ni5j%SrSSKrSSKrSSKrSSKrSSKrSSKrSSKJr SSK J r SSK J r SSK Jr SSKJrJrJrJrJrJrJrJr SSKrSSKrSS KJrJr SS KJ r SS K!J"r" SS K#J$r$ SS K%J&r&J'r' \"\(5r)Sq*\\\\4\+S'S5S\'S\\\,S\\,S\,S\-S\,4 Sjjr.S6S\&S\\,S\,S\\\,S\\\,\4S\-S\\,S\-S\\\,\/\\/4S\-S\\\\0\\\04S\-S \-S!\-S"\\,S#\\-S$\\\0S%\-S&\\,S'\\,S(\\,S\-S\,S\\\Rb\\,\4440S)jjr2S7S\&S*\&S\\\,\4S\,S\\\,S\\,S\-S\\\,\/\\/4S\-S\\\\0\\\04S\-S \-S!\-S"\\,S#\\-S$\\\0S%\-S&\\,S'\\,S(\\,S\-S\\,S\\Rb4.S+jjr3S8S\&S(\,S\,S\\\,S\\\,\/\\/4S\-S\\\\0\\\04S\-S \-S!\-S"\\,S#\\-S$\\\0S%\-S&\\,S'\\,S\-S\,SS4&S,jjr4S8S-\,S\,S\\\,S\\\,\/\\/4S\-S\\\\0\\\04S\-S \-S!\-S"\\,S#\\-S$\\\0S%\-S&\\,S(\\,S\-S\,SS4$S.jjr5S9S/\\\,S\,S\\,4S0jjr6S:S\,S\,4S1jjr7S;S2\\S\\\,\4S\\Rb4S3jjr8S\\,\4S$\\,S\\,\\/44S4jr9g)>> import tabula >>> dfs = tabula.read_pdf("/path/to/sample.pdf", pages="all") N) defaultdict)deepcopy)asdict) getLogger)AnyDictIterableListOptionalSequenceTupleUnion)SubprocessTabulaTabulaVm) CSVParseError) localize_file) load_template) FileLikeObj TabulaOption _tabula_vmoptions java_optionspathencodingforce_subprocessreturnc1Skn[X5nU(a[XRUS9q[(dH[ XRS9q[(a)[R (d[XRUS9qO`[ [[5(a[RX1URS9 O([U5U- (a[RS5 [RX5$)zCall tabula-java with the given lists of Java options and tabula-py options, as well as an optional path to pass to tabula-java as a regular argument to use for any required output sent to stderr. >-Dfile.encoding=UTF8-Djava.awt.headless=true,-Dorg.slf4j.simpleLogger.defaultLogLevel=offH-Dorg.apache.commons.logging.Log=org.apache.commons.logging.impl.NoOpLog)rsilentr)rr#)rrr#z;java_options is ignored until rebooting the Python process.) _build_java_optionsrr#rrtabula isinstanceupdate_encodingsetloggerwarningcall_tabula_java)rrrrrIGNORED_JAVA_OPTIONSs E/var/www/html/land-ocr/venv/lib/python3.13/site-packages/tabula/io.py_runr..s'|>L%%nnx  :<O :j//))..8J J 0 1 1"" #  \ 1 1TU  & &w 55 input_path output_formatpandas_optionsmultiple_tables user_agent use_raw_urlpagesguessarea relative_arealatticestreampasswordr#columnsrelative_columnsformatbatch output_pathcSnU(a=SnUR5S:XaO&UR5S:XaSnO[SU<35eU(aSn[UU U U U U UUUUUUUUUS9n[XUS9unn[R R U5(d=[[R[R"[R5U5e[R RU5S :Xa[US 35e[UUUUUS 9nU(a[R"U5 [U5S :Xa[R!S 5 /$Uc0n[#U5nUR$nUS:Xa+[&R("U5nU(a [+UU5$U$UR-S U5US '[.R0"[2R4"U540UD6/$!U(a[R"U5 ff=f![.R6R8anSnUS- n[;UU5eSnAff=f)a./Read tables in PDF. Args: input_path (str, path object or file-like object): File like object of target PDF file. It can be URL, which is downloaded by tabula-py automatically. output_format (str, optional): Output format for returned object (``dataframe`` or ``json``) Giving this option enforces to ignore `multiple_tables` option. encoding (str, optional): Encoding type for pandas. Default: ``utf-8`` java_options (list, optional): Set java options. This option will be ignored once JVM is launched. Example: ``["-Xmx256m"]`` pandas_options (dict, optional): Set pandas options. Example: ``{'header': None}`` Note: With ``multiple_tables=True`` (default), pandas_options is passed to pandas.DataFrame, otherwise it is passed to pandas.read_csv. Those two functions are different for accept options like ``dtype``. multiple_tables (bool): It enables to handle multiple tables within a page. Default: ``True`` Note: If `multiple_tables` option is enabled, tabula-py uses not :func:`pd.read_csv()`, but :func:`pd.DataFrame()`. Make sure to pass appropriate `pandas_options`. user_agent (str, optional): Set a custom user-agent when download a pdf from a url. Otherwise it uses the default ``urllib.request`` user-agent. use_raw_url (bool): It enforces to use `input_path` string for url without quoting/dequoting. Default: False pages (str, int, `iterable` of `int`, optional): An optional values specifying pages to extract from. It allows `str`,`int`, `iterable` of :`int`. Default: `1` Examples: ``'1-2,3'``, ``'all'``, ``[1,2]`` guess (bool, optional): Guess the portion of the page to analyze per page. Default `True` If you use "area" option, this option becomes `False`. Note: As of tabula-java 1.0.3, guess option becomes independent from lattice and stream option, you can use guess and lattice/stream option at the same time. area (iterable of float, iterable of iterable of float, optional): Portion of the page to analyze(top,left,bottom,right). Default is entire page. Note: If you want to use multiple area options and extract in one table, it should be better to set ``multiple_tables=False`` for :func:`read_pdf()` Examples: ``[269.875,12.75,790.5,561]``, ``[[12.1,20.5,30.1,50.2], [1.0,3.2,10.5,40.2]]`` relative_area (bool, optional): If all area values are between 0-100 (inclusive) and preceded by ``'%'``, input will be taken as % of actual height or width of the page. Default ``False``. lattice (bool, optional): Force PDF to be extracted using lattice-mode extraction (if there are ruling lines separating each cell, as in a PDF of an Excel spreadsheet) stream (bool, optional): Force PDF to be extracted using stream-mode extraction (if there are no ruling lines separating each cell, as in a PDF of an Excel spreadsheet) password (str, optional): Password to decrypt document. Default: empty silent (bool, optional): Suppress all stderr output. columns (Sequence, optional): X coordinates of column boundaries. Must be sorted and of a datatype that preserves order, e.g. tuple or list Example: ``[10.1, 20.2, 30.3]`` relative_columns (bool, optional): If all values are between 0-100 (inclusive) and preceded by '%', input will be taken as % of actual width of the page. Default ``False``. format (str, optional): Format for output file or extracted object. (``"CSV"``, ``"TSV"``, ``"JSON"``) batch (str, optional): Convert all PDF files in the provided directory. This argument should be directory path. output_path (str, optional): Output file path. File format of it is depends on ``format``. Same as ``--outfile`` option of tabula-java. force_subprocess (bool): Force to use tabula-java subprocess mode. If you have some issue with jpype, try this option with same environment. Default ``False``. options (str, optional): Raw option string for tabula-java. Returns: list of DataFrames or dict. Raises: FileNotFoundError: If downloaded remote file doesn't exist. ValueError: If output_format is unknown format, or if downloaded remote file size is 0. tabula.errors.CSVParseError: If pandas CSV parsing failed. tabula.errors.JavaNotFoundError: If java is not installed or found. subprocess.CalledProcessError: If tabula-java execution failed. Examples: Here is a simple example. Note that :func:`read_pdf()` only extract page 1 by default. Notes: As of tabula-py 2.0.0, :func:`read_pdf()` sets `multiple_tables=True` by default. If you want to get consistent output with previous version, set `multiple_tables=False`. >>> import tabula >>> pdf_path = "https://github.com/chezou/tabula-py/raw/master/tests/resources/data.pdf" >>> tabula.read_pdf(pdf_path, stream=True) [ Unnamed: 0 mpg cyl disp hp drat wt qsec vs am gear carb 0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2] If you want to extract all pages, set ``pages="all"``. >>> dfs = tabula.read_pdf(pdf_path, pages="all") >>> len(dfs) 4 >>> dfs [ 0 1 2 3 4 5 6 7 8 9 0 mpg cyl disp hp drat wt qsec vs am gear 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5, 0 1 2 3 4 0 Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa, 0 1 2 3 4 5 0 NaN Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 145 6.7 3.3 5.7 2.5 virginica 2 146 6.7 3.0 5.2 2.3 virginica 3 147 6.3 2.5 5.0 1.9 virginica 4 148 6.5 3.0 5.2 2.0 virginica 5 149 6.2 3.4 5.4 2.3 virginica 6 150 5.9 3.0 5.1 1.8 virginica, 0 0 supp 1 VC 2 VC 3 VC 4 VC 5 VC 6 VC 7 VC 8 VC 9 VC 10 VC 11 VC 12 VC 13 VC 14 VC] NF dataframejsonJSONUnknown output_format=)r6r7r8r9r:r;r<r#r=r>r?r@rArr3)r5r3 is empty. Check the file, or download it manually.)rrzThe output file is empty.rz?Error failed to create DataFrame with different column tables. zNTry to set `multiple_tables=True`or set `names` option for `pandas_options`. )lower ValueErrorrrosrexistsFileNotFoundErrorerrnoENOENTstrerrorgetsizer.unlinklenr)r*rr?rDloads _extract_fromgetpdread_csvioStringIOerrors ParserErrorr) r0r1rrr2r3r4r5r6r7r8r9r:r;r<r#r=r>r?r@rArrtabula_optionsr temporaryoutput_pandas_optionsfmtraw_jsonemessages r-read_pdfrdYs&ZF    K /   "f ,F6 '789 9! #)'N$$J TOD) 77>>$   bkk%,,.GNN wwt!D6!TUVV    -    IIdO 6{a23 ~.O   C f}"jj0  ?; ;O'6&9&9*h&O # ,KK F 3GGH H/  IIdO 0yy$$ ,XG A G  + + ,s$ G&:+H & H H?'H::H? template_pathc[XSUS9unn[U5n[UUU U U U U UUUUUUUS9n/nUHen[U4UUUUS.[ UR U55D6n[ U[5(aURU5 MTURU5 Mg U(a[R"U5 U$!U(a[R"U5 ff=f)a"Read tables in PDF with a Tabula App template. Args: input_path (str, path object or file-like object): File like object of target PDF file. It can be URL, which is downloaded by tabula-py automatically. template_path (str, path object or file-like object): File like object for Tabula app template. It can be URL, which is downloaded by tabula-py automatically. pandas_options (dict, optional): Set pandas options like {'header': None}. encoding (str, optional): Encoding type for pandas. Default is 'utf-8' java_options (list, optional): Set java options like ``["-Xmx256m"]``. This option will be ignored once JVM is launched. user_agent (str, optional): Set a custom user-agent when download a pdf from a url. Otherwise it uses the default ``urllib.request`` user-agent. use_raw_url (bool): It enforces to use `input_path` string for url without quoting/dequoting. Default: False pages (str, int, `iterable` of `int`, optional): An optional values specifying pages to extract from. It allows `str`,`int`, `iterable` of :`int`. Default: `1` Examples: ``'1-2,3'``, ``'all'``, ``[1,2]`` guess (bool, optional): Guess the portion of the page to analyze per page. Default `True` If you use "area" option, this option becomes `False`. Note: As of tabula-java 1.0.3, guess option becomes independent from lattice and stream option, you can use guess and lattice/stream option at the same time. area (iterable of float, iterable of iterable of float, optional): Portion of the page to analyze(top,left,bottom,right). Default is entire page. Note: If you want to use multiple area options and extract in one table, it should be better to set ``multiple_tables=False`` for :func:`read_pdf()` Examples: ``[269.875,12.75,790.5,561]``, ``[[12.1,20.5,30.1,50.2], [1.0,3.2,10.5,40.2]]`` relative_area (bool, optional): If all area values are between 0-100 (inclusive) and preceded by ``'%'``, input will be taken as % of actual height or width of the page. Default ``False``. lattice (bool, optional): Force PDF to be extracted using lattice-mode extraction (if there are ruling lines separating each cell, as in a PDF of an Excel spreadsheet) stream (bool, optional): Force PDF to be extracted using stream-mode extraction (if there are no ruling lines separating each cell, as in a PDF of an Excel spreadsheet) password (str, optional): Password to decrypt document. Default: empty silent (bool, optional): Suppress all stderr output. columns (Sequence, optional): X coordinates of column boundaries. Must be sorted and of a datatype that preserves order, e.g. tuple or list Example: ``[10.1, 20.2, 30.3]`` relative_columns (bool, optional): If all values are between 0-100 (inclusive) and preceded by '%', input will be taken as % of actual width of the page. Default ``False``. format (str, optional): Format for output file or extracted object. (``"CSV"``, ``"TSV"``, ``"JSON"``) batch (str, optional): Convert all PDF files in the provided directory. This argument should be directory path. output_path (str, optional): Output file path. File format of it is depends on ``format``. Same as ``--outfile`` option of tabula-java. force_subprocess (bool): Force to use tabula-java subprocess mode. If you have some issue with jpype, try this option with same environment. Default ``False``. options (str, optional): Raw option string for tabula-java. Returns: list of DataFrame. Raises: FileNotFoundError: If downloaded remote file doesn't exist. ValueError: If output_format is unknown format, or if downloaded remote file size is 0. tabula.errors.CSVParseError: If pandas CSV parsing failed. tabula.errors.JavaNotFoundError: If java is not installed or found. subprocess.CalledProcessError: If tabula-java execution failed. Examples: You can use template file extracted by tabula app. >>> import tabula >>> tabula.read_pdf_with_template(pdf_path, "/path/to/data.tabula-template.json") [ Unnamed: 0 mpg cyl disp hp ... qsec vs am gear carb 0 Mazda RX4 21.0 6 160.0 110 ... 16.46 0 1 4 4 1 Mazda RX4 Wag 21.0 6 160.0 110 ... 17.02 0 1 4 4 2 Datsun 710 22.8 4 108.0 93 ... 18.61 1 1 4 1 3 Hornet 4 Drive 21.4 6 258.0 110 ... 19.44 1 0 3 1 4 Hornet Sportabout 18.7 8 360.0 175 ... 17.02 0 0 3 2 5 Valiant 18.1 6 225.0 105 ... 20.22 1 0 3 1 6 Duster 360 14.3 8 360.0 245 ... 15.84 0 0 3 4 7 Merc 240D 24.4 4 146.7 62 ... 20.00 1 0 4 2 8 Merc 230 22.8 4 140.8 95 ... 22.90 1 0 4 2 9 Merc 280 19.2 6 167.6 123 ... 18.30 1 0 4 4 10 Merc 280C 17.8 6 167.6 123 ... 18.90 1 0 4 4 11 Merc 450SE 16.4 8 275.8 180 ... 17.40 0 0 3 3 12 Merc 450SL 17.3 8 275.8 180 ... 17.60 0 0 3 3 13 Merc 450SLC 15.2 8 275.8 180 ... 18.00 0 0 3 3 14 Cadillac Fleetwood 10.4 8 472.0 205 ... 17.98 0 0 3 4 15 Lincoln Continental 10.4 8 460.0 215 ... 17.82 0 0 3 4 16 Chrysler Imperial 14.7 8 440.0 230 ... 17.42 0 0 3 4 17 Fiat 128 32.4 4 78.7 66 ... 19.47 1 1 4 1 18 Honda Civic 30.4 4 75.7 52 ... 18.52 1 1 4 2 19 Toyota Corolla 33.9 4 71.1 65 ... 19.90 1 1 4 1 20 Toyota Corona 21.5 4 120.1 97 ... 20.01 1 0 3 1 21 Dodge Challenger 15.5 8 318.0 150 ... 16.87 0 0 3 2 22 AMC Javelin 15.2 8 304.0 150 ... 17.30 0 0 3 2 23 Camaro Z28 13.3 8 350.0 245 ... 15.41 0 0 3 4 24 Pontiac Firebird 19.2 8 400.0 175 ... 17.05 0 0 3 2 25 Fiat X1-9 27.3 4 79.0 66 ... 18.90 1 1 4 1 26 Porsche 914-2 26.0 4 120.3 91 ... 16.70 0 1 5 2 27 Lotus Europa 30.4 4 95.1 113 ... 16.90 1 1 5 2 28 Ford Pantera L 15.8 8 351.0 264 ... 14.50 0 1 5 4 29 Ferrari Dino 19.7 6 145.0 175 ... 15.50 0 1 5 6 30 Maserati Bora 15.0 8 301.0 335 ... 14.60 0 1 5 8 31 Volvo 142E 21.4 4 121.0 109 ... 18.60 1 1 4 2 [32 rows x 12 columns], 0 1 2 3 4 0 NaN Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa, 0 1 2 3 4 5 0 NaN Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 145 6.7 3.3 5.7 2.5 virginica 2 146 6.7 3.0 5.2 2.3 virginica 3 147 6.3 2.5 5.0 1.9 virginica 4 148 6.5 3.0 5.2 2.0 virginica 5 149 6.2 3.4 5.4 2.3 virginica, Unnamed: 0 supp dose 0 4.2 VC 0.5 1 11.5 VC 0.5 2 7.3 VC 0.5 3 5.8 VC 0.5 4 6.4 VC 0.5 5 10.0 VC 0.5 6 11.2 VC 0.5 7 11.2 VC 0.5 8 5.2 VC 0.5 9 7.0 VC 0.5 10 16.5 VC 1.0 11 16.5 VC 1.0 12 15.2 VC 1.0 13 17.3 VC 1.0] z.json)r4suffixr5r6r7r8r9r:r;r<r#r=r>r?r@rAr)r2rrr) rrrrdrmerger&listextendappendrJrQ)r0rer2rrr4r5r6r7r8r9r:r;r<r#r=r>r?r@rArrrr]_options _force_option dataframesoption_dfs r-read_pdf_with_templaterrs\$W+OD)T"H  #)M JF-!)!1  ,,V45 C#t$$!!#&!!#&  IIdO   IIdO s A+B== Cc\Ub[U5S:Xa [S5e[U5n[UUUUUU U U U U UUUUS9n[ U5unn[ R RU5(d=[[R[ R"[R5U5e[ R RU5S:Xa[US35e[UUUUS9 U(a[ R"U5 gg!U(a[ R"U5 ff=f)a` Convert tables from PDF into a file. Output file will be saved into `output_path`. Args: input_path (file like obj): File like object of target PDF file. output_path (str): File path of output file. output_format (str, optional): Output format of this function (``csv``, ``json`` or ``tsv``). Default: ``csv`` java_options (list, optional): Set java options. This option will be ignored once JVM is launched. Example: ``"-Xmx256m"``. pages (str, int, `iterable` of `int`, optional): An optional values specifying pages to extract from. It allows `str`,`int`, `iterable` of :`int`. Default: `1` Examples: ``'1-2,3'``, ``'all'``, ``[1,2]`` guess (bool, optional): Guess the portion of the page to analyze per page. Default `True` If you use "area" option, this option becomes `False`. Note: As of tabula-java 1.0.3, guess option becomes independent from lattice and stream option, you can use guess and lattice/stream option at the same time. area (iterable of float, iterable of iterable of float, optional): Portion of the page to analyze(top,left,bottom,right). Default is entire page. Note: If you want to use multiple area options and extract in one table, it should be better to set ``multiple_tables=False`` for :func:`read_pdf()` Examples: ``[269.875,12.75,790.5,561]``, ``[[12.1,20.5,30.1,50.2], [1.0,3.2,10.5,40.2]]`` relative_area (bool, optional): If all area values are between 0-100 (inclusive) and preceded by ``'%'``, input will be taken as % of actual height or width of the page. Default ``False``. lattice (bool, optional): Force PDF to be extracted using lattice-mode extraction (if there are ruling lines separating each cell, as in a PDF of an Excel spreadsheet) stream (bool, optional): Force PDF to be extracted using stream-mode extraction (if there are no ruling lines separating each cell, as in a PDF of an Excel spreadsheet) password (str, optional): Password to decrypt document. Default: empty silent (bool, optional): Suppress all stderr output. columns (Sequence, optional): X coordinates of column boundaries. Must be sorted and of a datatype that preserves order, e.g. tuple or list Example: ``[10.1, 20.2, 30.3]`` format (str, optional): Format for output file or extracted object. (``"CSV"``, ``"TSV"``, ``"JSON"``) batch (str, optional): Convert all PDF files in the provided directory. This argument should be directory path. force_subprocess (bool): Force to use tabula-java subprocess mode. If you have some issue with jpype, try this option with same environment. Default ``False``. options (str, optional): Raw option string for tabula-java. Raises: FileNotFoundError: If downloaded remote file doesn't exist. ValueError: If output_format is unknown format, or if downloaded remote file size is 0. tabula.errors.JavaNotFoundError: If java is not installed or found. subprocess.CalledProcessError: If tabula-java execution failed. Nrz('output_path' shoud not be None or emptyrhrGr)rRrI_extract_format_for_conversionrrrJrrKrLrMrNrOrPr.rQ)r0rAr1rr6r7r8r9r:r;r<r#r=r>r?r@rrr\rr]s r- convert_intorvs`c+.!3CDD +M :F! #)N"$J/OD) 77>>$   bkk%,,.GNN wwt!D6!TUVV ^\4BRS  IIdO 9 IIdO s D D+ input_dircUb$[RRU5(d [S5e[ U5n [ UUUUUUU U U U U UUUS9n[ UX/S9 g)a! Convert tables from PDFs in a directory. Args: input_dir (str): Directory path. output_format (str, optional): Output format of this function (csv, json or tsv) java_options (list, optional): Set java options like `-Xmx256m`. This option will be ignored once JVM is launched. pages (str, int, `iterable` of `int`, optional): An optional values specifying pages to extract from. It allows `str`,`int`, `iterable` of :`int`. Default: `1` Examples: ``'1-2,3'``, ``'all'``, ``[1,2]`` guess (bool, optional): Guess the portion of the page to analyze per page. Default `True` If you use "area" option, this option becomes `False`. Note: As of tabula-java 1.0.3, guess option becomes independent from lattice and stream option, you can use guess and lattice/stream option at the same time. area (iterable of float, iterable of iterable of float, optional): Portion of the page to analyze(top,left,bottom,right). Default is entire page. Note: If you want to use multiple area options and extract in one table, it should be better to set ``multiple_tables=False`` for :func:`read_pdf()` Examples: ``[269.875,12.75,790.5,561]``, ``[[12.1,20.5,30.1,50.2], [1.0,3.2,10.5,40.2]]`` relative_area (bool, optional): If all area values are between 0-100 (inclusive) and preceded by ``'%'``, input will be taken as % of actual height or width of the page. Default ``False``. lattice (bool, optional): Force PDF to be extracted using lattice-mode extraction (if there are ruling lines separating each cell, as in a PDF of an Excel spreadsheet) stream (bool, optional): Force PDF to be extracted using stream-mode extraction (if there are no ruling lines separating each cell, as in a PDF of an Excel spreadsheet) password (str, optional): Password to decrypt document. Default: empty silent (bool, optional): Suppress all stderr output. columns (Sequence, optional): X coordinates of column boundaries. Must be sorted and of a datatype that preserves order, e.g. tuple or list Example: ``[10.1, 20.2, 30.3]`` relative_columns (bool, optional): If all values are between 0-100 (inclusive) and preceded by '%', input will be taken as % of actual width of the page. Default ``False``. format (str, optional): Format for output file or extracted object. (``"CSV"``, ``"TSV"``, ``"JSON"``) force_subprocess (bool): Force to use tabula-java subprocess mode. If you have some issue with jpype, try this option with same environment. Default ``False``. options (str, optional): Raw option string for tabula-java. Returns: Nothing. Outputs are saved into the same directory with `input_dir` Raises: ValueError: If input_dir doesn't exist. Nz0'input_dir' should be an existing directory pathrhrt)rJrisdirrIrurr.)rwr1rr6r7r8r9r:r;r<r#r=r>r?rArrr\s r-convert_into_by_batchrzIssH i 8 8KLL +M :F! #)N" Ir/ _java_optionsc8Uc/nO+[U[5(a[R"U5n[R "5S:Xa,Sn[ [URU55(dUS/-nUS:Xa[ SU55(dUS/- nU$)NDarwinzjava.awt.headlessr utf-8c3,# UH nSU;v M g7f)z file.encodingN).0opts r- &_build_java_options..sC]c?c)]sr) r&strshlexsplitplatformsystemanyfilterfind)r{rrs r-r$r$s M3 ' ' M2 H$ 6!&&-011)-G,HHM7C]CCC 45 5M r/cUR5S:XagUR5S:XagUR5S:Xag[SU<35e)NcsvCSVrDrEtsvTSVrF)rHrI)r1s r-rurusR%    & (    % '2M#3455r/rac /nUc0nURSS5n[X5up4UGHn[US5S:XaMUSVVs/sH2nUVs/sH!owS(d[ROUSPM# snPM4 nnnUn [ U[ 5(aU(dURU5n Sn [U 5H&upU [RLdMSU 3X'U S- n M( [[ 5n [U 5H4upXnUS:aUS-X'U SU3n XnUS:aMXU 'US-X'M6 [R"S XS .UD6nURS 5(d/URHn[R"UUS S 9UU'M! UR!U5 GM U$s snfs snnf![[4a MYf=f)zExtract tables from json. Args: raw_json (list): Decoded list from tabula-java JSON. pandas_options (dict optional): pandas options for `pd.DataFrame()` Nr=datartextz Unnamed: r.)rr=dtyperaise)rZr)pop_convert_pandas_csv_optionsrRnpnanr&int enumeraterrV DataFramerUr= to_numericrI TypeErrorrl)rar2 data_framesr=header_line_numbertablerowrb list_data_columns _unname_idxidxcolcounts cur_countdfcs r-rTrTsK  D1G"=n"VG uV}  " V} $>A ASV9RVV!F) 3S A$   (# . .w }}%78HK%h/"&&=&/ }$=HM1$K0 &1%5F&h/"K !m"+a-FK E9+.C & I !m !$ '!m 0\\ My Mn M!!'**ZZMM"Q%@BqE  2UX O B D#I.s* F7(F2;F7;F=2F7=GGcURSU5nURSS5nURSS5 US:Xa[U5(dSOSnX$4$UnX$4$)zTranslate `pd.read_csv()` options into `pd.DataFrame()` especially for header. Args: pandas_options (dict): pandas options like {'header': None}. columns (iterable): iterable of column name. namesheaderinferrNr)rbool)r2r=rrrs r-rr(sl!!'73H   ' 2Fz4( &*8nnQ$  ''$  ''r/)NNr~F)Nr~NNTNFNTNFFFNNNFNNNF)Nr~NNFNFNFFFNNNFNNNFN)rNNTNFFFNNNFNNFr)Nr~)r)N):__doc__rMrXrDrJrr collectionsrcopyr dataclassesrloggingrtypingrrr r r r r rnumpyrpandasrVbackendrrrZr file_utilrtemplaterutilrr__name__r)r__annotations__rrr.rfloatrrdrrrvrzr$rurTrrr/r-rs$  #NNN/!$#+ 8 ;? HU8%556 7> )-" (6 (649%(6 3-(6 (6  (6  (6Z$((,/3 $6:HL"!)-" !%"/^,^,C=^,^,49% ^, T#s(^, ^,  ^, ^,^, E#sHSM12 3^, ^, 5%(8E?*CCD E^,^,^, ^,sm^, TN!^,"huo &#^,$%^,& SM'^,( C=)^,*#+^,,-^,./^,0 4 tCH~ -.1^,H 04(, $6:HL"!)-" !%"!-wwwT#s(^,w w 49% w  ww E#sHSM12 3w w 5%(8E?*CCD Ewww wsmw TNw huo &!w"#w$ SM%w& C='w(#)w*+w,c]-w. 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