# pandas.read_json

pandas.read_json(path_or_buf=None, orient=None, typ='frame', dtype=None, convert_axes=None, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, encoding_errors='strict', lines=False, chunksize=None, compression='infer', nrows=None, storage_options=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.

      Changed in version 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.

      Changed in version 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 since version 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.

    encoding_errors str, optional, default “strict”

      How encoding errors are treated. List of possible values .

      New in version 1.3.0.

    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.

      Changed in version 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.

      New in version 1.1.

    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` as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to `fsspec`. Please see `fsspec` and `urllib` for more details.

      New in version 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 `DataFrame` with a literal `Index` name of index gets written with `to_json()`, the subsequent read operation will incorrectly set the `Index` name to `None`. This is because index is also used by `DataFrame.to_json()` to denote a missing `Index` name, and the subsequent `read_json()` operation cannot distinguish between the two. The same limitation is encountered with a `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"}]}'