tabulate.py

# -*- coding: utf-8 -*-

"""Pretty-print tabular data."""

from __future__ import print_function
from __future__ import unicode_literals
from collections import namedtuple
from platform import python_version_tuple
import re


if python_version_tuple()[0] < "3":
    from itertools import izip_longest
    from functools import partial
    _none_type = type(None)
    _int_type = int
    _float_type = float
    _text_type = unicode
    _binary_type = str
else:
    from itertools import zip_longest as izip_longest
    from functools import reduce, partial
    _none_type = type(None)
    _int_type = int
    _float_type = float
    _text_type = str
    _binary_type = bytes


__all__ = ["tabulate", "tabulate_formats", "simple_separated_format"]
__version__ = "0.7.3"


Line = namedtuple("Line", ["begin", "hline", "sep", "end"])


DataRow = namedtuple("DataRow", ["begin", "sep", "end"])


# A table structure is suppposed to be:
#
#     --- lineabove ---------
#         headerrow
#     --- linebelowheader ---
#         datarow
#     --- linebewteenrows ---
#     ... (more datarows) ...
#     --- linebewteenrows ---
#         last datarow
#     --- linebelow ---------
#
# TableFormat's line* elements can be
#
#   - either None, if the element is not used,
#   - or a Line tuple,
#   - or a function: [col_widths], [col_alignments] -> string.
#
# TableFormat's *row elements can be
#
#   - either None, if the element is not used,
#   - or a DataRow tuple,
#   - or a function: [cell_values], [col_widths], [col_alignments] -> string.
#
# padding (an integer) is the amount of white space around data values.
#
# with_header_hide:
#
#   - either None, to display all table elements unconditionally,
#   - or a list of elements not to be displayed if the table has column headers.
#
TableFormat = namedtuple("TableFormat", ["lineabove", "linebelowheader",
                                         "linebetweenrows", "linebelow",
                                         "headerrow", "datarow",
                                         "padding", "with_header_hide"])


def _pipe_segment_with_colons(align, colwidth):
    """Return a segment of a horizontal line with optional colons which
    indicate column's alignment (as in `pipe` output format)."""
    w = colwidth
    if align in ["right", "decimal"]:
        return ('-' * (w - 1)) + ":"
    elif align == "center":
        return ":" + ('-' * (w - 2)) + ":"
    elif align == "left":
        return ":" + ('-' * (w - 1))
    else:
        return '-' * w


def _pipe_line_with_colons(colwidths, colaligns):
    """Return a horizontal line with optional colons to indicate column's
    alignment (as in `pipe` output format)."""
    segments = [_pipe_segment_with_colons(a, w) for a, w in zip(colaligns, colwidths)]
    return "|" + "|".join(segments) + "|"


def _mediawiki_row_with_attrs(separator, cell_values, colwidths, colaligns):
    alignment = { "left":    '',
                  "right":   'align="right"| ',
                  "center":  'align="center"| ',
                  "decimal": 'align="right"| ' }
    # hard-coded padding _around_ align attribute and value together
    # rather than padding parameter which affects only the value
    values_with_attrs = [' ' + alignment.get(a, '') + c + ' '
                         for c, a in zip(cell_values, colaligns)]
    colsep = separator*2
    return (separator + colsep.join(values_with_attrs)).rstrip()


def _latex_line_begin_tabular(colwidths, colaligns):
    alignment = { "left": "l", "right": "r", "center": "c", "decimal": "r" }
    tabular_columns_fmt = "".join([alignment.get(a, "l") for a in colaligns])
    return "\\begin{tabular}{" + tabular_columns_fmt + "}\n\hline"


_table_formats = {"simple":
                  TableFormat(lineabove=Line("", "-", "  ", ""),
                              linebelowheader=Line("", "-", "  ", ""),
                              linebetweenrows=None,
                              linebelow=Line("", "-", "  ", ""),
                              headerrow=DataRow("", "  ", ""),
                              datarow=DataRow("", "  ", ""),
                              padding=0,
                              with_header_hide=["lineabove", "linebelow"]),
                  "plain":
                  TableFormat(lineabove=None, linebelowheader=None,
                              linebetweenrows=None, linebelow=None,
                              headerrow=DataRow("", "  ", ""),
                              datarow=DataRow("", "  ", ""),
                              padding=0, with_header_hide=None),
                  "grid":
                  TableFormat(lineabove=Line("+", "-", "+", "+"),
                              linebelowheader=Line("+", "=", "+", "+"),
                              linebetweenrows=Line("+", "-", "+", "+"),
                              linebelow=Line("+", "-", "+", "+"),
                              headerrow=DataRow("|", "|", "|"),
                              datarow=DataRow("|", "|", "|"),
                              padding=1, with_header_hide=None),
                  "pipe":
                  TableFormat(lineabove=_pipe_line_with_colons,
                              linebelowheader=_pipe_line_with_colons,
                              linebetweenrows=None,
                              linebelow=None,
                              headerrow=DataRow("|", "|", "|"),
                              datarow=DataRow("|", "|", "|"),
                              padding=1,
                              with_header_hide=["lineabove"]),
                  "orgtbl":
                  TableFormat(lineabove=None,
                              linebelowheader=Line("|", "-", "+", "|"),
                              linebetweenrows=None,
                              linebelow=None,
                              headerrow=DataRow("|", "|", "|"),
                              datarow=DataRow("|", "|", "|"),
                              padding=1, with_header_hide=None),
                  "rst":
                  TableFormat(lineabove=Line("", "=", "  ", ""),
                              linebelowheader=Line("", "=", "  ", ""),
                              linebetweenrows=None,
                              linebelow=Line("", "=", "  ", ""),
                              headerrow=DataRow("", "  ", ""),
                              datarow=DataRow("", "  ", ""),
                              padding=0, with_header_hide=None),
                  "mediawiki":
                  TableFormat(lineabove=Line("{| class=\"wikitable\" style=\"text-align: left;\"",
                                             "", "", "\n|+ <!-- caption -->\n|-"),
                              linebelowheader=Line("|-", "", "", ""),
                              linebetweenrows=Line("|-", "", "", ""),
                              linebelow=Line("|}", "", "", ""),
                              headerrow=partial(_mediawiki_row_with_attrs, "!"),
                              datarow=partial(_mediawiki_row_with_attrs, "|"),
                              padding=0, with_header_hide=None),
                  "latex":
                  TableFormat(lineabove=_latex_line_begin_tabular,
                              linebelowheader=Line("\\hline", "", "", ""),
                              linebetweenrows=None,
                              linebelow=Line("\\hline\n\\end{tabular}", "", "", ""),
                              headerrow=DataRow("", "&", "\\\\"),
                              datarow=DataRow("", "&", "\\\\"),
                              padding=1, with_header_hide=None),
                  "tsv":
                  TableFormat(lineabove=None, linebelowheader=None,
                              linebetweenrows=None, linebelow=None,
                              headerrow=DataRow("", "\t", ""),
                              datarow=DataRow("", "\t", ""),
                              padding=0, with_header_hide=None)}


tabulate_formats = list(sorted(_table_formats.keys()))


_invisible_codes = re.compile("\x1b\[\d*m")  # ANSI color codes
_invisible_codes_bytes = re.compile(b"\x1b\[\d*m")  # ANSI color codes


def simple_separated_format(separator):
    """Construct a simple TableFormat with columns separated by a separator.

    >>> tsv = simple_separated_format("\\t") ; \
        tabulate([["foo", 1], ["spam", 23]], tablefmt=tsv) == 'foo \\t 1\\nspam\\t23'
    True

    """
    return TableFormat(None, None, None, None,
                       headerrow=DataRow('', separator, ''),
                       datarow=DataRow('', separator, ''),
                       padding=0, with_header_hide=None)


def _isconvertible(conv, string):
    try:
        n = conv(string)
        return True
    except ValueError:
        return False


def _isnumber(string):
    """
    >>> _isnumber("123.45")
    True
    >>> _isnumber("123")
    True
    >>> _isnumber("spam")
    False
    """
    return _isconvertible(float, string)


def _isint(string):
    """
    >>> _isint("123")
    True
    >>> _isint("123.45")
    False
    """
    return type(string) is int or \
           (isinstance(string, _binary_type) or isinstance(string, _text_type)) and \
           _isconvertible(int, string)


def _type(string, has_invisible=True):
    """The least generic type (type(None), int, float, str, unicode).

    >>> _type(None) is type(None)
    True
    >>> _type("foo") is type("")
    True
    >>> _type("1") is type(1)
    True
    >>> _type('\x1b[31m42\x1b[0m') is type(42)
    True
    >>> _type('\x1b[31m42\x1b[0m') is type(42)
    True

    """

    if has_invisible and \
       (isinstance(string, _text_type) or isinstance(string, _binary_type)):
        string = _strip_invisible(string)

    if string is None:
        return _none_type
    elif hasattr(string, "isoformat"):  # datetime.datetime, date, and time
        return _text_type
    elif _isint(string):
        return int
    elif _isnumber(string):
        return float
    elif isinstance(string, _binary_type):
        return _binary_type
    else:
        return _text_type


def _afterpoint(string):
    """Symbols after a decimal point, -1 if the string lacks the decimal point.

    >>> _afterpoint("123.45")
    2
    >>> _afterpoint("1001")
    -1
    >>> _afterpoint("eggs")
    -1
    >>> _afterpoint("123e45")
    2

    """
    if _isnumber(string):
        if _isint(string):
            return -1
        else:
            pos = string.rfind(".")
            pos = string.lower().rfind("e") if pos < 0 else pos
            if pos >= 0:
                return len(string) - pos - 1
            else:
                return -1  # no point
    else:
        return -1  # not a number


def _padleft(width, s, has_invisible=True):
    """Flush right.

    >>> _padleft(6, '\u044f\u0439\u0446\u0430') == '  \u044f\u0439\u0446\u0430'
    True

    """
    iwidth = width + len(s) - len(_strip_invisible(s)) if has_invisible else width
    fmt = "{0:>%ds}" % iwidth
    return fmt.format(s)


def _padright(width, s, has_invisible=True):
    """Flush left.

    >>> _padright(6, '\u044f\u0439\u0446\u0430') == '\u044f\u0439\u0446\u0430  '
    True

    """
    iwidth = width + len(s) - len(_strip_invisible(s)) if has_invisible else width
    fmt = "{0:<%ds}" % iwidth
    return fmt.format(s)


def _padboth(width, s, has_invisible=True):
    """Center string.

    >>> _padboth(6, '\u044f\u0439\u0446\u0430') == ' \u044f\u0439\u0446\u0430 '
    True

    """
    iwidth = width + len(s) - len(_strip_invisible(s)) if has_invisible else width
    fmt = "{0:^%ds}" % iwidth
    return fmt.format(s)


def _strip_invisible(s):
    "Remove invisible ANSI color codes."
    if isinstance(s, _text_type):
        return re.sub(_invisible_codes, "", s)
    else:  # a bytestring
        return re.sub(_invisible_codes_bytes, "", s)


def _visible_width(s):
    """Visible width of a printed string. ANSI color codes are removed.

    >>> _visible_width('\x1b[31mhello\x1b[0m'), _visible_width("world")
    (5, 5)

    """
    if isinstance(s, _text_type) or isinstance(s, _binary_type):
        return len(_strip_invisible(s))
    else:
        return len(_text_type(s))


def _align_column(strings, alignment, minwidth=0, has_invisible=True):
    """[string] -> [padded_string]

    >>> list(map(str,_align_column(["12.345", "-1234.5", "1.23", "1234.5", "1e+234", "1.0e234"], "decimal")))
    ['   12.345  ', '-1234.5    ', '    1.23   ', ' 1234.5    ', '    1e+234 ', '    1.0e234']

    >>> list(map(str,_align_column(['123.4', '56.7890'], None)))
    ['123.4', '56.7890']

    """
    if alignment == "right":
        strings = [s.strip() for s in strings]
        padfn = _padleft
    elif alignment == "center":
        strings = [s.strip() for s in strings]
        padfn = _padboth
    elif alignment == "decimal":
        decimals = [_afterpoint(s) for s in strings]
        maxdecimals = max(decimals)
        strings = [s + (maxdecimals - decs) * " "
                   for s, decs in zip(strings, decimals)]
        padfn = _padleft
    elif not alignment:
        return strings
    else:
        strings = [s.strip() for s in strings]
        padfn = _padright

    if has_invisible:
        width_fn = _visible_width
    else:
        width_fn = len

    maxwidth = max(max(map(width_fn, strings)), minwidth)
    padded_strings = [padfn(maxwidth, s, has_invisible) for s in strings]
    return padded_strings


def _more_generic(type1, type2):
    types = { _none_type: 0, int: 1, float: 2, _binary_type: 3, _text_type: 4 }
    invtypes = { 4: _text_type, 3: _binary_type, 2: float, 1: int, 0: _none_type }
    moregeneric = max(types.get(type1, 4), types.get(type2, 4))
    return invtypes[moregeneric]


def _column_type(strings, has_invisible=True):
    """The least generic type all column values are convertible to.

    >>> _column_type(["1", "2"]) is _int_type
    True
    >>> _column_type(["1", "2.3"]) is _float_type
    True
    >>> _column_type(["1", "2.3", "four"]) is _text_type
    True
    >>> _column_type(["four", '\u043f\u044f\u0442\u044c']) is _text_type
    True
    >>> _column_type([None, "brux"]) is _text_type
    True
    >>> _column_type([1, 2, None]) is _int_type
    True
    >>> import datetime as dt
    >>> _column_type([dt.datetime(1991,2,19), dt.time(17,35)]) is _text_type
    True

    """
    types = [_type(s, has_invisible) for s in strings ]
    return reduce(_more_generic, types, int)


def _format(val, valtype, floatfmt, missingval=""):
    """Format a value accoding to its type.

    Unicode is supported:

    >>> hrow = ['\u0431\u0443\u043a\u0432\u0430', '\u0446\u0438\u0444\u0440\u0430'] ; \
        tbl = [['\u0430\u0437', 2], ['\u0431\u0443\u043a\u0438', 4]] ; \
        good_result = '\\u0431\\u0443\\u043a\\u0432\\u0430      \\u0446\\u0438\\u0444\\u0440\\u0430\\n-------  -------\\n\\u0430\\u0437             2\\n\\u0431\\u0443\\u043a\\u0438           4' ; \
        tabulate(tbl, headers=hrow) == good_result
    True

    """
    if val is None:
        return missingval

    if valtype in [int, _text_type]:
        return "{0}".format(val)
    elif valtype is _binary_type:
        try:
            return _text_type(val, "ascii")
        except TypeError:
            return _text_type(val)
    elif valtype is float:
        return format(float(val), floatfmt)
    else:
        return "{0}".format(val)


def _align_header(header, alignment, width):
    if alignment == "left":
        return _padright(width, header)
    elif alignment == "center":
        return _padboth(width, header)
    elif not alignment:
        return "{0}".format(header)
    else:
        return _padleft(width, header)


def _normalize_tabular_data(tabular_data, headers):
    """Transform a supported data type to a list of lists, and a list of headers.

    Supported tabular data types:

    * list-of-lists or another iterable of iterables

    * list of named tuples (usually used with headers="keys")

    * list of dicts (usually used with headers="keys")

    * list of OrderedDicts (usually used with headers="keys")

    * 2D NumPy arrays

    * NumPy record arrays (usually used with headers="keys")

    * dict of iterables (usually used with headers="keys")

    * pandas.DataFrame (usually used with headers="keys")

    The first row can be used as headers if headers="firstrow",
    column indices can be used as headers if headers="keys".

    """

    if hasattr(tabular_data, "keys") and hasattr(tabular_data, "values"):
        # dict-like and pandas.DataFrame?
        if hasattr(tabular_data.values, "__call__"):
            # likely a conventional dict
            keys = tabular_data.keys()
            rows = list(izip_longest(*tabular_data.values()))  # columns have to be transposed
        elif hasattr(tabular_data, "index"):
            # values is a property, has .index => it's likely a pandas.DataFrame (pandas 0.11.0)
            keys = tabular_data.keys()
            vals = tabular_data.values  # values matrix doesn't need to be transposed
            names = tabular_data.index
            rows = [[v]+list(row) for v,row in zip(names, vals)]
        else:
            raise ValueError("tabular data doesn't appear to be a dict or a DataFrame")

        if headers == "keys":
            headers = list(map(_text_type,keys))  # headers should be strings

    else:  # it's a usual an iterable of iterables, or a NumPy array
        rows = list(tabular_data)

        if (headers == "keys" and
            hasattr(tabular_data, "dtype") and
            getattr(tabular_data.dtype, "names")):
            # numpy record array
            headers = tabular_data.dtype.names
        elif (headers == "keys"
              and len(rows) > 0
              and isinstance(rows[0], tuple)
              and hasattr(rows[0], "_fields")):
            # namedtuple
            headers = list(map(_text_type, rows[0]._fields))
        elif (len(rows) > 0
              and isinstance(rows[0], dict)):
            # dict or OrderedDict
            uniq_keys = set() # implements hashed lookup
            keys = [] # storage for set
            if headers == "firstrow":
                firstdict = rows[0] if len(rows) > 0 else {}
                keys.extend(firstdict.keys())
                uniq_keys.update(keys)
                rows = rows[1:]
            for row in rows:
                for k in row.keys():
                    #Save unique items in input order
                    if k not in uniq_keys:
                        keys.append(k)
                        uniq_keys.add(k)
            if headers == 'keys':
                headers = keys
            elif headers == "firstrow" and len(rows) > 0:
                headers = [firstdict.get(k, k) for k in keys]
                headers = list(map(_text_type, headers))
            rows = [[row.get(k) for k in keys] for row in rows]
        elif headers == "keys" and len(rows) > 0:
            # keys are column indices
            headers = list(map(_text_type, range(len(rows[0]))))

    # take headers from the first row if necessary
    if headers == "firstrow" and len(rows) > 0:
        headers = list(map(_text_type, rows[0])) # headers should be strings
        rows = rows[1:]

    headers = list(map(_text_type,headers))
    rows = list(map(list,rows))

    # pad with empty headers for initial columns if necessary
    if headers and len(rows) > 0:
       nhs = len(headers)
       ncols = len(rows[0])
       if nhs < ncols:
           headers = [""]*(ncols - nhs) + headers

    return rows, headers


def tabulate(tabular_data, headers=[], tablefmt="simple",
             floatfmt="g", numalign="decimal", stralign="left",
             missingval=""):
    """Format a fixed width table for pretty printing.

    >>> print(tabulate([[1, 2.34], [-56, "8.999"], ["2", "10001"]]))
    ---  ---------
      1      2.34
    -56      8.999
      2  10001
    ---  ---------

    The first required argument (`tabular_data`) can be a
    list-of-lists (or another iterable of iterables), a list of named
    tuples, a dictionary of iterables, an iterable of dictionaries,
    a two-dimensional NumPy array, NumPy record array, or a Pandas'
    dataframe.


    Table headers
    -------------

    To print nice column headers, supply the second argument (`headers`):

      - `headers` can be an explicit list of column headers
      - if `headers="firstrow"`, then the first row of data is used
      - if `headers="keys"`, then dictionary keys or column indices are used

    Otherwise a headerless table is produced.

    If the number of headers is less than the number of columns, they
    are supposed to be names of the last columns. This is consistent
    with the plain-text format of R and Pandas' dataframes.

    >>> print(tabulate([["sex","age"],["Alice","F",24],["Bob","M",19]],
    ...       headers="firstrow"))
           sex      age
    -----  -----  -----
    Alice  F         24
    Bob    M         19


    Column alignment
    ----------------

    `tabulate` tries to detect column types automatically, and aligns
    the values properly. By default it aligns decimal points of the
    numbers (or flushes integer numbers to the right), and flushes
    everything else to the left. Possible column alignments
    (`numalign`, `stralign`) are: "right", "center", "left", "decimal"
    (only for `numalign`), and None (to disable alignment).


    Table formats
    -------------

    `floatfmt` is a format specification used for columns which
    contain numeric data with a decimal point.

    `None` values are replaced with a `missingval` string:

    >>> print(tabulate([["spam", 1, None],
    ...                 ["eggs", 42, 3.14],
    ...                 ["other", None, 2.7]], missingval="?"))
    -----  --  ----
    spam    1  ?
    eggs   42  3.14
    other   ?  2.7
    -----  --  ----

    Various plain-text table formats (`tablefmt`) are supported:
    'plain', 'simple', 'grid', 'pipe', 'orgtbl', 'rst', 'mediawiki',
    and 'latex'. Variable `tabulate_formats` contains the list of
    currently supported formats.

    "plain" format doesn't use any pseudographics to draw tables,
    it separates columns with a double space:

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]],
    ...                 ["strings", "numbers"], "plain"))
    strings      numbers
    spam         41.9999
    eggs        451

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="plain"))
    spam   41.9999
    eggs  451

    "simple" format is like Pandoc simple_tables:

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]],
    ...                 ["strings", "numbers"], "simple"))
    strings      numbers
    ---------  ---------
    spam         41.9999
    eggs        451

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="simple"))
    ----  --------
    spam   41.9999
    eggs  451
    ----  --------

    "grid" is similar to tables produced by Emacs table.el package or
    Pandoc grid_tables:

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]],
    ...                ["strings", "numbers"], "grid"))
    +-----------+-----------+
    | strings   |   numbers |
    +===========+===========+
    | spam      |   41.9999 |
    +-----------+-----------+
    | eggs      |  451      |
    +-----------+-----------+

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="grid"))
    +------+----------+
    | spam |  41.9999 |
    +------+----------+
    | eggs | 451      |
    +------+----------+

    "pipe" is like tables in PHP Markdown Extra extension or Pandoc
    pipe_tables:

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]],
    ...                ["strings", "numbers"], "pipe"))
    | strings   |   numbers |
    |:----------|----------:|
    | spam      |   41.9999 |
    | eggs      |  451      |

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="pipe"))
    |:-----|---------:|
    | spam |  41.9999 |
    | eggs | 451      |

    "orgtbl" is like tables in Emacs org-mode and orgtbl-mode. They
    are slightly different from "pipe" format by not using colons to
    define column alignment, and using a "+" sign to indicate line
    intersections:

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]],
    ...                ["strings", "numbers"], "orgtbl"))
    | strings   |   numbers |
    |-----------+-----------|
    | spam      |   41.9999 |
    | eggs      |  451      |


    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="orgtbl"))
    | spam |  41.9999 |
    | eggs | 451      |

    "rst" is like a simple table format from reStructuredText; please
    note that reStructuredText accepts also "grid" tables:

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]],
    ...                ["strings", "numbers"], "rst"))
    =========  =========
    strings      numbers
    =========  =========
    spam         41.9999
    eggs        451
    =========  =========

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="rst"))
    ====  ========
    spam   41.9999
    eggs  451
    ====  ========

    "mediawiki" produces a table markup used in Wikipedia and on other
    MediaWiki-based sites:

    >>> print(tabulate([["strings", "numbers"], ["spam", 41.9999], ["eggs", "451.0"]],
    ...                headers="firstrow", tablefmt="mediawiki"))
    {| class="wikitable" style="text-align: left;"
    |+ <!-- caption -->
    |-
    ! strings   !! align="right"|   numbers
    |-
    | spam      || align="right"|   41.9999
    |-
    | eggs      || align="right"|  451
    |}

    "latex" produces a tabular environment of LaTeX document markup:

    >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="latex"))
    \\begin{tabular}{lr}
    \\hline
     spam &  41.9999 \\\\
     eggs & 451      \\\\
    \\hline
    \\end{tabular}

    """

    list_of_lists, headers = _normalize_tabular_data(tabular_data, headers)

    # optimization: look for ANSI control codes once,
    # enable smart width functions only if a control code is found
    plain_text = '\n'.join(['\t'.join(map(_text_type, headers))] + \
                            ['\t'.join(map(_text_type, row)) for row in list_of_lists])
    has_invisible = re.search(_invisible_codes, plain_text)
    if has_invisible:
        width_fn = _visible_width
    else:
        width_fn = len

    # format rows and columns, convert numeric values to strings
    cols = list(zip(*list_of_lists))
    coltypes = list(map(_column_type, cols))
    cols = [[_format(v, ct, floatfmt, missingval) for v in c]
             for c,ct in zip(cols, coltypes)]

    # align columns
    aligns = [numalign if ct in [int,float] else stralign for ct in coltypes]
    minwidths = [width_fn(h)+2 for h in headers] if headers else [0]*len(cols)
    cols = [_align_column(c, a, minw, has_invisible)
            for c, a, minw in zip(cols, aligns, minwidths)]

    if headers:
        # align headers and add headers
        minwidths = [max(minw, width_fn(c[0])) for minw, c in zip(minwidths, cols)]
        headers = [_align_header(h, a, minw)
                   for h, a, minw in zip(headers, aligns, minwidths)]
        rows = list(zip(*cols))
    else:
        minwidths = [width_fn(c[0]) for c in cols]
        rows = list(zip(*cols))

    if not isinstance(tablefmt, TableFormat):
        tablefmt = _table_formats.get(tablefmt, _table_formats["simple"])

    return _format_table(tablefmt, headers, rows, minwidths, aligns)


def _build_simple_row(padded_cells, rowfmt):
    "Format row according to DataRow format without padding."
    begin, sep, end = rowfmt
    return (begin + sep.join(padded_cells) + end).rstrip()


def _build_row(padded_cells, colwidths, colaligns, rowfmt):
    "Return a string which represents a row of data cells."
    if not rowfmt:
        return None
    if hasattr(rowfmt, "__call__"):
        return rowfmt(padded_cells, colwidths, colaligns)
    else:
        return _build_simple_row(padded_cells, rowfmt)


def _build_line(colwidths, colaligns, linefmt):
    "Return a string which represents a horizontal line."
    if not linefmt:
        return None
    if hasattr(linefmt, "__call__"):
        return linefmt(colwidths, colaligns)
    else:
        begin, fill, sep,  end = linefmt
        cells = [fill*w for w in colwidths]
        return _build_simple_row(cells, (begin, sep, end))


def _pad_row(cells, padding):
    if cells:
        pad = " "*padding
        padded_cells = [pad + cell + pad for cell in cells]
        return padded_cells
    else:
        return cells


def _format_table(fmt, headers, rows, colwidths, colaligns):
    """Produce a plain-text representation of the table."""
    lines = []
    hidden = fmt.with_header_hide if (headers and fmt.with_header_hide) else []
    pad = fmt.padding
    headerrow = fmt.headerrow

    padded_widths = [(w + 2*pad) for w in colwidths]
    padded_headers = _pad_row(headers, pad)
    padded_rows = [_pad_row(row, pad) for row in rows]

    if fmt.lineabove and "lineabove" not in hidden:
        lines.append(_build_line(padded_widths, colaligns, fmt.lineabove))

    if padded_headers:
        lines.append(_build_row(padded_headers, padded_widths, colaligns, headerrow))
        if fmt.linebelowheader and "linebelowheader" not in hidden:
            lines.append(_build_line(padded_widths, colaligns, fmt.linebelowheader))

    if padded_rows and fmt.linebetweenrows and "linebetweenrows" not in hidden:
        # initial rows with a line below
        for row in padded_rows[:-1]:
            lines.append(_build_row(row, padded_widths, colaligns, fmt.datarow))
            lines.append(_build_line(padded_widths, colaligns, fmt.linebetweenrows))
        # the last row without a line below
        lines.append(_build_row(padded_rows[-1], padded_widths, colaligns, fmt.datarow))
    else:
        for row in padded_rows:
            lines.append(_build_row(row, padded_widths, colaligns, fmt.datarow))

    if fmt.linebelow and "linebelow" not in hidden:
        lines.append(_build_line(padded_widths, colaligns, fmt.linebelow))

    return "\n".join(lines)