# dask.dataframe.to_json

dask.dataframe.to_json(df, url_path, orient='records', lines=None, storage_options=None, compute=True, encoding='utf-8', errors='strict', compression=None, compute_kwargs=None, name_function=None, **kwargs)

  Write dataframe into JSON text files

  This utilises `pandas.DataFrame.to_json()`, and most parameters are passed through - see its docstring.

  Differences: orient is ‘records’ by default, with lines=True; this produces the kind of JSON output that is most common in big-data applications, and which can be chunked when reading (see `read_json()`).

  Parameters:
    df: dask.DataFrame

      Data to save

    url_path: str, list of str

      Location to write to. If a string, and there are more than one partitions in df, should include a glob character to expand into a set of file names, or provide a `name_function=` parameter. Supports protocol specifications such as `"s3://"`.

    encoding, errors:

      The text encoding to implement, e.g., “utf-8” and how to respond to errors in the conversion (see `str.encode()`).

    orient, lines, kwargs

      passed to pandas; if not specified, lines=True when orient=’records’, False otherwise.

    storage_options: dict

      Passed to backend file-system implementation

    compute: bool

      If true, immediately executes. If False, returns a set of delayed objects, which can be computed at a later time.

    compute_kwargs dict, optional

      Options to be passed in to the compute method

    compression string or None

      String like ‘gzip’ or ‘xz’.

    name_function callable, default None

      Function accepting an integer (partition index) and producing a string to replace the asterisk in the given filename globstring. Should preserve the lexicographic order of partitions.