h2o-py/h2o/h2o.py
def upload_file(path, destination_frame=None, header=0, sep=None, col_names=None, col_types=None,
                na_strings=None, skipped_columns=None):
    """
    Upload a dataset from the provided local path to the H2O cluster.

    Does a single-threaded push to H2O. Also see :meth:`import_file`.

    :param path: A path specifying the location of the data to upload.
    :param destination_frame:  The unique hex key assigned to the imported file. If none is given, a key will
        be automatically generated.
    :param header: -1 means the first line is data, 0 means guess, 1 means first line is header.
    :param sep: The field separator character. Values on each line of the file are separated by
        this character. If not provided, the parser will automatically detect the separator.
    :param col_names: A list of column names for the file.
    :param col_types: A list of types or a dictionary of column names to types to specify whether columns
        should be forced to a certain type upon import parsing. If a list, the types for elements that are
        one will be guessed. The possible types a column may have are:

        - "unknown" - this will force the column to be parsed as all NA
        - "uuid"    - the values in the column must be true UUID or will be parsed as NA
        - "string"  - force the column to be parsed as a string
        - "numeric" - force the column to be parsed as numeric. H2O will handle the compression of the numeric
          data in the optimal manner.
        - "enum"    - force the column to be parsed as a categorical column.
        - "time"    - force the column to be parsed as a time column. H2O will attempt to parse the following
          list of date time formats: (date) "yyyy-MM-dd", "yyyy MM dd", "dd-MMM-yy", "dd MMM yy", (time)
          "HH:mm:ss", "HH:mm:ss:SSS", "HH:mm:ss:SSSnnnnnn", "HH.mm.ss" "HH.mm.ss.SSS", "HH.mm.ss.SSSnnnnnn".
          Times can also contain "AM" or "PM".
    :param na_strings: A list of strings, or a list of lists of strings (one list per column), or a dictionary
        of column names to strings which are to be interpreted as missing values.
    :param skipped_columns: an integer lists of column indices to skip and not parsed into the final frame from the import file.

    :returns: a new :class:`H2OFrame` instance.

    :examples:
        >>> frame = h2o.upload_file("/path/to/local/data")
    """
    coltype = U(None, "unknown", "uuid", "string", "float", "real", "double", "int", "numeric",
                "categorical", "factor", "enum", "time")
    natype = U(str, [str])
    assert_is_type(path, str)
    assert_is_type(destination_frame, str, None)
    assert_is_type(header, -1, 0, 1)
    assert_is_type(sep, None, I(str, lambda s: len(s) == 1))
    assert_is_type(col_names, [str], None)
    assert_is_type(col_types, [coltype], {str: coltype}, None)
    assert_is_type(na_strings, [natype], {str: natype}, None)
    assert (skipped_columns==None) or isinstance(skipped_columns, list), \
        "The skipped_columns should be an list of column names!"

    check_frame_id(destination_frame)
    if path.startswith("~"):
        path = os.path.expanduser(path)
    return H2OFrame()._upload_parse(path, destination_frame, header, sep, col_names, col_types, na_strings, skipped_columns)
def upload_file(path, destination_frame=None, header=0, sep=None, col_names=None, col_types=None,
                na_strings=None, skipped_columns=None):
    coltype = U(None, "unknown", "uuid", "string", "float", "real", "double", "int", "numeric",
                "categorical", "factor", "enum", "time")
    natype = U(str, [str])
    assert_is_type(path, str)
    assert_is_type(destination_frame, str, None)
    assert_is_type(header, -1, 0, 1)
    assert_is_type(sep, None, I(str, lambda s: len(s) == 1))
    assert_is_type(col_names, [str], None)
    assert_is_type(col_types, [coltype], {str: coltype}, None)
    assert_is_type(na_strings, [natype], {str: natype}, None)
    assert (skipped_columns==None) or isinstance(skipped_columns, list), \
        "The skipped_columns should be an list of column names!"

    check_frame_id(destination_frame)
    if path.startswith("~"):
        path = os.path.expanduser(path)
    return H2OFrame()._upload_parse(path, destination_frame, header, sep, col_names, col_types, na_strings, skipped_columns)
def upload_file ( path , destination_frame = None , header = 0 , sep = None , col_names = None , col_types = None , na_strings = None , skipped_columns = None ) : coltype = U ( None , "unknown" , "uuid" , "string" , "float" , "real" , "double" , "int" , "numeric" , "categorical" , "factor" , "enum" , "time" ) natype = U ( str , [ str ] ) assert_is_type ( path , str ) assert_is_type ( destination_frame , str , None ) assert_is_type ( header , - 1 , 0 , 1 ) assert_is_type ( sep , None , I ( str , lambda s : len ( s ) == 1 ) ) assert_is_type ( col_names , [ str ] , None ) assert_is_type ( col_types , [ coltype ] , { str : coltype } , None ) assert_is_type ( na_strings , [ natype ] , { str : natype } , None ) assert ( skipped_columns == None ) or isinstance ( skipped_columns , list ) , "The skipped_columns should be an list of column names!" check_frame_id ( destination_frame ) if path . startswith ( "~" ) : path = os . path . expanduser ( path ) return H2OFrame ( ) . _upload_parse ( path , destination_frame , header , sep , col_names , col_types , na_strings , skipped_columns )
Upload a dataset from the provided local path to the H2O cluster.

    Does a single-threaded push to H2O. Also see :meth:`import_file`.

    :param path: A path specifying the location of the data to upload.
    :param destination_frame:  The unique hex key assigned to the imported file. If none is given, a key will
        be automatically generated.
    :param header: -1 means the first line is data, 0 means guess, 1 means first line is header.
    :param sep: The field separator character. Values on each line of the file are separated by
        this character. If not provided, the parser will automatically detect the separator.
    :param col_names: A list of column names for the file.
    :param col_types: A list of types or a dictionary of column names to types to specify whether columns
        should be forced to a certain type upon import parsing. If a list, the types for elements that are
        one will be guessed. The possible types a column may have are:

        - "unknown" - this will force the column to be parsed as all NA
        - "uuid"    - the values in the column must be true UUID or will be parsed as NA
        - "string"  - force the column to be parsed as a string
        - "numeric" - force the column to be parsed as numeric. H2O will handle the compression of the numeric
          data in the optimal manner.
        - "enum"    - force the column to be parsed as a categorical column.
        - "time"    - force the column to be parsed as a time column. H2O will attempt to parse the following
          list of date time formats: (date) "yyyy-MM-dd", "yyyy MM dd", "dd-MMM-yy", "dd MMM yy", (time)
          "HH:mm:ss", "HH:mm:ss:SSS", "HH:mm:ss:SSSnnnnnn", "HH.mm.ss" "HH.mm.ss.SSS", "HH.mm.ss.SSSnnnnnn".
          Times can also contain "AM" or "PM".
    :param na_strings: A list of strings, or a list of lists of strings (one list per column), or a dictionary
        of column names to strings which are to be interpreted as missing values.
    :param skipped_columns: an integer lists of column indices to skip and not parsed into the final frame from the import file.

    :returns: a new :class:`H2OFrame` instance.

    :examples:
        >>> frame = h2o.upload_file("/path/to/local/data")
Upload a dataset from the provided local path to the H2O cluster .