from abc import ABC, abstractmethod from collections import abc import functools from io import StringIO from itertools import islice from typing import Any, Callable, Mapping, Optional, Tuple, Type, Union import numpy as np import pandas._libs.json as json from pandas._libs.tslibs import iNaT from pandas._typing import ( CompressionOptions, IndexLabel, JSONSerializable, StorageOptions, ) from pandas.errors import AbstractMethodError from pandas.util._decorators import deprecate_kwarg, deprecate_nonkeyword_arguments, doc from pandas.core.dtypes.common import ensure_str, is_period_dtype from pandas import DataFrame, MultiIndex, Series, isna, notna, to_datetime from pandas.core import generic from pandas.core.construction import create_series_with_explicit_dtype from pandas.core.generic import NDFrame from pandas.core.reshape.concat import concat from pandas.io.common import ( IOHandles, file_exists, get_handle, is_fsspec_url, is_url, 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 from pandas.io.parsers import validate_integer loads = json.loads dumps = json.dumps TABLE_SCHEMA_VERSION = "0.20.0" # interface to/from def to_json( path_or_buf, obj: NDFrame, orient: Optional[str] = None, date_format: str = "epoch", double_precision: int = 10, force_ascii: bool = True, date_unit: str = "ms", default_handler: Optional[Callable[[Any], JSONSerializable]] = None, lines: bool = False, compression: CompressionOptions = "infer", index: bool = True, indent: int = 0, storage_options: StorageOptions = None, ): if not index and orient not in ["split", "table"]: raise ValueError( "'index=False' is only valid when 'orient' is 'split' or 'table'" ) if lines and orient != "records": raise ValueError("'lines' keyword only valid when 'orient' is records") if orient == "table" and isinstance(obj, Series): obj = obj.to_frame(name=obj.name or "values") 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, "w", compression=compression, storage_options=storage_options ) as handles: handles.handle.write(s) else: return s class Writer(ABC): _default_orient: str def __init__( self, obj, orient: Optional[str], date_format: str, double_precision: int, ensure_ascii: bool, date_unit: str, index: bool, default_handler: Optional[Callable[[Any], JSONSerializable]] = None, indent: int = 0, ): 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.is_copy = None self._format_axes() def _format_axes(self): raise AbstractMethodError(self) def write(self): iso_dates = self.date_format == "iso" return 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) -> Union[NDFrame, Mapping[IndexLabel, Any]]: """Object to write in JSON format.""" pass class SeriesWriter(Writer): _default_orient = "index" @property def obj_to_write(self) -> Union[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): 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) -> Union[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): """ 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: Optional[str], date_format: str, double_precision: int, ensure_ascii: bool, date_unit: str, index: bool, default_handler: Optional[Callable[[Any], JSONSerializable]] = None, indent: int = 0, ): """ 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) # 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) obj = obj.copy() timedeltas = obj.select_dtypes(include=["timedelta"]).columns if len(timedeltas): obj[timedeltas] = obj[timedeltas].applymap(lambda x: x.isoformat()) # Convert PeriodIndex to datetimes before serializing if is_period_dtype(obj.index.dtype): obj.index = obj.index.to_timestamp() # exclude index from obj if index=False if not self.index: self.obj = obj.reset_index(drop=True) else: self.obj = obj.reset_index(drop=False) self.date_format = "iso" self.orient = "records" self.index = index @property def obj_to_write(self) -> Union[NDFrame, Mapping[IndexLabel, Any]]: return {"schema": self.schema, "data": self.obj} @doc(storage_options=generic._shared_docs["storage_options"]) @deprecate_kwarg(old_arg_name="numpy", new_arg_name=None) @deprecate_nonkeyword_arguments( version="2.0", allowed_args=["path_or_buf"], stacklevel=3 ) def read_json( path_or_buf=None, orient=None, typ="frame", dtype=None, convert_axes=None, convert_dates=True, keep_default_dates: bool = True, numpy: bool = False, precise_float: bool = False, date_unit=None, encoding=None, lines: bool = False, chunksize: Optional[int] = None, compression: CompressionOptions = "infer", nrows: Optional[int] = None, storage_options: StorageOptions = None, ): """ Convert a JSON string to pandas object. Parameters ---------- path_or_buf : a valid JSON str, 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 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 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. .. versionchanged:: 0.25.0 Not applicable for ``orient='table'``. convert_axes : bool, default None Try to convert the axes to the proper dtypes. For all ``orient`` values except ``'table'``, default is True. .. versionchanged:: 0.25.0 Not applicable for ``orient='table'``. 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'``. numpy : bool, default False Direct decoding to numpy arrays. Supports numeric data only, but non-numeric column and index labels are supported. Note also that the JSON ordering MUST be the same for each term if numpy=True. .. deprecated:: 1.0.0 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. 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. .. versionchanged:: 1.2 ``JsonReader`` is a context manager. compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer' For on-the-fly decompression of on-disk data. If 'infer', then use gzip, bz2, zip or xz if path_or_buf is a string ending in '.gz', '.bz2', '.zip', or 'xz', respectively, and no decompression otherwise. If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression. 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. .. versionadded:: 1.1 {storage_options} .. versionadded:: 1.2.0 Returns ------- Series or DataFrame 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. 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 -------- >>> 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(_, orient='split') 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(_, orient='index') 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(_, orient='records') 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"}}, {{"name": "col 1", "type": "string"}}, {{"name": "col 2", "type": "string"}}], "primaryKey": "index", "pandas_version": "0.20.0"}}, "data": [{{"index": "row 1", "col 1": "a", "col 2": "b"}}, {{"index": "row 2", "col 1": "c", "col 2": "d"}}]}}' """ 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'") if dtype is None and orient != "table": dtype = True 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, numpy=numpy, precise_float=precise_float, date_unit=date_unit, encoding=encoding, lines=lines, chunksize=chunksize, compression=compression, nrows=nrows, storage_options=storage_options, ) if chunksize: return json_reader with json_reader: return json_reader.read() class JsonReader(abc.Iterator): """ 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, dtype, convert_axes, convert_dates, keep_default_dates: bool, numpy: bool, precise_float: bool, date_unit, encoding, lines: bool, chunksize: Optional[int], compression: CompressionOptions, nrows: Optional[int], storage_options: StorageOptions = 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.numpy = numpy self.precise_float = precise_float self.date_unit = date_unit self.encoding = encoding self.compression = compression self.storage_options = storage_options self.lines = lines self.chunksize = chunksize self.nrows_seen = 0 self.nrows = nrows self.handles: Optional[IOHandles] = None 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.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") data = self._get_data_from_filepath(filepath_or_buffer) self.data = self._preprocess_data(data) def _preprocess_data(self, data): """ At this point, the data either has a `read` attribute (e.g. a file object or a StringIO) or is a string that is a JSON document. If self.chunksize, we prepare the data for the `__next__` method. Otherwise, we read it into memory for the `read` method. """ if hasattr(data, "read") and not (self.chunksize or self.nrows): data = data.read() self.close() if not hasattr(data, "read") and (self.chunksize or self.nrows): data = StringIO(data) return 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) 3. JSON string This method turns (1) into (2) to simplify the rest of the processing. It returns input types (2) and (3) unchanged. """ # if it is a string but the file does not exist, it might be a JSON string filepath_or_buffer = stringify_path(filepath_or_buffer) if ( not isinstance(filepath_or_buffer, str) or is_url(filepath_or_buffer) or is_fsspec_url(filepath_or_buffer) or file_exists(filepath_or_buffer) ): self.handles = get_handle( filepath_or_buffer, "r", encoding=self.encoding, compression=self.compression, storage_options=self.storage_options, ) 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))}]' ) def read(self): """ Read the whole JSON input into a pandas object. """ 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) self.close() return obj def _get_object_parser(self, json): """ 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, "numpy": self.numpy, "precise_float": self.precise_float, "date_unit": self.date_unit, } obj = None if typ == "frame": obj = FrameParser(json, **kwargs).parse() if typ == "series" or obj is None: if not isinstance(dtype, bool): kwargs["dtype"] = dtype obj = SeriesParser(json, **kwargs).parse() return obj def close(self): """ 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 __next__(self): if self.nrows: if self.nrows_seen >= self.nrows: self.close() raise StopIteration lines = list(islice(self.data, self.chunksize)) if lines: 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) return obj self.close() raise StopIteration def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): 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, } def __init__( self, json, orient, dtype=None, convert_axes=True, convert_dates=True, keep_default_dates=False, numpy=False, precise_float=False, date_unit=None, ): self.json = json if orient is None: orient = self._default_orient self.orient = orient self.dtype = dtype if orient == "split": numpy = False 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.numpy = numpy 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.obj = None def check_keys_split(self, decoded): """ 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}") def parse(self): # try numpy numpy = self.numpy if numpy: self._parse_numpy() else: self._parse_no_numpy() if self.obj is None: return None if self.convert_axes: self._convert_axes() self._try_convert_types() return self.obj def _parse_numpy(self): raise AbstractMethodError(self) def _parse_no_numpy(self): raise AbstractMethodError(self) def _convert_axes(self): """ Try to convert axes. """ obj = self.obj assert obj is not None # for mypy for axis_name in obj._AXIS_ORDERS: new_axis, result = self._try_convert_data( name=axis_name, data=obj._get_axis(axis_name), use_dtypes=False, convert_dates=True, ) if result: setattr(self.obj, axis_name, new_axis) def _try_convert_types(self): raise AbstractMethodError(self) def _try_convert_data(self, name, data, use_dtypes=True, convert_dates=True): """ Try to parse a ndarray like into a column by inferring dtype. """ # don't try to coerce, unless a force conversion if use_dtypes: if not self.dtype: if all(notna(data)): return data, False return data.fillna(np.nan), True elif self.dtype is True: pass else: # dtype to force dtype = ( self.dtype.get(name) if isinstance(self.dtype, dict) else self.dtype ) if dtype is not None: try: dtype = np.dtype(dtype) return data.astype(dtype), True except (TypeError, ValueError): return data, False if convert_dates: new_data, result = self._try_convert_to_date(data) if result: return new_data, True result = False if data.dtype == "object": # try float try: data = data.astype("float64") result = True except (TypeError, ValueError): pass if data.dtype.kind == "f": if data.dtype != "float64": # coerce floats to 64 try: data = data.astype("float64") result = True except (TypeError, ValueError): pass # don't coerce 0-len data if len(data) and (data.dtype == "float" or data.dtype == "object"): # coerce ints if we can try: new_data = data.astype("int64") if (new_data == data).all(): data = new_data result = True except (TypeError, ValueError, OverflowError): pass # coerce ints to 64 if data.dtype == "int": # coerce floats to 64 try: data = data.astype("int64") result = True except (TypeError, ValueError): pass return data, result def _try_convert_to_date(self, data): """ Try to parse a ndarray like into a date column. Try to coerce object in epoch/iso formats and integer/float in epoch formats. Return a boolean if parsing was successful. """ # no conversion on empty if not len(data): return data, False new_data = data if new_data.dtype == "object": try: new_data = data.astype("int64") except (TypeError, ValueError, OverflowError): 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, False date_units = (self.date_unit,) if self.date_unit else self._STAMP_UNITS for date_unit in date_units: try: new_data = to_datetime(new_data, errors="raise", unit=date_unit) except (ValueError, OverflowError, TypeError): continue return new_data, True return data, False def _try_convert_dates(self): raise AbstractMethodError(self) class SeriesParser(Parser): _default_orient = "index" _split_keys = ("name", "index", "data") def _parse_no_numpy(self): data = 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) self.obj = create_series_with_explicit_dtype(**decoded) else: self.obj = create_series_with_explicit_dtype(data, dtype_if_empty=object) def _parse_numpy(self): load_kwargs = { "dtype": None, "numpy": True, "precise_float": self.precise_float, } if self.orient in ["columns", "index"]: load_kwargs["labelled"] = True loads_ = functools.partial(loads, **load_kwargs) data = loads_(self.json) if self.orient == "split": decoded = {str(k): v for k, v in data.items()} self.check_keys_split(decoded) self.obj = create_series_with_explicit_dtype(**decoded) elif self.orient in ["columns", "index"]: # error: "create_series_with_explicit_dtype" # gets multiple values for keyword argument "dtype_if_empty self.obj = create_series_with_explicit_dtype( *data, dtype_if_empty=object ) # type:ignore[misc] else: self.obj = create_series_with_explicit_dtype(data, dtype_if_empty=object) def _try_convert_types(self): if self.obj is None: return obj, result = self._try_convert_data( "data", self.obj, convert_dates=self.convert_dates ) if result: self.obj = obj class FrameParser(Parser): _default_orient = "columns" _split_keys = ("columns", "index", "data") def _parse_numpy(self): json = self.json orient = self.orient if orient == "columns": args = loads( json, dtype=None, numpy=True, labelled=True, precise_float=self.precise_float, ) if len(args): args = (args[0].T, args[2], args[1]) self.obj = DataFrame(*args) elif orient == "split": decoded = loads( json, dtype=None, numpy=True, precise_float=self.precise_float ) decoded = {str(k): v for k, v in decoded.items()} self.check_keys_split(decoded) self.obj = DataFrame(**decoded) elif orient == "values": self.obj = DataFrame( loads(json, dtype=None, numpy=True, precise_float=self.precise_float) ) else: self.obj = DataFrame( *loads( json, dtype=None, numpy=True, labelled=True, precise_float=self.precise_float, ) ) def _parse_no_numpy(self): json = self.json orient = self.orient if orient == "columns": self.obj = DataFrame( loads(json, precise_float=self.precise_float), dtype=None ) elif orient == "split": decoded = { str(k): v for k, v in loads(json, precise_float=self.precise_float).items() } self.check_keys_split(decoded) self.obj = DataFrame(dtype=None, **decoded) elif orient == "index": self.obj = DataFrame.from_dict( loads(json, precise_float=self.precise_float), dtype=None, orient="index", ) elif orient == "table": self.obj = parse_table_schema(json, precise_float=self.precise_float) else: self.obj = DataFrame( loads(json, precise_float=self.precise_float), dtype=None ) def _process_converter(self, f, filt=None): """ Take a conversion function and possibly recreate the frame. """ if filt is None: filt = lambda col, c: True obj = self.obj assert obj is not None # for mypy needs_new_obj = False new_obj = {} for i, (col, c) in enumerate(obj.items()): if filt(col, c): new_data, result = f(col, c) if result: c = new_data needs_new_obj = True new_obj[i] = c if needs_new_obj: # possibly handle dup columns new_frame = DataFrame(new_obj, index=obj.index) new_frame.columns = obj.columns self.obj = new_frame def _try_convert_types(self): if self.obj is None: return if self.convert_dates: self._try_convert_dates() self._process_converter( lambda col, c: self._try_convert_data(col, c, convert_dates=False) ) def _try_convert_dates(self): if self.obj is None: return # our columns to parse convert_dates = self.convert_dates if convert_dates is True: convert_dates = [] convert_dates = set(convert_dates) def is_ok(col) -> bool: """ Return if this col is ok to try for a date parse. """ if not isinstance(col, str): return False col_lower = col.lower() if ( col_lower.endswith("_at") or col_lower.endswith("_time") or col_lower == "modified" or col_lower == "date" or col_lower == "datetime" or col_lower.startswith("timestamp") ): return True return False self._process_converter( lambda col, c: self._try_convert_to_date(c), lambda col, c: ( (self.keep_default_dates and is_ok(col)) or col in convert_dates ), )