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 `_ . 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 `_ 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 `_. 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 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