: int,
compression: CompressionOptions = ...,
nrows: int | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
engine: JSONEngine = ...,
) -> JsonReader[Literal["frame"]]: ...
@overload
def read_json(
path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
*,
orient: str | None = ...,
typ: Literal["series"],
dtype: DtypeArg | None = ...,
convert_axes: bool | None = ...,
convert_dates: bool | list[str] = ...,
keep_default_dates: bool = ...,
precise_float: bool = ...,
date_unit: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
lines: bool = ...,
chunksize: int,
compression: CompressionOptions = ...,
nrows: int | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
engine: JSONEngine = ...,
) -> JsonReader[Literal["series"]]: ...
@overload
def read_json(
path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
*,
orient: str | None = ...,
typ: Literal["series"],
dtype: DtypeArg | None = ...,
convert_axes: bool | None = ...,
convert_dates: bool | list[str] = ...,
keep_default_dates: bool = ...,
precise_float: bool = ...,
date_unit: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
lines: bool = ...,
chunksize: None = ...,
compression: CompressionOptions = ...,
nrows: int | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
engine: JSONEngine = ...,
) -> Series: ...
@overload
def read_json(
path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
*,
orient: str | None = ...,
typ: Literal["frame"] = ...,
dtype: DtypeArg | None = ...,
convert_axes: bool | None = ...,
convert_dates: bool | list[str] = ...,
keep_default_dates: bool = ...,
precise_float: bool = ...,
date_unit: str | None = ...,
encoding: str | None = ...,
encoding_errors: str | None = ...,
lines: bool = ...,
chunksize: None = ...,
compression: CompressionOptions = ...,
nrows: int | None = ...,
storage_options: StorageOptions = ...,
dtype_backend: DtypeBackend | lib.NoDefault = ...,
engine: JSONEngine = ...,
) -> DataFrame: ...
@set_module("pandas")
def read_json(
path_or_buf: FilePath | ReadBuffer[str] | ReadBuffer[bytes],
*,
orient: str | None = None,
typ: Literal["frame", "series"] = "frame",
dtype: DtypeArg | None = None,
convert_axes: bool | None = None,
convert_dates: bool | list[str] = True,
keep_default_dates: bool = True,
precise_float: bool = False,
date_unit: str | None = None,
encoding: str | None = None,
encoding_errors: str | None = "strict",
lines: bool = False,
chunksize: int | None = None,
compression: CompressionOptions = "infer",
nrows: int | None = None,
storage_options: StorageOptions | None = None,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
engine: JSONEngine = "ujson",
) -> DataFrame | Series | JsonReader:
"""
Convert a JSON string to pandas object.
This method reads JSON files or JSON-like data and converts them into pandas
objects. It supports a variety of input formats, including line-delimited JSON,
compressed files, and various data representations (table, records, index-based,
etc.). When `chunksize` is specified, an iterator is returned instead of loading
the entire data into memory.
Parameters
----------
path_or_buf : a str path, path object or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be:
``file://localhost/path/to/table.json``.
If you want to pass in a path object, pandas accepts any
``os.PathLike``.
By file-like object, we refer to objects with a ``read()`` method,
such as a file handle (e.g. via builtin ``open`` function)
or ``StringIO``.
orient : str, optional
Indication of expected JSON string format.
Compatible JSON strings can be produced by ``to_json()`` with a
corresponding orient value.
The set of possible orients is:
- ``'split'`` : dict like
``{index -> [index], columns -> [columns], data -> [values]}``
- ``'records'`` : list like
``[{column -> value}, ... , {column -> value}]``
- ``'index'`` : dict like ``{index -> {column -> value}}``
- ``'columns'`` : dict like ``{column -> {index -> value}}``
- ``'values'`` : just the values array
- ``'table'`` : dict like ``{'schema': {schema}, 'data': {data}}``
The allowed and default values depend on the value
of the `typ` parameter.
* when ``typ == 'series'``,
- allowed orients are ``{'split','records','index'}``
- default is ``'index'``
- The Series index must be unique for orient ``'index'``.
* when ``typ == 'frame'``,
- allowed orients are ``{'split','records','index',
'columns','values', 'table'}``
- default is ``'columns'``
- The DataFrame index must be unique for orients ``'index'`` and
``'columns'``.
- The DataFrame columns must be unique for orients ``'index'``,
``'columns'``, and ``'records'``.
typ : {'frame', 'series'}, default 'frame'
The type of object to recover.
dtype : bool or dict, default None
If True, infer dtypes; if a dict of column to dtype, then use those;
if False, then don't infer dtypes at all, applies only to the data.
For all ``orient`` values except ``'table'``, default is True.
convert_axes : bool, default None
Try to convert the axes to the proper dtypes.
For all ``orient`` values except ``'table'``, default is True.
convert_dates : bool or list of str, default True
If True then default datelike columns may be converted (depending on
keep_default_dates).
If False, no dates will be converted.
If a list of column names, then those columns will be converted and
default datelike columns may also be converted (depending on
keep_default_dates).
keep_default_dates : bool, default True
If parsing dates (convert_dates is not False), then try to parse the
default datelike columns.
A column label is datelike if
* it ends with ``'_at'``,
* it ends with ``'_time'``,
* it begins with ``'timestamp'``,
* it is ``'modified'``, or
* it is ``'date'``.
precise_float : bool, default False
Set to enable usage of higher precision (strtod) function when
decoding string to double values. Default (False) is to use fast but
less precise builtin functionality.
date_unit : str, default None
The timestamp unit to detect if converting dates. The default behaviour
is to try and detect the correct precision, but if this is not desired
then pass one of 's', 'ms', 'us' or 'ns' to force parsing only seconds,
milliseconds, microseconds or nanoseconds respectively.
encoding : str, default is 'utf-8'
The encoding to use to decode py3 bytes.
encoding_errors : str, optional, default "strict"
How encoding errors are treated. `List of possible values
<https://docs.python.org/3/library/codecs.html#error-handlers>`_ .
lines : bool, default False
Read the file as a json object per line.
chunksize : int, optional
Return JsonReader object for iteration.
See the `line-delimited json docs
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#line-delimited-json>`_
for more information on ``chunksize``.
This can only be passed if `lines=True`.
If this is None, the file will be read into memory all at once.
compression : str or dict, default 'infer'
For on-the-fly decompression of on-disk data. If 'infer' and 'path_or_buf' is
path-like, then detect compression from the following extensions: '.gz',
'.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
(otherwise no compression).
If using 'zip' or 'tar', the ZIP file must contain only one data file to be
read in.
Set to ``None`` for no decompression.
Can also be a dict with key ``'method'`` set
to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``}
and other key-value pairs are forwarded to
``zipfile.ZipFile``, ``gzip.GzipFile``,
``bz2.BZ2File``, ``zstandard.ZstdDecompressor``, ``lzma.LZMAFile`` or
``tarfile.TarFile``, respectively.
As an example, the following could be passed for Zstandard decompression using a
custom compression dictionary:
``compression={'method': 'zstd', 'dict_data': my_compression_dict}``.
nrows : int, optional
The number of lines from the line-delimited jsonfile that has to be read.
This can only be passed if `lines=True`.
If this is None, all the rows will be returned.
storage_options : dict, optional
Extra options that make sense for a particular storage connection, e.g.
host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
are forwarded to ``urllib.request.Request`` as header options. For other
URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
details, and for more examples on storage options refer `here
<https://pandas.pydata.org/docs/user_guide/io.html?
highlight=storage_options#reading-writing-remote-files>`_.
dtype_backend : {'numpy_nullable', 'pyarrow'}
Back-end data type applied to the resultant :class:`DataFrame`
(still experimental). If not specified, the default behavior
is to not use nullable data types. If specified, the behavior
is as follows:
* ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
* ``"pyarrow"``: returns pyarrow-backed nullable
:class:`ArrowDtype` :class:`DataFrame`
.. versionadded:: 2.0
engine : {"ujson", "pyarrow"}, default "ujson"
Parser engine to use. The ``"pyarrow"`` engine is only available when
``lines=True``.
.. versionadded:: 2.0
Returns
-------
Series, DataFrame, or pandas.api.typing.JsonReader
A JsonReader is returned when ``chunksize`` is not ``0`` or ``None``.
Otherwise, the type returned depends on the value of ``typ``.
See Also
--------
DataFrame.to_json : Convert a DataFrame to a JSON string.
Series.to_json : Convert a Series to a JSON string.
json_normalize : Normalize semi-structured JSON data into a flat table.
Notes
-----
Specific to ``orient='table'``, if a :class:`DataFrame` with a literal
:class:`Index` name of `index` gets written with :func:`to_json`, the
subsequent read operation will incorrectly set the :class:`Index` name to
``None``. This is because `index` is also used by :func:`DataFrame.to_json`
to denote a missing :class:`Index` name, and the subsequent
:func:`read_json` operation cannot distinguish between the two. The same
limitation is encountered with a :class:`MultiIndex` and any names
beginning with ``'level_'``.
Examples
--------
>>> from io import StringIO
>>> df = pd.DataFrame(
... [["a", "b"], ["c", "d"]],
... index=["row 1", "row 2"],
... columns=["col 1", "col 2"],
... )
Encoding/decoding a Dataframe using ``'split'`` formatted JSON:
>>> df.to_json(orient="split")
'{"columns":["col 1","col 2"],"index":["row 1","row 2"],"data":[["a","b"],["c","d"]]}'
>>> pd.read_json(StringIO(_), orient="split") # noqa: F821
col 1 col 2
row 1 a b
row 2 c d
Encoding/decoding a Dataframe using ``'index'`` formatted JSON:
>>> df.to_json(orient="index")
'{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}'
>>> pd.read_json(StringIO(_), orient="index") # noqa: F821
col 1 col 2
row 1 a b
row 2 c d
Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
Note that index labels are not preserved with this encoding.
>>> df.to_json(orient="records")
'[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]'
>>> pd.read_json(StringIO(_), orient="records") # noqa: F821
col 1 col 2
0 a b
1 c d
Encoding with Table Schema
>>> df.to_json(orient="table")
'{"schema":{"fields":[{"name":"index","type":"string","extDtype":"str"},{"name":"col 1","type":"string","extDtype":"str"},{"name":"col 2","type":"string","extDtype":"str"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":"row 1","col 1":"a","col 2":"b"},{"index":"row 2","col 1":"c","col 2":"d"}]}'
The following example uses ``dtype_backend="numpy_nullable"``
>>> data = '''{"index": {"0": 0, "1": 1},
... "a": {"0": 1, "1": null},
... "b": {"0": 2.5, "1": 4.5},
... "c": {"0": true, "1": false},
... "d": {"0": "a", "1": "b"},
... "e": {"0": 1577.2, "1": 1577.1}}'''
>>> pd.read_json(StringIO(data), dtype_backend="numpy_nullable")
index a b c d e
0 0 1 2.5 True a 1577.2
1 1 <NA> 4.5 False b 1577.1
""" # noqa: E501
if orient == "table" and dtype:
raise ValueError("cannot pass both dtype and orient='table'")
if orient == "table" and convert_axes:
raise ValueError("cannot pass both convert_axes and orient='table'")
check_dtype_backend(dtype_backend)
if dtype is None and orient != "table":
# error: Incompatible types in assignment (expression has type "bool", variable
# has type "Union[ExtensionDtype, str, dtype[Any], Type[str], Type[float],
# Type[int], Type[complex], Type[bool], Type[object], Dict[Hashable,
# Union[ExtensionDtype, Union[str, dtype[Any]], Type[str], Type[float],
# Type[int], Type[complex], Type[bool], Type[object]]], None]")
dtype = True # type: ignore[assignment]
if convert_axes is None and orient != "table":
convert_axes = True
json_reader = JsonReader(
path_or_buf,
orient=orient,
typ=typ,
dtype=dtype,
convert_axes=convert_axes,
convert_dates=convert_dates,
keep_default_dates=keep_default_dates,
precise_float=precise_float,
date_unit=date_unit,
encoding=encoding,
lines=lines,
chunksize=chunksize,
compression=compression,
nrows=nrows,
storage_options=storage_options,
encoding_errors=encoding_errors,
dtype_backend=dtype_backend,
engine=engine,
)
if chunksize:
return json_reader
else:
return json_reader.read()
@set_module("pandas.api.typing")
class JsonReader(abc.Iterator, Generic[FrameSeriesStrT]):
"""
JsonReader provides an interface for reading in a JSON file.
If initialized with ``lines=True`` and ``chunksize``, can be iterated over
``chunksize`` lines at a time. Otherwise, calling ``read`` reads in the
whole document.
"""
def __init__(
self,
filepath_or_buffer,
orient,
typ: FrameSeriesStrT,
dtype,
convert_axes: bool | None,
convert_dates,
keep_default_dates: bool,
precise_float: bool,
date_unit,
encoding,
lines: bool,
chunksize: int | None,
compression: CompressionOptions,
nrows: int | None,
storage_options: StorageOptions | None = None,
encoding_errors: str | None = "strict",
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
engine: JSONEngine = "ujson",
) -> None:
self.orient = orient
self.typ = typ
self.dtype = dtype
self.convert_axes = convert_axes
self.convert_dates = convert_dates
self.keep_default_dates = keep_default_dates
self.precise_float = precise_float
self.date_unit = date_unit
self.encoding = encoding
self.engine = engine
self.compression = compression
self.storage_options = storage_options
self.lines = lines
self.chunksize = chunksize
self.nrows_seen = 0
self.nrows = nrows
self.encoding_errors = encoding_errors
self.handles: IOHandles[str] | None = None
self.dtype_backend = dtype_backend
if self.engine not in {"pyarrow", "ujson"}:
raise ValueError(
f"The engine type {self.engine} is currently not supported."
)
if self.chunksize is not None:
self.chunksize = validate_integer("chunksize", self.chunksize, 1)
if not self.lines:
raise ValueError("chunksize can only be passed if lines=True")
if self.engine == "pyarrow":
raise ValueError(
"currently pyarrow engine doesn't support chunksize parameter"
)
if self.nrows is not None:
self.nrows = validate_integer("nrows", self.nrows, 0)
if not self.lines:
raise ValueError("nrows can only be passed if lines=True")
if self.engine == "pyarrow":
if not self.lines:
raise ValueError(
"currently pyarrow engine only supports "
"the line-delimited JSON format"
)
self.data = filepath_or_buffer
elif self.engine == "ujson":
data = self._get_data_from_filepath(filepath_or_buffer)
# If self.chunksize, we prepare the data for the `__next__` method.
# Otherwise, we read it into memory for the `read` method.
if not (self.chunksize or self.nrows):
with self:
self.data = data.read()
else:
self.data = data
def _get_data_from_filepath(self, filepath_or_buffer):
"""
The function read_json accepts three input types:
1. filepath (string-like)
2. file-like object (e.g. open file object, StringIO)
"""
filepath_or_buffer = stringify_path(filepath_or_buffer)
try:
self.handles = get_handle(
filepath_or_buffer,
"r",
encoding=self.encoding,
compression=self.compression,
storage_options=self.storage_options,
errors=self.encoding_errors,
)
except OSError as err:
raise FileNotFoundError(
f"File {filepath_or_buffer} does not exist"
) from err
filepath_or_buffer = self.handles.handle
return filepath_or_buffer
def _combine_lines(self, lines) -> str:
"""
Combines a list of JSON objects into one JSON object.
"""
return (
f"[{','.join([line for line in (line.strip() for line in lines) if line])}]"
)
@overload
def read(self: JsonReader[Literal["frame"]]) -> DataFrame: ...
@overload
def read(self: JsonReader[Literal["series"]]) -> Series: ...
@overload
def read(self: JsonReader[Literal["frame", "series"]]) -> DataFrame | Series: ...
def read(self) -> DataFrame | Series:
"""
Read the whole JSON input into a pandas object.
"""
obj: DataFrame | Series
with self:
if self.engine == "pyarrow":
obj = self._read_pyarrow()
elif self.engine == "ujson":
obj = self._read_ujson()
return obj
def _read_pyarrow(self) -> DataFrame:
"""
Read JSON using the pyarrow engine.
"""
pyarrow_json = import_optional_dependency("pyarrow.json")
options = None
if isinstance(self.dtype, dict):
pa = import_optional_dependency("pyarrow")
fields = []
for field, dtype in self.dtype.items():
pd_dtype = pandas_dtype(dtype)
if isinstance(pd_dtype, ArrowDtype):
fields.append((field, pd_dtype.pyarrow_dtype))
schema = pa.schema(fields)
options = pyarrow_json.ParseOptions(
explicit_schema=schema, unexpected_field_behavior="infer"
)
pa_table = pyarrow_json.read_json(self.data, parse_options=options)
df = arrow_table_to_pandas(pa_table, dtype_backend=self.dtype_backend)
return df
def _read_ujson(self) -> DataFrame | Series:
"""
Read JSON using the ujson engine.
"""
obj: DataFrame | Series
if self.lines:
if self.chunksize:
obj = concat(self)
elif self.nrows:
lines = list(islice(self.data, self.nrows))
lines_json = self._combine_lines(lines)
obj = self._get_object_parser(lines_json)
else:
data = ensure_str(self.data)
data_lines = data.split("\n")
obj = self._get_object_parser(self._combine_lines(data_lines))
else:
obj = self._get_object_parser(self.data)
if self.dtype_backend is not lib.no_default:
with option_context("future.distinguish_nan_and_na", False):
return obj.convert_dtypes(
infer_objects=False, dtype_backend=self.dtype_backend
)
else:
return obj
def _get_object_parser(self, json: str) -> DataFrame | Series:
"""
Parses a json document into a pandas object.
"""
typ = self.typ
dtype = self.dtype
kwargs = {
"orient": self.orient,
"dtype": self.dtype,
"convert_axes": self.convert_axes,
"convert_dates": self.convert_dates,
"keep_default_dates": self.keep_default_dates,
"precise_float": self.precise_float,
"date_unit": self.date_unit,
"dtype_backend": self.dtype_backend,
}
if typ == "frame":
return FrameParser(json, **kwargs).parse()
elif typ == "series":
if not isinstance(dtype, bool):
kwargs["dtype"] = dtype
return SeriesParser(json, **kwargs).parse()
else:
raise ValueError(f"{typ=} must be 'frame' or 'series'.")
def close(self) -> None:
"""
If we opened a stream earlier, in _get_data_from_filepath, we should
close it.
If an open stream or file was passed, we leave it open.
"""
if self.handles is not None:
self.handles.close()
def __iter__(self) -> Self:
return self
@overload
def __next__(self: JsonReader[Literal["frame"]]) -> DataFrame: ...
@overload
def __next__(self: JsonReader[Literal["series"]]) -> Series: ...
@overload
def __next__(
self: JsonReader[Literal["frame", "series"]],
) -> DataFrame | Series: ...
def __next__(self) -> DataFrame | Series:
if self.nrows and self.nrows_seen >= self.nrows:
self.close()
raise StopIteration
lines = list(islice(self.data, self.chunksize))
if not lines:
self.close()
raise StopIteration
try:
lines_json = self._combine_lines(lines)
obj = self._get_object_parser(lines_json)
# Make sure that the returned objects have the right index.
obj.index = range(self.nrows_seen, self.nrows_seen + len(obj))
self.nrows_seen += len(obj)
except Exception as ex:
self.close()
raise ex
if self.dtype_backend is not lib.no_default:
with option_context("future.distinguish_nan_and_na", False):
return obj.convert_dtypes(
infer_objects=False, dtype_backend=self.dtype_backend
)
else:
return obj
def __enter__(self) -> Self:
return self
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> None:
self.close()
class Parser:
_split_keys: tuple[str, ...]
_default_orient: str
_STAMP_UNITS = ("s", "ms", "us", "ns")
_MIN_STAMPS = {
"s": 31536000,
"ms": 31536000000,
"us": 31536000000000,
"ns": 31536000000000000,
}
json: str
def __init__(
self,
json: str,
orient,
dtype: DtypeArg | None = None,
convert_axes: bool = True,
convert_dates: bool | list[str] = True,
keep_default_dates: bool = False,
precise_float: bool = False,
date_unit=None,
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
) -> None:
self.json = json
if orient is None:
orient = self._default_orient
self.orient = orient
self.dtype = dtype
if date_unit is not None:
date_unit = date_unit.lower()
if date_unit not in self._STAMP_UNITS:
raise ValueError(f"date_unit must be one of {self._STAMP_UNITS}")
self.min_stamp = self._MIN_STAMPS[date_unit]
else:
self.min_stamp = self._MIN_STAMPS["s"]
self.precise_float = precise_float
self.convert_axes = convert_axes
self.convert_dates = convert_dates
self.date_unit = date_unit
self.keep_default_dates = keep_default_dates
self.dtype_backend = dtype_backend
@final
def check_keys_split(self, decoded: dict) -> None:
"""
Checks that dict has only the appropriate keys for orient='split'.
"""
bad_keys = set(decoded.keys()).difference(set(self._split_keys))
if bad_keys:
bad_keys_joined = ", ".join(bad_keys)
raise ValueError(f"JSON data had unexpected key(s): {bad_keys_joined}")
@final
def parse(self) -> DataFrame | Series:
obj = self._parse()
if self.convert_axes:
obj = self._convert_axes(obj)
obj = self._try_convert_types(obj)
return obj
def _parse(self) -> DataFrame | Series:
raise AbstractMethodError(self)
@final
def _convert_axes(self, obj: DataFrame | Series) -> DataFrame | Series:
"""
Try to convert axes.
"""
for axis_name in obj._AXIS_ORDERS:
ax = obj._get_axis(axis_name)
ser = Series(ax, dtype=ax.dtype, copy=False)
new_ser, result = self._try_convert_data(
name=axis_name,
data=ser,
use_dtypes=False,
convert_dates=True,
is_axis=True,
)
if result:
new_axis = Index(new_ser, dtype=new_ser.dtype, copy=False)
setattr(obj, axis_name, new_axis)
return obj
def _try_convert_types(self, obj):
raise AbstractMethodError(self)
@final
def _try_convert_data(
self,
name: Hashable,
data: Series,
use_dtypes: bool = True,
convert_dates: bool | list[str] = True,
is_axis: bool = False,
) -> tuple[Series, bool]:
"""
Try to parse a Series into a column by inferring dtype.
"""
org_data = data
# don't try to coerce, unless a force conversion
if use_dtypes:
if not self.dtype:
if all(notna(data)):
return data, False
filled = data.fillna(np.nan)
return filled, True
elif self.dtype is True:
pass
elif not _should_convert_dates(
convert_dates, self.keep_default_dates, name
):
# convert_dates takes precedence over columns listed in dtypes
dtype = (
self.dtype.get(name) if isinstance(self.dtype, dict) else self.dtype
)
if dtype is not None:
try:
return data.astype(dtype), True
except (TypeError, ValueError):
return data, False
if convert_dates:
new_data = self._try_convert_to_date(data)
if new_data is not data:
return new_data, True
converted = False
if self.dtype_backend is not lib.no_default and not is_axis:
# Fall through for conversion later on
return data, True
elif is_string_dtype(data.dtype):
# try float
try:
data = data.astype("float64")
converted = True
except (TypeError, ValueError):
pass
if data.dtype.kind == "f" and data.dtype != "float64":
# coerce floats to 64
try:
data = data.astype("float64")
converted = True
except (TypeError, ValueError):
pass
# don't coerce 0-len data
if len(data) and data.dtype in ("float", "object"):
# coerce ints if we can
try:
new_data = org_data.astype("int64")
if (new_data == data).all():
data = new_data
converted = True
except (TypeError, ValueError, OverflowError):
pass
if data.dtype == "int" and data.dtype != "int64":
# coerce ints to 64
try:
data = data.astype("int64")
converted = True
except (TypeError, ValueError):
pass
# if we have an index, we want to preserve dtypes
if name == "index" and len(data):
if self.orient == "split":
return data, False
return data, converted
@final
def _try_convert_to_date(self, data: Series) -> Series:
"""
Try to parse an ndarray like into a date column.
Try to coerce object in epoch/iso formats and integer/float in epoch
formats.
"""
# no conversion on empty
if not len(data):
return data
new_data = data
if new_data.dtype == "object" or new_data.dtype == "string": # noqa: PLR1714
try:
new_data = data.astype("int64")
except OverflowError:
return data
except (TypeError, ValueError):
pass
# ignore numbers that are out of range
if issubclass(new_data.dtype.type, np.number):
in_range = (
isna(new_data._values)
| (new_data > self.min_stamp)
| (new_data._values == iNaT)
)
if not in_range.all():
return data
if new_data.dtype == "string":
with warnings.catch_warnings():
# ignore "Could not infer format" warnings from to_datetime
# which is incorrectly raised for non-date strings
warnings.simplefilter("ignore", UserWarning)
for format in (None, "iso8601", "mixed"):
try:
return to_datetime(new_data, errors="raise", format=format)
except Exception:
pass
else:
# numeric or mixed objects
date_units = (self.date_unit,) if self.date_unit else self._STAMP_UNITS
for date_unit in date_units:
try:
# In case of multiple possible units, infer the likely unit
# based on the first unit for which the parsed dates fit
# within the nanoseconds bounds
# -> do as_unit cast to ensure OutOfBounds error
data = to_datetime(new_data, errors="raise", unit=date_unit)
_ = data.dt.as_unit("ns")
break
except OutOfBoundsDatetime:
continue
except (ValueError, OverflowError, TypeError):
pass
return data
class SeriesParser(Parser):
_default_orient = "index"
_split_keys = ("name", "index", "data")
def _parse(self) -> Series:
data = ujson_loads(self.json, precise_float=self.precise_float)
if self.orient == "split":
decoded = {str(k): v for k, v in data.items()}
self.check_keys_split(decoded)
return Series(**decoded)
else:
return Series(data)
def _try_convert_types(self, obj: Series) -> Series:
obj, _ = self._try_convert_data("data", obj, convert_dates=self.convert_dates)
return obj
class FrameParser(Parser):
_default_orient = "columns"
_split_keys = ("columns", "index", "data")
def _parse(self) -> DataFrame:
json = self.json
orient = self.orient
if orient == "split":
decoded = {
str(k): v
for k, v in ujson_loads(json, precise_float=self.precise_float).items()
}
self.check_keys_split(decoded)
orig_names = [
(tuple(col) if isinstance(col, list) else col)
for col in decoded["columns"]
]
decoded["columns"] = dedup_names(
orig_names,
is_potential_multi_index(orig_names, None),
)
return DataFrame(dtype=None, **decoded)
elif orient == "index":
return DataFrame.from_dict(
ujson_loads(json, precise_float=self.precise_float),
dtype=None,
orient="index",
)
elif orient == "table":
return parse_table_schema(json, precise_float=self.precise_float)
else:
# includes orient == "columns"
return DataFrame(
ujson_loads(json, precise_float=self.precise_float), dtype=None
)
def _try_convert_types(self, obj: DataFrame) -> DataFrame:
arrays = []
for col_label, series in obj.items():
result, _ = self._try_convert_data(
col_label,
series,
convert_dates=_should_convert_dates(
self.convert_dates,
keep_default_dates=self.keep_default_dates,
col=col_label,
),
)
arrays.append(result.array)
return DataFrame._from_arrays(
arrays, obj.columns, obj.index, verify_integrity=False
)
def _should_convert_dates(
convert_dates: bool | list[str],
keep_default_dates: bool,
col: Hashable,
) -> bool:
"""
Return bool whether a DataFrame column should be cast to datetime.
"""
if convert_dates is False:
# convert_dates=True means follow keep_default_dates
return False
elif not isinstance(convert_dates, bool) and col in set(convert_dates):
return True
elif not keep_default_dates:
return False
elif not isinstance(col, str):
return False
col_lower = col.lower()
if (
col_lower.endswith(("_at", "_time"))
or col_lower in {"modified", "date", "datetime"}
or col_lower.startswith("timestamp")
):
return True
return False
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