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True >>> _isnumber_with_thousands_separator(".1") True >>> _isnumber_with_thousands_separator("1000") False >>> _isnumber_with_thousands_separator("1,000") True >>> _isnumber_with_thousands_separator("1,0000") False >>> _isnumber_with_thousands_separator("1,000.1234") True >>> _isnumber_with_thousands_separator(b"1,000.1234") True >>> _isnumber_with_thousands_separator("+1,000.1234") True >>> _isnumber_with_thousands_separator("-1,000.1234") True )decodeUnicodeDecodeErrorAttributeErrorboolrematch _float_with_thousands_separatorsstrings&r"_isnumber_with_thousands_separatorr^sD2  96B CC  /   s6A  A cHV!V4R# \\3dR#i;i)TF)r TypeError)convrs&&r_isconvertiblers( V   "s  !!c\V4\\39;'g\\V4;'d\ V\ \ 34'*;'gg\P!\V44;'g \P!\V44'*;'gVP4R9#)aDetects if something *could* be considered a numeric value, vs. just a string. This promotes types convertible to both int and float to be considered a float. Note that, iff *all* values appear to be some form of numeric value such as eg. "1e2", they would be considered numbers! The exception is things that appear to be numbers but overflow to +/-inf, eg. "1e23456"; we'll have to exclude them explicitly. >>> _isnumber(123) True >>> _isnumber(123.45) True >>> _isnumber("123.45") True >>> _isnumber("123") True >>> _isnumber("spam") False >>> _isnumber("123e45") True >>> _isnumber("123e45678") # evaluates equal to 'inf', but ... isn't False >>> _isnumber("inf") True >>> from fractions import Fraction >>> _isnumber(Fraction(1,3)) True )infz-infnan) r!floatintrrr"rmathisinfisnanlowerrs&r _isnumberrsB V  $   5& )  vU|44E&M2OOdjjv6OP@@||~)??rc$\V4VJ;'g}\VR4;'g\VR4;'d,\\V44PR4;'g*\ V\ \34;'d \ W4#)z3 >>> _isint("123") True >>> _isint("123.45") False is_integer __array__z>> _isbool(True) True >>> _isbool("False") True >>> _isbool(1) False )TrueFalse)r!rrrr"rs&r_isboolrs; <4   6E3<(HHV7H-HrcV'd(\V\\34'd \V4pVe%\V\\34'dV'g \ R4#\ VR4'd\#\ V4'd\#V'dF\V4'g/\V\4'd\V4'dRV9d\#V'd?\V4'g(\V\4'd\V4'd\#\V\4'd\#\#)aThe least generic type (type(None), int, float, str, unicode). 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ANSI color codes are removed. >>> _visible_width('hello'), _visible_width("world") (5, 5) )wcwidthWIDE_CHARS_MODEwcswidthr*rr"rr)rlen_fns& r_visible_widthr4dsL!!!c5\""k!n%%c!f~rc\V\4'd%\\P!\ V44#\\P!\ V44#r)rr"rrsearch_multiline_codes_multiline_codes_bytesr.s&r _is_multiliner9vs=!SBII.233BII4a899rc V\\V\P!RV444#z1Visible width of a potentially multiline content.[ ])maxrrsplit multiline_s line_width_fns&&r_multiline_widthrB}s s="((8["AB CCrcaV'd\oMV'd\PoM\oV'd V3RlpV#SpV#)z2Return a function to calculate visible cell width.c<\VS4#r)rBrrAs&r"_choose_width_fn..s -a?rr4r0r2r*renable_widechars is_multilinewidth_fnrAs&&& @r_choose_width_fnrMs9& ((  ? O! OrcVR8Xd0V'gVUu.uFqDP4NK pp\pW3#VR8Xd0V'gVUu.uFqDP4NK pp\pW3#VR8XdV'd%VUu.uFp\\ V44NK ppMVUu.uFp\V4NK pp\ V4p\ W4UUu.uFwrHWGV, R,,NK ppp\pW3#V'g \pW3#V'gVUu.uFqDP4NK pp\pW3#uupiuupiuupiuupiuuppiuupi)r4r2r5rX) r#r!r&rrr=rGr)r$) stringsr]rpreserve_whitespacerpadfndecimals maxdecimalsdecss &&&& r_align_column_choose_padfnrUsJG"*12'Qwwy'G2& >% h "*12'Qwwy'G2 > i  =DEW KN3WHEH0781 AH8(m ADWAWXAWga1d*c111AWX >  >#*12'Qwwy'G2 >)33F8X 3s#E EE $E!EEcaV'd\oMV'd\PoM\oV'd V3RlpV#SpV#)c<\VS4#r)_align_column_multiline_widthrEs&rrF/_align_column_choose_width_fn..s: } rrHrIs&&& @r_align_column_choose_width_fnrZs;& ((    O! Orc V\\V\P!RV444#r;)r)rrr>r?s&&rrXrXs M288Hk#BC DDrc.pVF=p\V\4'dVPV4K,VPV4K? V#r)rr)extendr) nested_listretitems& r _flat_listras= C dD ! ! JJt  JJt   Jrc\WW64wr\W4V4p\\W44p \ \ \ V 44V4p V'Ed\V'gPV'gHVU U u.uF7p RP V P4U u.uF q!W4NK up 4NK9 p p p V #VU U u.uF3p \P!RV 4U u.uFp \V 4NK up NK5 pp p \W4UUUUu.uF5wpp\VV4UUu.uFwppV VV, , NK uppNK7 ppppp\VV4U UU Uu.uFTwrRP \V P4;'gT V4U Uu.uFwp pV!VV 4NK upp 4NKV p p pp pV #V'g!V'gVU u.uF q!W4NK p p V #\\\V44p\W4UUu.uFwppV VV, , NK ppp\VV4U Uu.uFwp pV!VV 4NK p p pV #uup iuup p iuup iuup p iuuppiuuppppiuupp iuupp pp iuup iuuppiuupp i)ztring] -> [padded_string]r}r<) rUrZr)rr=rarF splitlinesrr>r*rG)rOr]minwidthrrJrKrPrQrLs_widthsmaxwidthmsrpadded_stringss_lensmwmlr9r{visible_widthss&&&&&&& r _align_columnrmsh0MNG-HC*+H3z(+,h7H| "!B r}}G!5-GH! 6 +JQQ2rxx"'=>'=!s1v'=>FQ"(33FB14B < 1QU## <3 "'>::FB 3 8M8M2PR3ST3S41a5A;3STU: N  :AB'QeH0'NB  #c7+,F=@=RS=RTQh!a%00=RNS7:'>6RS6RdaeAqk6RNS 5H ?Q<U CTTsx+"I  I I 2II $I>I$ I1I$ -I2 = I2 I, I2 I:I?.JI II$ ,I2 c\R4^\^\^\^\^\ ^/p^\ ^\^\^\^\^\R4/p\ VPV^4VPV^44pW4,#r)r!rrrrr"r=rY)type1type2typesinvtypes moregenerics&& r _more_genericrts T A a Q q q Q  E 3 5 5 3 4 4: Heiiq)599UA+>?K   rclVUu.uFp\W1V4NK pp\\V\4#uupi)uThe least generic type all column values are convertible to. >>> _column_type([True, False]) is bool True >>> _column_type(["1", "2"]) is int True >>> _column_type(["1", "2.3"]) is float True >>> _column_type(["1", "2.3", "four"]) is str True >>> _column_type(["four", 'пять']) is str True >>> _column_type([None, "brux"]) is str True >>> _column_type([1, 2, None]) is int True >>> import datetime as dt >>> _column_type([dt.datetime(1991,2,19), dt.time(17,35)]) is str True )rr rtr)rOrrrrqs&&& r _column_typervs5,9@ @1U1X .E @ - -- As1cVfV#\V\\34'd V'gR#V\JdV #V\JEd \V\4'dVP R4P R4p\ P!RV4pV'd\VP44pV^8XdVP^4p V P4'dbVP^4\\V 4V4,VP^4,p V P R4P R4pRp\W4#V\Jd\VR4#V\JdT;'d\V\\34p V 'd3\!V4p \\V 4V4p TP%Y4#\V\4'dRV9dVP%RR4p\\V4V4#V # \\3d\T4u#i;i \"\3dT u#i;i \"\3dT u#i;i)uFormat a value according to its deduced type. Empty values are deemed valid for any type. Unicode is supported: >>> hrow = ['буква', 'цифра'] ; tbl = [['аз', 2], ['буки', 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 runicode_escapezutf-8z/(\\[xX]+[0-9a-fA-F]+\[\d+[mM]+)([0-9.]+)(\\.*)$asciir)rrr"rencoderrr6r*groupsgroupisdigitrprrrrrreplace)rvaltypefloatfmtintfmt missingvalr val_stripedcolored total_groupsdigitsval_newis_a_colored_numberraw_val formatted_vals&&&&&& r_formatr-s {#s|$$S#~ C c3  **%56==gFKiiBKG"7>>#34 1$$]]1-F~~''#MM!,$S[&9:%mmA./ &nnW5<<=MNFc"" E  sG$ $ E +MM 3e 0M !#&G &uW~x @ ;;w6 6#s##s kk#r* eCj(33'-. s8O  *   *  s6 H$H4:IH10H14I  I I%$I%c ^V'dN\P!\V4pVUu.uFp\WqW%!V44NK ppRP V4#\ V4V, p W), pVR8Xd \ W 4#VR8Xd \W 4#V'gV #\W 4#uupi)zIPad string header to width chars given known visible_width of the header.r}r3r2) rr>r7 _align_headerrFr*r$r&r!) rxr]rP visible_widthrKrL header_linesh padded_lines ninvisibles &&&&&& rrrlsxx 0&9 EQ EQM!x{ ;\  yy&&V},J EF'' h && && sB*c\V\4'dQ.p.p\V4F:wr4\V4'dVP V4K)VP V4K< W!3#VR3#r)rr) enumerater.r)rseparating_lines sans_rowsindexr+s& r_remove_separating_linesrsf$ #D/JE"3'' ''.  % * **TzrcXV'd"VFpVPV\4K R#R#r)insertr$)rrrs&& r_reinsert_separating_linesrs"%E KK /&rc VeVRJdV#\V\4'dD\V4\V48wd+\RR\V4 R\V4 2,4h\ V4wr#.p\ V4pVF0p\ V4pVPV.\V4,4K2 Tp\W4V#)zAdd a left-most index column.Fz2index must be as long as the number of data rows: z len(index)=z len(rows)=) rrr*rriternextrr)r)rrrrr index_iterr+index_vs&& r_prepend_row_indexrs } %CJ#d)$; @CJ<{3t9+> ?  #;4"@IHeJz" DI-. Dt6 Krc>\V4# \dR#i;i)zDA wrapper around standard bool() which doesn't throw on NumPy arraysF)rrrs&r_boolrs#Cy s c l\V4RpRp\VR4'Edd\VR4'EdQ\ VP 4'd3VP 4p\\VP 4!4pM\VR4'd\V4pVR9dwVPPe_\VPP\4'dVPPVR&MVPP.VR&VP p\VP4pVUu.uFp\V4NK ppM \R 4hVR8Xd\\\V44pEM/\V4pTR8Xd T'g.pEMTR8XdG\TR 4'd5TPP'dTPPpEMTR8Xds\!T4^8dc\T^,\"4'dF\T^,R 4'd-\\\T^,P$44pEMJ\!T4^8Ed\T^,R4'Ed\T^,R4'Ed\'4p .pTR 8XdU\!T4^8d T^,M/p TP)T P 44T P+T4TR ,pTFDpTP 4F-p Y9gK TP-T 4T P/T 4K/ KF TR8XdTpM\T\04'd:TU u.uFqP3Y4NK pp \\\T44pMgTR 8XdN\!T4^8d;TU u.uFp X P3Y4NK pp \\\T44pM.pMT'd \R4hTUU u.uF"qU u.uFqP3T 4NK up NK$ ppp EMbTR8XdZ\TR4'dH\TR4'd6\TR4'd$TP4U u.uF q^,NK pp EM\6e\!T4^8d\6P8!T^,4'd\6P:!T^,4U u.uFqPNK pp TR8XdTpTUUu.uF1p\=T4'gTUu.uFp\?Y4NK upMTNK3 pppMITR8XdC\!T4^8d3\\\\A\!T^,4444pVR 8Xdl\!V4^8d\Ve,V^,.\V^,4,pVR ,pM V^,p\\\V44pVR ,pM VR 8Xd.p\\\V44p\\RV44p\CV4\\D39pVR8XdVe \GWd4pM\V\H4'dV'g\GV\V44pM\V\J4'dV'g \GWb4pMnVR8Xg\MV4'd6V'g.Vf\\A\!V444p\GWd4pM!VR8Xg\MV4'g V'g^pV'dN\!V4^8d>\O^\!V^,4\!V4, 4pR.V,V,pWaV3# \d\T4pELi;i \d \T4hi;iuupi \d \T4hi;iuup iuup iuup iuup piuup iuup iuupiuuppi)aTransform a supported data type to a list of lists, and a list of headers, with headers padding. 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") * list of dataclasses (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 showindex="default", show row indices of the pandas.DataFrame. If showindex="always", show row indices for all types of data. If showindex="never", don't show row indices for all types of data. If showindex is an iterable, show its values as row indices. z To build a table python-tabulate requires two-dimensional data like a list of lists or similar. Did you forget a pair of extra [] or ',' in ()?Nkeysrhrralways:NrcNz7tabular data doesn't appear to be a dict or a DataFramedtype_fieldsfirstrowr(NNz6headers for a list of dicts is not a dict or a keyword descriptionfetchonerowcountc>\V4'dV#\V4#r)r.r))r|s&rrF)_normalize_tabular_data..hs#6q#9#9aFtAwFrneverr)rrT)(rrr)rcallablerhr izip_longestrrnamerrr"rnamesr*tuplersetr]updateradddictrYr dataclasses is_dataclassfieldsr.getattrranger!rrrrrr=) tabular_datar showindexerr_msgrrrvalsr+ uniq_keys firstdictkcolumnfield field_namesrshowindex_is_a_str headers_pads&&& r_normalize_tabular_datarsD  W  < E|V$$x)H)H L'' ( ($$&D ) ,"5"5"78 \7 + + %D88 &&++7l0055t<<+1166DH , 2 2 7 78DH&&D++,E)-.#DID.DVW W f 3sD>*G % %D f TG v  g..""(((#((..G v D A 47E**Q++3sDGOO45G Y]wtAw77GDGX>> repr(_to_str(b'foo')) "'foo'" >>> repr(_to_str('foo')) "'foo'" >>> repr(_to_str(42)) "'42'" )encodingerrors)rrrr")rrrs&&&r_to_strrs+(!Uxxx99 q6Mrcla9Vf.p\WVR7wppp\V4wppV e\V 4\Jd \ V 4p \ V4'd\ V^,4pM^p\ V \4'd\V VV 4p M \V VR4p \V V4p\VV VVVVR7pVe|V'd\ V^,4M \ V4p\ V\4'd\VVV4pM \VVR4p\V V4p\V.VVVVVR7^,pVR8Xd\VV4wpp\pVR8Xd$^pRp V\8XdRMTpV\8XdRMTpMV\8XdRMTpV\8XdR MTpVR 8XdR p R pR P\\!\"V4\P$!R V4444p\&P)V4RJp\*RJ;'d\,p\ V\.4'g5V\09d*\3V4'd\0P5W"4pRpMR p\7VVV4o9\ \9V!4p\V \ V44p\;VV4UU u.uFwpp \=VV R7NK p!pp \ V\>4'd\ V4V.,p"MZ\ V4p"\ V"4\ V48d7V"PA\ V4\ V"4, \B.,4\ V\>4'd\ V4V.,p#MZ\ V4p#\ V#4\ V48d7V#PA\ V4\ V#4, \D.,4\ V\>4'd\ V4V.,p$MZ\ V4p$\ V$4\ V48d7V$PA\ V4\ V$4, \F.,4\;VV!V"V#V$4U%U&U'U(U)U*u.uF)wp%p&p'p(p)V%U*u.uFp*\IV*V&V'V(V)V4NK up*NK+ pp)p(p'p&p%p*V eV .\ V4,p+M$V!U&u.uFp&V&\\J39dTMTNK p+p&V e\ V \L4'gQh\ V \>4'd3\NPP!RV U%u.uFp%V%NK up% RV RV R2^R7\SV 4F&wp,p-V,\ V+48gMV-R8wgK!V-V+V,&K( V'd VU.u.uFp.S9!V.4V,NK up.M^.\ V4,p/V+PU4p0VR 8XdR .\ V4,p0\;VV0V/4U%U1U2u.uFwp%p1p2\WV%V1V2VVVV 4NK pp1p%p2Rp3V'EdT;'gR..\ V4,p4VeV.\ V44,p3MT+;'gV.\ V4,p3Ve\ V\L4'gQh\ V\>4'd3\NPP!RVU%u.uFp%V%NK up% RV RV R2^R7\SV4FTwp,p-VV,,p5V5\ V348gM7V-R8XdV5\ V+48dV+V5,V3V5&KFV-R8wgKOV-V3V5&KV \;V/V44U2U%u.uF%wp2p%\YV2\YV93RlV%444NK' p/p2p%\;VV3V/4U.U1U2u.uFwp.p1p2\[V.V1V2S9!V.4VS94NK pp1p.p2\ \;V!4p6M6VU%u.uFp%\YV93RlV%44NK p/p%\ \;V!4p6\ V\.4'g"\\P5V\\R,4p\ V\>4'dTMRp7\V\ V64V74p8\_V6V4\aVVV3V6V/V+VV8R7#uup piuup*iuup*p)p(p'p&p%iuup&iuup%iuup.iuup2p1p%iuup%iuup%p2iuup2p1p.iuup%i)uKFormat 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, an iterable of dataclasses, 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 By default, pandas.DataFrame data have an additional column called row index. To add a similar column to all other types of data, use `showindex="always"` or `showindex=True`. To suppress row indices for all types of data, pass `showindex="never" or `showindex=False`. To add a custom row index column, pass `showindex=some_iterable`. >>> print(tabulate([["F",24],["M",19]], showindex="always")) - - -- 0 F 24 1 M 19 - - -- Column and Headers 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). `colglobalalign` allows for global alignment of columns, before any specific override from `colalign`. Possible values are: None (defaults according to coltype), "right", "center", "decimal", "left". `colalign` allows for column-wise override starting from left-most column. Possible values are: "global" (no override), "right", "center", "decimal", "left". `headersglobalalign` allows for global headers alignment, before any specific override from `headersalign`. Possible values are: None (follow columns alignment), "right", "center", "left". `headersalign` allows for header-wise override starting from left-most given header. Possible values are: "global" (no override), "same" (follow column alignment), "right", "center", "left". Note on intended behaviour: If there is no `tabular_data`, any column alignment argument is ignored. Hence, in this case, header alignment cannot be inferred from column alignment. Table formats ------------- `intfmt` is a format specification used for columns which contain numeric data without a decimal point. This can also be a list or tuple of format strings, one per column. `floatfmt` is a format specification used for columns which contain numeric data with a decimal point. This can also be a list or tuple of format strings, one per column. `None` values are replaced with a `missingval` string (like `floatfmt`, this can also be a list of values for different columns): >>> 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', 'latex', 'latex_raw', 'latex_booktabs', 'latex_longtable' and tsv. 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 | +------+----------+ "simple_grid" draws a grid using single-line box-drawing characters: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "simple_grid")) ┌───────────┬───────────┐ │ strings │ numbers │ ├───────────┼───────────┤ │ spam │ 41.9999 │ ├───────────┼───────────┤ │ eggs │ 451 │ └───────────┴───────────┘ "rounded_grid" draws a grid using single-line box-drawing characters with rounded corners: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "rounded_grid")) ╭───────────┬───────────╮ │ strings │ numbers │ ├───────────┼───────────┤ │ spam │ 41.9999 │ ├───────────┼───────────┤ │ eggs │ 451 │ ╰───────────┴───────────╯ "heavy_grid" draws a grid using bold (thick) single-line box-drawing characters: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "heavy_grid")) ┏━━━━━━━━━━━┳━━━━━━━━━━━┓ ┃ strings ┃ numbers ┃ ┣━━━━━━━━━━━╋━━━━━━━━━━━┫ ┃ spam ┃ 41.9999 ┃ ┣━━━━━━━━━━━╋━━━━━━━━━━━┫ ┃ eggs ┃ 451 ┃ ┗━━━━━━━━━━━┻━━━━━━━━━━━┛ "mixed_grid" draws a grid using a mix of light (thin) and heavy (thick) lines box-drawing characters: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "mixed_grid")) ┍━━━━━━━━━━━┯━━━━━━━━━━━┑ │ strings │ numbers │ ┝━━━━━━━━━━━┿━━━━━━━━━━━┥ │ spam │ 41.9999 │ ├───────────┼───────────┤ │ eggs │ 451 │ ┕━━━━━━━━━━━┷━━━━━━━━━━━┙ "double_grid" draws a grid using double-line box-drawing characters: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "double_grid")) ╔═══════════╦═══════════╗ ║ strings ║ numbers ║ ╠═══════════╬═══════════╣ ║ spam ║ 41.9999 ║ ╠═══════════╬═══════════╣ ║ eggs ║ 451 ║ ╚═══════════╩═══════════╝ "fancy_grid" draws a grid using a mix of single and double-line box-drawing characters: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "fancy_grid")) ╒═══════════╤═══════════╕ │ strings │ numbers │ ╞═══════════╪═══════════╡ │ spam │ 41.9999 │ ├───────────┼───────────┤ │ eggs │ 451 │ ╘═══════════╧═══════════╛ "colon_grid" is similar to "grid" but uses colons only to define columnwise content alignment, without whitespace padding, similar to the alignment specification of Pandoc `grid_tables`: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "colon_grid")) +-----------+-----------+ | strings | numbers | +:==========+:==========+ | spam | 41.9999 | +-----------+-----------+ | eggs | 451 | +-----------+-----------+ >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "colon_grid", ... colalign=["right", "left"])) +-----------+-----------+ | strings | numbers | +==========:+:==========+ | spam | 41.9999 | +-----------+-----------+ | eggs | 451 | +-----------+-----------+ "outline" is the same as the "grid" format but doesn't draw lines between rows: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "outline")) +-----------+-----------+ | strings | numbers | +===========+===========+ | spam | 41.9999 | | eggs | 451 | +-----------+-----------+ >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="outline")) +------+----------+ | spam | 41.9999 | | eggs | 451 | +------+----------+ "simple_outline" is the same as the "simple_grid" format but doesn't draw lines between rows: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "simple_outline")) ┌───────────┬───────────┐ │ strings │ numbers │ ├───────────┼───────────┤ │ spam │ 41.9999 │ │ eggs │ 451 │ └───────────┴───────────┘ "rounded_outline" is the same as the "rounded_grid" format but doesn't draw lines between rows: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "rounded_outline")) ╭───────────┬───────────╮ │ strings │ numbers │ ├───────────┼───────────┤ │ spam │ 41.9999 │ │ eggs │ 451 │ ╰───────────┴───────────╯ "heavy_outline" is the same as the "heavy_grid" format but doesn't draw lines between rows: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "heavy_outline")) ┏━━━━━━━━━━━┳━━━━━━━━━━━┓ ┃ strings ┃ numbers ┃ ┣━━━━━━━━━━━╋━━━━━━━━━━━┫ ┃ spam ┃ 41.9999 ┃ ┃ eggs ┃ 451 ┃ ┗━━━━━━━━━━━┻━━━━━━━━━━━┛ "mixed_outline" is the same as the "mixed_grid" format but doesn't draw lines between rows: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "mixed_outline")) ┍━━━━━━━━━━━┯━━━━━━━━━━━┑ │ strings │ numbers │ ┝━━━━━━━━━━━┿━━━━━━━━━━━┥ │ spam │ 41.9999 │ │ eggs │ 451 │ ┕━━━━━━━━━━━┷━━━━━━━━━━━┙ "double_outline" is the same as the "double_grid" format but doesn't draw lines between rows: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "double_outline")) ╔═══════════╦═══════════╗ ║ strings ║ numbers ║ ╠═══════════╬═══════════╣ ║ spam ║ 41.9999 ║ ║ eggs ║ 451 ║ ╚═══════════╩═══════════╝ "fancy_outline" is the same as the "fancy_grid" format but doesn't draw lines between rows: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "fancy_outline")) ╒═══════════╤═══════════╕ │ strings │ numbers │ ╞═══════════╪═══════════╡ │ 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 | "presto" is like tables produce by the Presto CLI: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], ... ["strings", "numbers"], "presto")) 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;" |+ |- ! strings !! style="text-align: right;"| numbers |- | spam || style="text-align: right;"| 41.9999 |- | eggs || style="text-align: right;"| 451 |} "html" produces HTML markup as an html.escape'd str with a ._repr_html_ method so that Jupyter Lab and Notebook display the HTML and a .str property so that the raw HTML remains accessible the unsafehtml table format can be used if an unescaped HTML format is required: >>> print(tabulate([["strings", "numbers"], ["spam", 41.9999], ["eggs", "451.0"]], ... headers="firstrow", tablefmt="html"))
strings numbers
spam 41.9999
eggs 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} "latex_raw" is similar to "latex", but doesn't escape special characters, such as backslash and underscore, so LaTeX commands may embedded into cells' values: >>> print(tabulate([["spam$_9$", 41.9999], ["\\emph{eggs}", "451.0"]], tablefmt="latex_raw")) \begin{tabular}{lr} \hline spam$_9$ & 41.9999 \\ \emph{eggs} & 451 \\ \hline \end{tabular} "latex_booktabs" produces a tabular environment of LaTeX document markup using the booktabs.sty package: >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="latex_booktabs")) \begin{tabular}{lr} \toprule spam & 41.9999 \\ eggs & 451 \\ \bottomrule \end{tabular} "latex_longtable" produces a tabular environment that can stretch along multiple pages, using the longtable package for LaTeX. >>> print(tabulate([["spam", 41.9999], ["eggs", "451.0"]], tablefmt="latex_longtable")) \begin{longtable}{lr} \hline spam & 41.9999 \\ eggs & 451 \\ \hline \end{longtable} Number parsing -------------- By default, anything which can be parsed as a number is a number. This ensures numbers represented as strings are aligned properly. This can lead to weird results for particular strings such as specific git SHAs e.g. "42992e1" will be parsed into the number 429920 and aligned as such. To completely disable number parsing (and alignment), use `disable_numparse=True`. For more fine grained control, a list column indices is used to disable number parsing only on those columns e.g. `disable_numparse=[0, 2]` would disable number parsing only on the first and third columns. Column Widths and Auto Line Wrapping ------------------------------------ Tabulate will, by default, set the width of each column to the length of the longest element in that column. However, in situations where fields are expected to reasonably be too long to look good as a single line, tabulate can help automate word wrapping long fields for you. Use the parameter `maxcolwidths` to provide a list of maximal column widths: >>> print(tabulate( [('1', 'John Smith', 'This is a rather long description that might look better if it is wrapped a bit')], headers=("Issue Id", "Author", "Description"), maxcolwidths=[None, None, 30], tablefmt="grid" )) +------------+------------+-------------------------------+ | Issue Id | Author | Description | +============+============+===============================+ | 1 | John Smith | This is a rather long | | | | description that might look | | | | better if it is wrapped a bit | +------------+------------+-------------------------------+ Header column width can be specified in a similar way using `maxheadercolwidths`. N)r)rrrrrrTr2r5r3rrc3B"TFp\\V4xK R#5ir)rr)r?r+s& rrAtabulate..E sK]cGS 1 1]sF)rz*As a string, `colalign` is interpreted as z". Did you mean `colglobalalign = "z"` or `colalign = ("z",)`?) stacklevelglobalrz.As a string, `headersalign` is interpreted as z&. Did you mean `headersglobalalign = "z"` or `headersalign = ("samec34<"TF pS!V4xK R#5irr6r?clrLs& rrAr s32(2,,c34<"TF pS!V4xK R#5irr6rs& rrAr s2""rr) rowaligns)1rrr!rr)r*rrr_expand_numparserr MIN_PADDING_DEFAULT_ALIGNrFr rr from_iterabler+r6r0r1rmultiline_formatsr9rYrMrrGrvr"r]_DEFAULT_FLOATFMT_DEFAULT_INTFMT_DEFAULT_MISSINGVALrrrwarningswarnrcopyrmr=r_table_formatsr _format_table):rrtablefmtrrnumalignstralignrrdisable_numparsecolglobalalignrrP maxcolwidthsheadersglobalalign headersalignrowalignmaxheadercolwidthsrrrrrrr min_padding plain_textrrJrKcolscolnpcoltypes float_formats int_formats missing_valsr^ctfl_fmtint_fmtmiss_vrfalignsidxr7r minwidths aligns_copyr@minwaligns_headerst_colshidxr ra_defaultrrLs:&&&&&&&&&&&&&&&&&&&& @rrrssz *A+'M7K'?}&M#M#   & -L }  =+,HH lC ( (+L(LQL+L(DIL$%5x@ /  !--  %,93}Q'(s7| (# . .!1"H.@" "22DhPT!U $%5x@ ) I !--    5!9-!Q w K8 '>98x'>98x (N :9%76X <#  !   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If `original` is shorter than `num_desired`, it will be padded with the value in `default`. If `original` is not a list to begin with (i.e. scalar value) a list of length `num_desired` completely populated with `default will be returned )rrr"r*)original num_desiredrs&&&rrr sC(H%%j3.G.G){S]'BCCCy;&&rcV'd;V\8Xd\#RV,pVUu.uFq2V,V,NK ppV#V#uupi)rX)r$)cellsrpadr padded_cellss&& r_pad_rowr! sJ O #" "Gm5:;UTd S((U ; +AKB S+& 'F00EB A80;@-H-QK0KbqEEK0-KHR% %I&I L!5B1Hs(CC$C C(C# %C(#C(cV'gR#\V4'd V!W4#Vwr4rVVUu.uF qtV,NK pp\WWV34#uupi)z3Return a string which represents a horizontal line.Nr') rIrJlinefmtr#fillr$r%r9rs &&& r _build_linerE? sU y,, 'S#,-9a9- (9::.sA c<VP\WV44V#r)rrE)r*rIrJrCs&&&&r _append_linerGK s LLY7;< Lrc:a]tRtRtoRtRt]R4tRtVt R#)JupyterHTMLStriP zQWrap the string with a _repr_html_ method so that Jupyter displays the HTML tablecV#rr6selfs&r _repr_html_JupyterHTMLStr._repr_html_T s rc V#)z>add a .str property so that the raw string is still accessibler6rKs&rr"JupyterHTMLStr.strW s  rr6N) __name__ __module__ __qualname____firstlineno____doc__rMpropertyr"__static_attributes____classdictcell__ __classdict__s@rrIrIP s#rrIc .pV'dVP'd VPM.p VPp VPp VU u.uFq^V ,,NK p p V'dRp\\V R7pM \ p\ pV!W4pVP'dRV 9d\WWPP4V'd;V!VVWV 4VP'dRV 9d\WWPP4V'dVP'dRV 9d\VRR V4FFwppV\8wd V!VV!VV 4V VVPVR7\WWPP4KH V!VV!VR ,V 4V VVPVR ,R7MVP;'gKVP;'g7VP;'g#VP;'g\RRRR4pVF>p\!V4'd\WVV4K#V!W!VV 4WVP4K@ VP'dR V 9d\WWPP4V'g V'd4R P#V4pVP\$8Xd \'V4#V#R#uup i) z1Produce a plain-text representation of the table.cV#rr6)r+rs&&rrF_format_table..i s3r)rrrrN)rrrr}r)rrrr rAr!r+rrGrrrGr$rrrr.rFrmrI)r r headersalignsrrIrJrKrr*hiddenrrr9r:pad_row append_rowpadded_headersr+ralignseparating_lineoutputs&&&&&&&& rrr] sl E&-#2F2F2FS ! !RF ++C I,56Iq!c'kkIM6  2< & W*N }}}F2U9mmD5.- R    #4F#B y:M:M N ###(9(GtCRy)4KCo%C%!KK#  y:M:M N5   DHc "   KKr]      $ $"" $ $}} $ $}} $ $BB# C#3''U9oN73,m   }}}F2U9mmD$5! ==< <!&) )MM7sKcLa]tRtRtoRtRt]R4tRtRt Rt Rt Vt R #) ri aGA custom implementation of CPython's textwrap.TextWrapper. This supports both wide characters (Korea, Japanese, Chinese) - including mixed string. For the most part, the `_handle_long_word` and `_wrap_chunks` functions were copy pasted out of the CPython baseline, and updated with our custom length and line appending logic. cj.VnRVn\PP!V.VO5/VBR#r) _active_codes max_linestextwrap TextWrapper__init__)rLrkwargss&*,rrl_CustomTextWrap.__init__ s/%%d"',<(VR,P4R8Xd"WPP VR,4,pVRV'gEKVPeo\!V4^,VP8gNV'd@VP'd^\!V4^8XdNV^,P4'g1WV8:d+VP#W#RP%V4,4EK>V'dVR,P4'dmWPP VP 4,V8:dGVPVP 4VP#W#RP%V4,4V#WPP VR,4,pVRKV'dpVR,P'4pVP V4VP VP 4,VP8:dWP ,VR&V#VP#W#VP P4,4V#V#)aN_wrap_chunks(chunks : [string]) -> [string] Wrap a sequence of text chunks and return a list of lines of length 'self.width' or less. (If 'break_long_words' is false, some lines may be longer than this.) Chunks correspond roughly to words and the whitespace between them: each chunk is indivisible (modulo 'break_long_words'), but a line break can come between any two chunks. 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Whitespace chunks will be removed from the beginning and end of lines, but apart from that whitespace is preserved. zinvalid width %r (must be > 0)z#placeholder too large for max widthrr)rPrrisubsequent_indentinitial_indentrq placeholderlstripreversedrop_whitespacer#rrrsumrr*r|rFrZ) rLchunksr*indentrrrP chunk_len prev_lines && r _wrap_chunks_CustomTextWrap._wrap_chunks sY ::?= JK K >> %~~!//,,yy 499T-=-=-D-D-F#GG$**T !FGG fHG//,,JJ6!22E###r (8(8(:b(@U2J IIfRj1 &%/OOFJJL1(G$))F2J/%7&&vHc$))X67###Xb\5G5G5IR5O99Xb\22RLxNN*5zA~6"///K1, &q  1 1(&&urwwx7H.HI"$RL..00 '))D4D4D*E E N$OOD,<,<= ..urwwx?P6PQ!  99Xb\#::$RL (-b (8(8(:I $ ) 4tyyAQAQ7R R#'::!.-68H8H,Hb % **54;K;K;R;R;T2TU u r)rhriN) rQrRrSrTrUrl staticmethodrqr|rrrWrXrYs@rrr s:= !!6/3nmmrrc^RIp\P!\P4pVP\ P R,R.R O4wr#.p\p\pRpRp Rp Rp XFwrV R 9dRpKV R 9dT p KV R9dT pK&V R9dT pK1V R9dV P4pKJV R9dCV \9d4\RV ,4\V4\ P!^4T p KV R9dT p KV R9gK\V4\ P!^4K X'g\ P.MTpV R8Xd\ PM \!V R 4;_uu_4pVFopVR8Xd\ Pp\#V4'd\%VVV V VVVVR 7K@\!V4;_uu_4p\%VVV V VVVVR 7RRR4Kq RRR4R# TPd8p\T4\T4\ P!^4Rp?ELRp?ii;i +'giK;i +'giR#;i)aUsage: tabulate [options] [FILE ...] Pretty-print tabular data. 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