pandas.api.typing.JsonReader#

class pandas.api.typing.JsonReader(filepath_or_buffer, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, precise_float, date_unit, encoding, lines, chunksize, compression, nrows, storage_options=None, encoding_errors='strict', dtype_backend=<no_default>, engine='ujson')[source]#

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.

A JsonReader is returned by read_json() when chunksize is passed; it is not usually instantiated directly.

Parameters:
filepath_or_buffera 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. pandas accepts any os.PathLike, or file-like object with a read() method, such as a file handle or StringIO.

orientstr

Indication of expected JSON string format. Compatible JSON strings can be produced by to_json() with a corresponding orient value. See read_json() for the set of possible orients.

typ{“frame”, “series”}

The type of object to recover.

dtypebool or dict

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.

convert_axesbool or None

Try to convert the axes to the proper dtypes.

convert_datesbool or list of str

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_datesbool

If parsing dates (convert_dates is not False), then try to parse the default datelike columns. See read_json() for the criteria used to determine whether a column label is datelike.

precise_floatbool

Set to enable usage of higher precision (strtod) function when decoding string to double values.

date_unitstr or 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.

encodingstr or None

The encoding to use to decode py3 bytes.

linesbool

Read the file as a json object per line.

chunksizeint or None

Return a JsonReader for iteration, yielding chunksize lines at a time. Can only be passed if lines=True. If this is None, the file will be read into memory all at once.

compressionstr or dict

For on-the-fly decompression of on-disk data, see read_json() for the accepted values.

nrowsint or None

The number of lines from the line-delimited json file that has to be read. Can only be passed if lines=True. If this is None, all the rows will be returned.

storage_optionsdict, optional

Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. See read_json() for more details.

encoding_errorsstr, optional, default “strict”

How encoding errors are treated. List of possible values .

dtype_backend{“numpy_nullable”, “pyarrow”}

Back-end data type applied to the resultant DataFrame (still experimental). See read_json() for more details.

engine{“ujson”, “pyarrow”}, default “ujson”

Parser engine to use. See read_json() for the restrictions on the "pyarrow" engine.

See also

read_json

Convert a JSON string to pandas object.

Examples

>>> from io import StringIO
>>> df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
>>> reader = pd.read_json(
...     StringIO(df.to_json(orient="records", lines=True)),
...     lines=True,
...     chunksize=1,
... )
>>> for chunk in reader:
...     print(chunk)
   a  b
0  1  3
   a  b
1  2  4

Methods

close()

Close the underlying file handle, if one was opened by the reader.

read()

Read the whole JSON input into a pandas object.
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