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tabula-py example notebook

tabula-py is a tool for convert PDF tables to pandas DataFrame. tabula-py is a wrapper of tabula-java, which requires java on your machine. tabula-py also enables you to convert tables in a PDF into CSV/TSV files.

tabula-py's PDF extraction accuracy is same as tabula-java or tabula app; GUI tool of tabula, so if you want to know the performance of tabula-py, I highly recommend you to try tabula app.

tabula-py is good for:

  • automation with Python script
  • advanced analytics after converting pandas DataFrame
  • casual analytics with Jupyter notebook or Google Colabolatory

Check Java environment and install tabula-py

tabula-py requires a java environment, so let's check the java environment on your machine.

In [1]:
!java -version
openjdk version "11.0.7" 2020-04-14
OpenJDK Runtime Environment (build 11.0.7+10-post-Ubuntu-2ubuntu218.04)
OpenJDK 64-Bit Server VM (build 11.0.7+10-post-Ubuntu-2ubuntu218.04, mixed mode, sharing)

After confirming the java environment, install tabula-py by using pip.

In [2]:
# To be more precisely, it's better to use `{sys.executable} -m pip install tabula-py`
!pip install -q tabula-py
     |████████████████████████████████| 10.4MB 2.7MB/s 

Before trying tabula-py, check your environment via tabula-py environment_info() function, which shows Python version, Java version, and your OS environment.

In [3]:
import tabula

tabula.environment_info()
Python version:
    3.6.9 (default, Apr 18 2020, 01:56:04) 
[GCC 8.4.0]
Java version:
    openjdk version "11.0.7" 2020-04-14
OpenJDK Runtime Environment (build 11.0.7+10-post-Ubuntu-2ubuntu218.04)
OpenJDK 64-Bit Server VM (build 11.0.7+10-post-Ubuntu-2ubuntu218.04, mixed mode, sharing)
tabula-py version: 2.1.0
platform: Linux-4.19.104+-x86_64-with-Ubuntu-18.04-bionic
uname:
    uname_result(system='Linux', node='385ad8f65f50', release='4.19.104+', version='#1 SMP Wed Feb 19 05:26:34 PST 2020', machine='x86_64', processor='x86_64')
linux_distribution: ('Ubuntu', '18.04', 'bionic')
mac_ver: ('', ('', '', ''), '')
    

Read a PDF with read_pdf() function

Let's read a PDF from GitHub. tabula-py can load a PDF or file like object on both local or internet by using read_pdf() function.

In [5]:
import tabula
pdf_path = "https://github.com/chezou/tabula-py/raw/master/tests/resources/data.pdf"

dfs = tabula.read_pdf(pdf_path, stream=True)
# read_pdf returns list of DataFrames
print(len(dfs))
dfs[0]
'pages' argument isn't specified.Will extract only from page 1 by default.
Got stderr: Jun 04, 2020 8:22:21 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:22:21 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

1
Out[5]:
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

Options for read_pdf()

Note that read_pdf() function reads only page 1 by default. For more details, use ?read_pdf and ?tabula.wrapper.build_options.

In [6]:
help(tabula.read_pdf)
Help on function read_pdf in module tabula.io:

read_pdf(input_path, output_format=None, encoding='utf-8', java_options=None, pandas_options=None, multiple_tables=True, user_agent=None, **kwargs)
    Read tables in PDF.
    
    Args:
        input_path (str, path object or file-like object):
            File like object of tareget 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``)
        encoding (str, optional):
            Encoding type for pandas. Default: ``utf-8``
        java_options (list, optional):
            Set java options.
    
            Example:
                ``["-Xmx256m"]``
        pandas_options (dict, optional):
            Set pandas options.
    
            Example:
                ``{'header': None}``
    
            Note:
                With ``multiple_tables=True`` (default), pandas_options is passed
                to pandas.read_csv, otherwise it is passed to pandas.DataFrame.
                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.
        kwargs:
            Dictionary of option for tabula-java. Details are shown in
            :func:`build_options()`
    
    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]

In [7]:
help(tabula.io.build_options)
Help on function build_options in module tabula.io:

build_options(pages=None, guess=True, area=None, relative_area=False, lattice=False, stream=False, password=None, silent=None, columns=None, format=None, batch=None, output_path=None, options='')
    Build options for tabula-java
    
    Args:
        pages (str, int, `list` of `int`, optional):
            An optional values specifying pages to extract from. It allows
            `str`,`int`, `list` 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 (list of float, list of list 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 (list, optional):
            X coordinates of column boundaries.
    
            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.
        output_path (str, optional):
            Output file path. File format of it is depends on ``format``.
            Same as ``--outfile`` option of tabula-java.
        options (str, optional):
            Raw option string for tabula-java.
    
    Returns:
        list:
            Built list of options

Let's set pages option. Here is the extraction result of page 3:

In [8]:
# set pages option
dfs = tabula.read_pdf(pdf_path, pages=3, stream=True)
dfs[0]
Got stderr: Jun 04, 2020 8:22:51 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:22:51 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

Out[8]:
len 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
14 22.5 VC 1.0
In [9]:
# pass pages as string
tabula.read_pdf(pdf_path, pages="1-2,3", stream=True)
Got stderr: Jun 04, 2020 8:23:57 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:23:57 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

Out[9]:
[             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],
    Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species
 0           5.1          3.5           1.4          0.2  setosa
 1           4.9          3.0           1.4          0.2  setosa
 2           4.7          3.2           1.3          0.2  setosa
 3           4.6          3.1           1.5          0.2  setosa
 4           5.0          3.6           1.4          0.2  setosa
 5           5.4          3.9           1.7          0.4  setosa,
    Unnamed: 0  Sepal.Length  Sepal.Width  Petal.Length  Petal.Width    Species
 0         145           6.7          3.3           5.7          2.5  virginica
 1         146           6.7          3.0           5.2          2.3  virginica
 2         147           6.3          2.5           5.0          1.9  virginica
 3         148           6.5          3.0           5.2          2.0  virginica
 4         149           6.2          3.4           5.4          2.3  virginica
 5         150           5.9          3.0           5.1          1.8  virginica,
      len 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
 14  22.5   VC   1.0]

You can set pages="all" for extration all pages. If you hit OOM error with Java, you should set appropriate -Xmx option for java_options.

In [10]:
# extract all pages
tabula.read_pdf(pdf_path, pages="all", stream=True)
Got stderr: Jun 04, 2020 8:24:02 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:24:02 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

Out[10]:
[             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],
    Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species
 0           5.1          3.5           1.4          0.2  setosa
 1           4.9          3.0           1.4          0.2  setosa
 2           4.7          3.2           1.3          0.2  setosa
 3           4.6          3.1           1.5          0.2  setosa
 4           5.0          3.6           1.4          0.2  setosa
 5           5.4          3.9           1.7          0.4  setosa,
    Unnamed: 0  Sepal.Length  Sepal.Width  Petal.Length  Petal.Width    Species
 0         145           6.7          3.3           5.7          2.5  virginica
 1         146           6.7          3.0           5.2          2.3  virginica
 2         147           6.3          2.5           5.0          1.9  virginica
 3         148           6.5          3.0           5.2          2.0  virginica
 4         149           6.2          3.4           5.4          2.3  virginica
 5         150           5.9          3.0           5.1          1.8  virginica,
      len 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
 14  22.5   VC   1.0]

Read partial area of PDF

If you want to set a certain part of page, you can use area option.

Note that as of tabula-py 2.0.0, multiple_tables option became True so if you want to use multiple area options like [[0, 0, 100, 50], [0, 50, 100, 100]], you need to set multiple_tables=False.

In [11]:
# set area option
dfs = tabula.read_pdf(pdf_path, area=[126,149,212,462], pages=2)
dfs[0]
Got stderr: Jun 04, 2020 8:24:12 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:24:12 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

Out[11]:
Unnamed: 0 Sepal.Width Petal.Length Petal.Width Species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
5 3.9 1.7 0.4 setosa NaN

Read giving column information

In [12]:
pdf_path2 = "https://github.com/chezou/tabula-py/raw/master/tests/resources/campaign_donors.pdf"

dfs = tabula.read_pdf(pdf_path2, columns=[47, 147, 256, 310, 375, 431, 504], guess=False, pages=1)
df = dfs[0].drop(["Unnamed: 0"], axis=1)
df
Out[12]:
Apellido Nombre Matricula Cuit Fecha Tipo Importe
0 MENA JUAN MARTÍN 27.083.460 20-27083460-5 09/10/2013 EFECTIVO $ 10.000,00
1 MOLLE MATÍAS 25.348.547 20-25348547-8 09/10/2013 EFECTIVO $ 10.000,00
2 MOLLEVI FEDERICO OSCAR 25.028.246 20-25028246-0 09/10/2013 EFECTIVO $ 10.000,00
3 PERAZZO PABLO DANIEL 25.348.394 20-25348394-7 09/10/2013 EFECTIVO $ 10.000,00
4 PICARDI FRANCO EDUARDO 27.382.271 20-27382271-3 09/10/2013 EFECTIVO $ 10.000,00
5 PISONI CARLOS ENRIQUE 26.034.823 20-26034823-0 09/10/2013 EFECTIVO $ 10.000,00
6 PONTORIERO MARÍA PAULA 23.249.597 27-23249597-4 09/10/2013 EFECTIVO $ 10.000,00
7 PULESTON JUAN MIGUEL 11.895.661 20-11895661-4 09/10/2013 EFECTIVO $ 10.000,00
8 REMÓN MABEL AURORA 11.292.939 27-11292939-3 09/10/2013 EFECTIVO $ 10.000,00
9 SARRABAYROUSE DIEGO 24.662.899 20-24662899-9 09/10/2013 EFECTIVO $ 10.000,00
10 SPATOLA ANALÍA CARMEN 24.560.922 27-24560922-7 09/10/2013 EFECTIVO $ 10.000,00
11 STAMILLA SERGIO ADRIÁN 29.364.226 20-29364226-6 09/10/2013 EFECTIVO $ 10.000,00
12 SZARANGOWICZ GUSTAVO ALEJANDRO 25.096.244 20-25096244-5 09/10/2013 EFECTIVO $ 10.000,00
13 TAILHADE LUIS RODOLFO 21.386.299 20-21386299-6 09/10/2013 EFECTIVO $ 10.000,00
14 TEDESCHI ADRIÁN ALBERTO 24.171.507 20-24171507-9 09/10/2013 EFECTIVO $ 10.000,00
15 URRIZA MARÍA TERESA 18.135.604 27-18135604-4 09/10/2013 EFECTIVO $ 10.000,00
16 USTARROZ GERÓNIMO JAVIER 24.912.947 20-24912947-0 09/10/2013 EFECTIVO $ 10.000,00
17 VALSANGIACOMO BLANC OFERNANDO JORGE 26.800.203 20-26800203-1 09/10/2013 EFECTIVO $ 10.000,00
18 VICENTE PABLO ARIEL 21.897.586 20-21897586-1 09/10/2013 EFECTIVO $ 10.000,00
19 AMBURI HUGO ALBERTO 14.096.560 20-14096560-0 09/10/2013 EFECTIVO $ 20.000,00
20 BERRA CLAUDIA SUSANA 14.433.112 27-14433112-0 09/10/2013 EFECTIVO $ 10.000,00
21 LASALA RICARDO ALBERTO 21.760.811 20-21760811-3 09/10/2013 EFECTIVO $ 10.000,00
22 AZPITARTE MARCELO CARLOS 16.018.569 20-16018569-5 09/10/2013 EFECTIVO $ 10.000,00
23 BERARDO CRISTINA BEATRIZ 12.609.615 27-12609615-7 09/10/2013 EFECTIVO $ 10.000,00
24 OREIRO RODOLFO MIGUEL 13.655.728 23-13655728-9 09/10/2013 EFECTIVO $ 10.000,00
25 FAIENZA MIGUEL ERNESTO 27.264.638 20-27264638-5 09/10/2013 EFECTIVO $ 20.000,00
26 GRANILLO FERNANDEZ JUAN MANUEL 27.235.280 20-27235280-2 09/10/2013 EFECTIVO $ 10.000,00
27 RODRIGUEZ PINGITORE CLAUDIA MONICA 23.343.332 27-23343332-8 09/10/2013 EFECTIVO $ 10.000,00
28 CORBELLA TERESITA GRACIELA 11.353.491 27-11353491-0 09/10/2013 EFECTIVO $ 5.000,00
29 CORVINO GERMAN HORACIO 27.491.524 20-27491524-3 09/10/2013 EFECTIVO $ 20.000,00
30 GARCIA DELATOUR AGUSTINA 29.501.230 27-29501230-2 09/10/2013 EFECTIVO $ 5.000,00
31 AGUIRRE LIDIA ELIZABETH 17.997.634 27-17997634-5 09/10/2013 EFECTIVO $ 5.000,00
32 FORNERO RAUL OSCAR 10.177.167 23-10177167-9 09/10/2013 EFECTIVO $ 5.000,00
33 SKOCILIC NATALIA ELIZABETH 30.237.737 27-30237737-0 09/10/2013 EFECTIVO $ 5.000,00
34 SARRA CARMEN 11.812.381 27-11812381-1 09/10/2013 EFECTIVO $ 20.000,00
35 VIVALDO MARÍA ITATÍ 20.458.214 27-20458214-4 09/10/2013 EFECTIVO $ 10.000,00
36 LORENZO FERNANDO LUIS 22.608.380 20-22608380-5 09/10/2013 EFECTIVO $ 15.000,00
37 VESTILLERO CARLOS SEBASTIAN 25.638.595 20-25638595-4 09/10/2013 EFECTIVO $ 10.000,00
38 CASTRO MARIA VICTORIA 22.642.061 23-22642061-4 09/10/2013 EFECTIVO $ 10.000,00
39 PÉREZ SIMONDINI ANDREA FABIANA 17.768.992 27-17768992-6 09/10/2013 EFECTIVO $ 10.000,00
40 CLAVERO MITRE LILIANA INES 11.176.760 27-11176760-8 09/10/2013 EFECTIVO $ 10.000,00
41 SÁNCHEZ MARIANA CLARA 20.205.651 27-20205651-8 09/10/2013 EFECTIVO $ 10.000,00
42 TALLÓN MATÍAS NORBERTO 33.498.601 20-33498601-3 09/10/2013 EFECTIVO $ 10.000,00
43 SUAREZ FERNANDO 11.633.291 20-11633291-5 09/10/2013 EFECTIVO $ 10.000,00
44 TARSINO MARIA EVELYN 23.276.482 27-23276482-7 09/10/2013 EFECTIVO $ 10.000,00
45 GRIMBERG MARQUEZ PAULA 27.680.600 27-27680600-4 09/10/2013 EFECTIVO $ 10.000,00
46 THORP MARIO 18.179.104 20-18179104-8 09/10/2013 EFECTIVO $ 10.000,00
47 BARABAN LORENA ELIZABETH 23.747.106 27-23747106-2 09/10/2013 EFECTIVO $ 10.000,00
48 FRECHERO EZEQUIEL 27.746.115 20-27746115-4 09/10/2013 EFECTIVO $ 10.000,00
49 TORRES CARLOS HUGO 25.957.223 20-25957223-2 09/10/2013 EFECTIVO $ 10.000,00
50 BILLERES GASTON MARTIN 23.301.948 20-23301948-9 09/10/2013 EFECTIVO $ 10.000,00
51 RODRIGUEZ EDGARDO JAVIER 21.502.793 20-21502793-8 09/10/2013 EFECTIVO $ 10.000,00
52 KARPINSKY ENRIQUE MARCELO 14.877.266 20-14877266-6 09/10/2013 EFECTIVO $ 10.000,00
53 GRIMBERG MARQUEZ JUAN CARLOS 7.831.240 20-07831240-9 09/10/2013 EFECTIVO $ 10.000,00
54 BOGADO GONZALEZ JULIO BERNABE 27.592.814 20-27592814-4 09/10/2013 EFECTIVO $ 10.000,00
55 NaN NaN NaN NaN ANE XO I-A-1 Págin a 12 de 14

Extract to JSON, TSV, or CSV

tabula-py has capability to convert not only DataFrame but also JSON, TSV, or CSV. You can set output format with output_format option.

In [13]:
# read pdf as JSON
tabula.read_pdf(pdf_path, output_format="json")
'pages' argument isn't specified.Will extract only from page 1 by default.
Got stderr: Jun 04, 2020 8:24:24 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:24:24 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

Out[13]:
[{'bottom': 520.3189,
  'data': [[{'height': 12.186347961425781,
     'left': 247.14917,
     'text': 'mpg',
     'top': 125.17005,
     'width': 30.773834228515625},
    {'height': 12.186347961425781,
     'left': 277.923,
     'text': 'cyl',
     'top': 125.17005,
     'width': 24.407867431640625},
    {'height': 12.186347961425781,
     'left': 302.33087,
     'text': 'disp',
     'top': 125.17005,
     'width': 34.6478271484375},
    {'height': 12.186347961425781,
     'left': 336.9787,
     'text': 'hp',
     'top': 125.17005,
     'width': 26.899566650390625},
    {'height': 12.186347961425781,
     'left': 363.87827,
     'text': 'drat',
     'top': 125.17005,
     'width': 30.24810791015625},
    {'height': 12.186347961425781,
     'left': 394.12637,
     'text': 'wt',
     'top': 125.17005,
     'width': 34.64752197265625},
    {'height': 12.186347961425781,
     'left': 428.7739,
     'text': 'qsec',
     'top': 125.17005,
     'width': 34.64801025390625},
    {'height': 12.186347961425781,
     'left': 463.4219,
     'text': 'vs',
     'top': 125.17005,
     'width': 21.1429443359375},
    {'height': 12.186347961425781,
     'left': 484.56485,
     'text': 'am',
     'top': 125.17005,
     'width': 25.238006591796875},
    {'height': 12.186347961425781,
     'left': 509.80286,
     'text': 'gear',
     'top': 125.17005,
     'width': 30.24798583984375}],
   [{'height': 12.352630615234375,
     'left': 247.14917,
     'text': '21.0',
     'top': 137.3564,
     'width': 30.773834228515625},
    {'height': 12.352630615234375,
     'left': 277.923,
     'text': '6',
     'top': 137.3564,
     'width': 24.407867431640625},
    {'height': 12.352630615234375,
     'left': 302.33087,
     'text': '160.0',
     'top': 137.3564,
     'width': 34.6478271484375},
    {'height': 12.352630615234375,
     'left': 336.9787,
     'text': '110',
     'top': 137.3564,
     'width': 26.899566650390625},
    {'height': 12.352630615234375,
     'left': 363.87827,
     'text': '3.90',
     'top': 137.3564,
     'width': 30.24810791015625},
    {'height': 12.352630615234375,
     'left': 394.12637,
     'text': '2.620',
     'top': 137.3564,
     'width': 34.64752197265625},
    {'height': 12.352630615234375,
     'left': 428.7739,
     'text': '16.46',
     'top': 137.3564,
     'width': 34.64801025390625},
    {'height': 12.352630615234375,
     'left': 463.4219,
     'text': '0',
     'top': 137.3564,
     'width': 21.1429443359375},
    {'height': 12.352630615234375,
     'left': 484.56485,
     'text': '1',
     'top': 137.3564,
     'width': 25.238006591796875},
    {'height': 12.352630615234375,
     'left': 509.80286,
     'text': '4',
     'top': 137.3564,
     'width': 30.24798583984375}],
   [{'height': 12.35186767578125,
     'left': 247.14917,
     'text': '21.0',
     'top': 149.70903,
     'width': 30.773834228515625},
    {'height': 12.35186767578125,
     'left': 277.923,
     'text': '6',
     'top': 149.70903,
     'width': 24.407867431640625},
    {'height': 12.35186767578125,
     'left': 302.33087,
     'text': '160.0',
     'top': 149.70903,
     'width': 34.6478271484375},
    {'height': 12.35186767578125,
     'left': 336.9787,
     'text': '110',
     'top': 149.70903,
     'width': 26.899566650390625},
    {'height': 12.35186767578125,
     'left': 363.87827,
     'text': '3.90',
     'top': 149.70903,
     'width': 30.24810791015625},
    {'height': 12.35186767578125,
     'left': 394.12637,
     'text': '2.875',
     'top': 149.70903,
     'width': 34.64752197265625},
    {'height': 12.35186767578125,
     'left': 428.7739,
     'text': '17.02',
     'top': 149.70903,
     'width': 34.64801025390625},
    {'height': 12.35186767578125,
     'left': 463.4219,
     'text': '0',
     'top': 149.70903,
     'width': 21.1429443359375},
    {'height': 12.35186767578125,
     'left': 484.56485,
     'text': '1',
     'top': 149.70903,
     'width': 25.238006591796875},
    {'height': 12.35186767578125,
     'left': 509.80286,
     'text': '4',
     'top': 149.70903,
     'width': 30.24798583984375}],
   [{'height': 12.35662841796875,
     'left': 247.14917,
     'text': '22.8',
     'top': 162.0609,
     'width': 30.773834228515625},
    {'height': 12.35662841796875,
     'left': 277.923,
     'text': '4',
     'top': 162.0609,
     'width': 24.407867431640625},
    {'height': 12.35662841796875,
     'left': 302.33087,
     'text': '108.0',
     'top': 162.0609,
     'width': 34.6478271484375},
    {'height': 12.35662841796875,
     'left': 336.9787,
     'text': '93',
     'top': 162.0609,
     'width': 26.899566650390625},
    {'height': 12.35662841796875,
     'left': 363.87827,
     'text': '3.85',
     'top': 162.0609,
     'width': 30.24810791015625},
    {'height': 12.35662841796875,
     'left': 394.12637,
     'text': '2.320',
     'top': 162.0609,
     'width': 34.64752197265625},
    {'height': 12.35662841796875,
     'left': 428.7739,
     'text': '18.61',
     'top': 162.0609,
     'width': 34.64801025390625},
    {'height': 12.35662841796875,
     'left': 463.4219,
     'text': '1',
     'top': 162.0609,
     'width': 21.1429443359375},
    {'height': 12.35662841796875,
     'left': 484.56485,
     'text': '1',
     'top': 162.0609,
     'width': 25.238006591796875},
    {'height': 12.35662841796875,
     'left': 509.80286,
     'text': '4',
     'top': 162.0609,
     'width': 30.24798583984375}],
   [{'height': 12.352005004882812,
     'left': 247.14917,
     'text': '21.4',
     'top': 174.41753,
     'width': 30.773834228515625},
    {'height': 12.352005004882812,
     'left': 277.923,
     'text': '6',
     'top': 174.41753,
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Convert PDF tables into CSV, TSV, or JSON files

You can convert files directly rather creating Python objects with convert_into() function.

In [14]:
# You can convert from pdf into JSON, CSV, TSV

tabula.convert_into(pdf_path, "test.json", output_format="json")
!cat test.json
'pages' argument isn't specified.Will extract only from page 1 by default.
Got stderr: Jun 04, 2020 8:24:28 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:24:28 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

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In [15]:
tabula.convert_into(pdf_path, "test.tsv", output_format="tsv")
!cat test.tsv
'pages' argument isn't specified.Will extract only from page 1 by default.
Got stderr: Jun 04, 2020 8:24:31 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:24:31 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear
21.0	6	160.0	110	3.90	2.620	16.46	0	1	4
21.0	6	160.0	110	3.90	2.875	17.02	0	1	4
22.8	4	108.0	93	3.85	2.320	18.61	1	1	4
21.4	6	258.0	110	3.08	3.215	19.44	1	0	3
18.7	8	360.0	175	3.15	3.440	17.02	0	0	3
18.1	6	225.0	105	2.76	3.460	20.22	1	0	3
14.3	8	360.0	245	3.21	3.570	15.84	0	0	3
24.4	4	146.7	62	3.69	3.190	20.00	1	0	4
22.8	4	140.8	95	3.92	3.150	22.90	1	0	4
19.2	6	167.6	123	3.92	3.440	18.30	1	0	4
17.8	6	167.6	123	3.92	3.440	18.90	1	0	4
16.4	8	275.8	180	3.07	4.070	17.40	0	0	3
17.3	8	275.8	180	3.07	3.730	17.60	0	0	3
15.2	8	275.8	180	3.07	3.780	18.00	0	0	3
10.4	8	472.0	205	2.93	5.250	17.98	0	0	3
10.4	8	460.0	215	3.00	5.424	17.82	0	0	3
14.7	8	440.0	230	3.23	5.345	17.42	0	0	3
32.4	4	78.7	66	4.08	2.200	19.47	1	1	4
30.4	4	75.7	52	4.93	1.615	18.52	1	1	4
33.9	4	71.1	65	4.22	1.835	19.90	1	1	4
21.5	4	120.1	97	3.70	2.465	20.01	1	0	3
15.5	8	318.0	150	2.76	3.520	16.87	0	0	3
15.2	8	304.0	150	3.15	3.435	17.30	0	0	3
13.3	8	350.0	245	3.73	3.840	15.41	0	0	3
19.2	8	400.0	175	3.08	3.845	17.05	0	0	3
27.3	4	79.0	66	4.08	1.935	18.90	1	1	4
26.0	4	120.3	91	4.43	2.140	16.70	0	1	5
30.4	4	95.1	113	3.77	1.513	16.90	1	1	5
15.8	8	351.0	264	4.22	3.170	14.50	0	1	5
19.7	6	145.0	175	3.62	2.770	15.50	0	1	5
15.0	8	301.0	335	3.54	3.570	14.60	0	1	5
In [16]:
tabula.convert_into(pdf_path, "test.csv", output_format="csv", stream=True)
!cat test.csv
'pages' argument isn't specified.Will extract only from page 1 by default.
Got stderr: Jun 04, 2020 8:24:35 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:24:35 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

"",mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb
Mazda RX4,21.0,6,160.0,110,3.90,2.620,16.46,0,1,4,4
Mazda RX4 Wag,21.0,6,160.0,110,3.90,2.875,17.02,0,1,4,4
Datsun 710,22.8,4,108.0,93,3.85,2.320,18.61,1,1,4,1
Hornet 4 Drive,21.4,6,258.0,110,3.08,3.215,19.44,1,0,3,1
Hornet Sportabout,18.7,8,360.0,175,3.15,3.440,17.02,0,0,3,2
Valiant,18.1,6,225.0,105,2.76,3.460,20.22,1,0,3,1
Duster 360,14.3,8,360.0,245,3.21,3.570,15.84,0,0,3,4
Merc 240D,24.4,4,146.7,62,3.69,3.190,20.00,1,0,4,2
Merc 230,22.8,4,140.8,95,3.92,3.150,22.90,1,0,4,2
Merc 280,19.2,6,167.6,123,3.92,3.440,18.30,1,0,4,4
Merc 280C,17.8,6,167.6,123,3.92,3.440,18.90,1,0,4,4
Merc 450SE,16.4,8,275.8,180,3.07,4.070,17.40,0,0,3,3
Merc 450SL,17.3,8,275.8,180,3.07,3.730,17.60,0,0,3,3
Merc 450SLC,15.2,8,275.8,180,3.07,3.780,18.00,0,0,3,3
Cadillac Fleetwood,10.4,8,472.0,205,2.93,5.250,17.98,0,0,3,4
Lincoln Continental,10.4,8,460.0,215,3.00,5.424,17.82,0,0,3,4
Chrysler Imperial,14.7,8,440.0,230,3.23,5.345,17.42,0,0,3,4
Fiat 128,32.4,4,78.7,66,4.08,2.200,19.47,1,1,4,1
Honda Civic,30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
Toyota Corolla,33.9,4,71.1,65,4.22,1.835,19.90,1,1,4,1
Toyota Corona,21.5,4,120.1,97,3.70,2.465,20.01,1,0,3,1
Dodge Challenger,15.5,8,318.0,150,2.76,3.520,16.87,0,0,3,2
AMC Javelin,15.2,8,304.0,150,3.15,3.435,17.30,0,0,3,2
Camaro Z28,13.3,8,350.0,245,3.73,3.840,15.41,0,0,3,4
Pontiac Firebird,19.2,8,400.0,175,3.08,3.845,17.05,0,0,3,2
Fiat X1-9,27.3,4,79.0,66,4.08,1.935,18.90,1,1,4,1
Porsche 914-2,26.0,4,120.3,91,4.43,2.140,16.70,0,1,5,2
Lotus Europa,30.4,4,95.1,113,3.77,1.513,16.90,1,1,5,2
Ford Pantera L,15.8,8,351.0,264,4.22,3.170,14.50,0,1,5,4
Ferrari Dino,19.7,6,145.0,175,3.62,2.770,15.50,0,1,5,6
Maserati Bora,15.0,8,301.0,335,3.54,3.570,14.60,0,1,5,8
Volvo 142E,21.4,4,121.0,109,4.11,2.780,18.60,1,1,4,2

Use lattice mode for more accurate extraction for spreadsheet style tables

If your tables have lines separating cells, you can use lattice option. By default, tabula-py sets guess=True, which is the same behavior for default of tabula app. If your tables don't have separation lines, you can try stream option.

As it mentioned, try tabula app before struglling with tabula-py option. Or, PDFplumber can be an alternative since it has different extraction strategy.

In [17]:
pdf_path3 = "https://github.com/tabulapdf/tabula-java/raw/master/src/test/resources/technology/tabula/spanning_cells.pdf"
dfs = tabula.read_pdf(
    pdf_path3,
    pages="1",
    lattice=True,
    pandas_options={"header": [0, 1]},
    area=[0, 0, 50, 100],
    relative_area=True,
    multiple_tables=False,
)
dfs[0]
Out[17]:
Improved operation scenario Unnamed: 1_level_0 Unnamed: 2_level_0 Unnamed: 3_level_0 Unnamed: 4_level_0 Unnamed: 5_level_0
Volume servers in: 2007 2008 2009 2010 2011
0 Server closets 1,505 1,580 1,643 1,673 1,689
1 Server rooms 1,512 1,586 1,646 1,677 1,693
2 Localized data centers 1,512 1,586 1,646 1,677 1,693
3 Mid-tier data centers 1,512 1,586 1,646 1,677 1,693
4 Enterprise-class data centers 1,512 1,586 1,646 1,677 1,693
5 Best practice scenario NaN NaN NaN NaN NaN
6 Volume servers in: 2007 2008 2009 2010 2011
7 Server closets 1,456 1,439 1,386 1,296 1,326
8 Server rooms 1,465 1,472 1,427 1,334 1,371
9 Localized data centers 1,465 1,471 1,426 1,334 1,371
10 Mid-tier data centers 1,465 1,471 1,426 1,334 1,371
11 Enterprise-class data centers 1,465 1,471 1,426 1,334 1,371
12 State-of-the-art scenario NaN NaN NaN NaN NaN
13 Volume servers in: 2007 2008 2009 2010 2011
14 Server closets 1,485 1,471 1,424 1,315 1,349
15 Server rooms 1,495 1,573 1,586 1,424 1,485
16 Localized data centers 1,495 1,572 1,585 1,424 1,485
17 Mid-tier data centers 1,495 1,572 1,585 1,424 1,485
18 Enterprise-class data centers 1,495 1,572 1,585 1,424 1,485

Use tabula app template

tabula-py can handle tabula app template, which has area options set by GUI app to reuse.

In [18]:
template_path = "https://github.com/chezou/tabula-py/raw/master/tests/resources/data.tabula-template.json"
tabula.read_pdf_with_template(pdf_path, template_path)
Got stderr: Jun 04, 2020 8:24:53 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:24:53 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

Got stderr: Jun 04, 2020 8:24:55 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:24:55 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

Got stderr: Jun 04, 2020 8:24:57 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font Symbol
Jun 04, 2020 8:24:57 AM org.apache.pdfbox.pdmodel.font.PDType1Font <init>
WARNING: Using fallback font LiberationSans for base font ZapfDingbats

Out[18]:
[             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],
    Unnamed: 0  Sepal.Width  Petal.Length  Petal.Width Species
 0         5.1          3.5           1.4          0.2  setosa
 1         4.9          3.0           1.4          0.2  setosa
 2         4.7          3.2           1.3          0.2  setosa
 3         4.6          3.1           1.5          0.2  setosa
 4         5.0          3.6           1.4          0.2  setosa,
    Unnamed: 0  Sepal.Length  Sepal.Width  Petal.Length  Petal.Width    Species
 0         145           6.7          3.3           5.7          2.5  virginica
 1         146           6.7          3.0           5.2          2.3  virginica
 2         147           6.3          2.5           5.0          1.9  virginica
 3         148           6.5          3.0           5.2          2.0  virginica
 4         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]

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