tabula-py

Aki Ariga

Aug 25, 2024

CONTENTS

1 Getting Started 3

1.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 FAQ 5

2.1 tabula-py does not work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 I can’t run from tabula import read_pdf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3 I got an empty DataFrame. How can I resolve it? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.4 The result is diﬀerent from tabula-java. Or, stream option seems not to work appropriately . . . 6

2.5 Can I use option xxx? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.6 How can I ignore useless area? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.7 I faced ParserError: Error tokenizing data. C error. How can I extract multiple tables? 8

2.8 I want to prevent tabula-py from stealing focus on every call on my mac . . . . . . . . . . . . . . . . 8

2.9 I got ? character with results on Windows. How can I avoid it? . . . . . . . . . . . . . . . . . . . . . 8

2.10 I can’t extract ﬁle/directory names with space on Windows . . . . . . . . . . . . . . . . . . . . . . . 8

2.11 I want to use a diﬀerent tabula .jar ﬁle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.12 I want to extract multiple tables from a document . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.13 Table cell contents sometimes overﬂow into the next row. . . . . . . . . . . . . . . . . . . . . . . . . 9

2.14 I got a warning/error message from PDFBox including org.apache.pdfbox.pdmodel.. Is it the

cause of the empty dataframe? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.15 java_options is ignored once read_pdf or similar funcion is called. . . . . . . . . . . . . . . . . 9

2.16 I can’t ﬁgure out accurate extraction with tabula-py. Are there any similar Python libraries? . . . . . 10

3 Contributing to tabula-py 11

3.1 Code formatting and testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4 tabula 13

4.1 High level interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.2 Internal interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5 tabula.errors 29

6 Indices and tables 31

Python Module Index 33

Index 35

tabula-py

tabula-py is a simple Python wrapper of tabula-java, which can read table of PDF. You can read tables from PDF

and convert them into pandas’ DataFrame. tabula-py also converts a PDF ﬁle into CSV/TSV/JSON ﬁle.

We highly recommend looking at the example notebook and trying it on Google Colab.

For high-level API reference, see High level interfaces.

CONTENTS 1

tabula-py

2 CONTENTS

CHAPTER

ONE

GETTING STARTED

1.1 Requirements

• Java

– Java 8+

• Python

– 3.8+

1.2 Installation

Before installing tabula-py, ensure you have Java runtime on your environment.

You can install tabula-py from PyPI with pip command.

pip install tabula-py

If you want to leverage faster execution with jpype, install with jpype extra.

pip install tabula-py[jpype]

Note: conda recipe on conda-forge is not maintained by us. We recommend installing via pip to use the latest version

of tabula-py.

1.2.1 Get tabula-py working (Windows 10)

This instruction is originally written by @lahoﬀm. Thanks!

• If you don’t have it already, install Java

• Try to run an example code (replace the appropriate PDF ﬁle name).

• If there’s a FileNotFoundError when it calls read_pdf(), and when you type java on com-

mand line it says 'java' is not recognized as an internal or external command, operable

program or batch file, you should set PATH environment variable to point to the Java directory.

• Find the main Java folder like jre... or jdk.... On Windows 10 it was under C:\Program Files\Java

• On Windows 10: Control Panel -> System and Security -> System -> Advanced System Settings -> Envi-

ronment Variables -> Select PATH –> Edit

tabula-py

• Add the bin folder like C:\Program Files\Java\jre1.8.0_144\bin, hit OK a bunch of times.

• On command line, java should now print a list of options, and tabula.read_pdf() should run.

1.3 Example

tabula-py enables you to extract tables from a PDF into a DataFrame, or a JSON. It can also extract tables from a PDF

and save the ﬁle as a CSV, a TSV, or a JSON.

import tabula

# Read pdf into a list of DataFrame

dfs = tabula.read_pdf("test.pdf", pages='all')

# Read remote pdf into a list of DataFrame

dfs2 = tabula.read_pdf("https://github.com/tabulapdf/tabula-java/raw/master/src/test/

˓→resources/technology/tabula/arabic.pdf")

# convert PDF into CSV

tabula.convert_into("test.pdf", "output.csv", output_format="csv", pages='all')

# convert all PDFs in a directory

tabula.convert_into_by_batch("input_directory", output_format='csv', pages='all')

See example notebook for more detail. I also recommend reading the tutorial article written by @aegis4048 and another

tutorial written by @tdpetrou.

Note: If you face some issues, we’d recommend trying tabula.app to see the limitation of tabula-java. Also, see FAQ

as well.

4 Chapter 1. Getting Started

CHAPTER

TWO

FAQ

2.1 tabula-py does not work

There are several possible reasons, but tabula-py is just a wrapper of tabula-java , make sure you’ve installed Java,

and you can use java command on your terminal. Many issue reporters forget to set PATH for java command.

You can check whether tabula-py can call java from the Python process with tabula.environment_info() func-

tion.

2.2 I can’t run from tabula import read_pdf

If you’ve installed tabula, it will conﬂict with the namespace. You should install tabula-py after removing tabula.

pip uninstall tabula

pip install tabula-py

2.3 I got an empty DataFrame. How can I resolve it?

tabula-py and tabula-java don’t support image-based PDFs. It should contain text-based table information.

Before tuning the tabula-py option, you have to check you set an appropriate pages option. By default, tabula-py

extracts tables from the ﬁrst page of your PDF, with pages=1 argument. If you want to extract from all pages, you

need to set pages option like pages="all" or pages=[1, 2, 3]. You might want to extract multiple tables from

multiple pages, if so you need to set multiple_tables=True together.

Depending on the PDF’s complexity, it might be diﬃcult to extract table contents accurately.

Tuning points of tabula-py are limited:

• Set speciﬁc area for accurate table detection

• Try lattice=True option for the table having explicit lines. Or try stream=True option

To know the limitation of tabula-java, I highly recommend using tabula app, the GUI version of tabula-java.

tabula app can:

• specify the area with GUI

• show a preview of the extraction with lattice or stream mode

• export template that is reusable for tabula-py

tabula-py

Even if you can’t extract tabula-py for those table contents which can be extracted tabula app appropriately, ﬁle an issue

on GitHub.

2.4 The result is diﬀerent from tabula-java. Or, stream option seems

not to work appropriately

tabula-py set guess option True by default, for beginners. It is known to make a conﬂict between stream option.

If you feel something strange with your result, please set guess=False.

2.5 Can I use option xxx?

Yes. You can use options argument as follows. The format is the same as CLI of tabula-java.

read_pdf(file_path, options="--columns 10.1,20.2,30.3")

2.6 How can I ignore useless area?

In short, you can extract with area and spreadsheet options.

In [4]: tabula.read_pdf('./table.pdf', spreadsheet=True, area=(337.29, 226.49, 472.85,␣

˓→384.91))

Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8

Out[4]:

Unnamed: 0 Col2 Col3 Col4 Col5

0 A B 12 R G

1 NaN R T 23 H

2 B B 33 R A

3 C T 99 E M

4 D I 12 34 M

5 E I I W 90

6 NaN 1 2 W h

7 NaN 4 3 E H

8 F E E4 R 4

2.6.1 How to use area option

According to tabula-java wiki, there is an explanation of how to specify the area: https://github.com/tabulapdf/

tabula-java/wiki/Using-the-command-line-tabula-extractor-tool#grab-coordinates-of-the-table-you-want

For example, using macOS’s preview, I got area information of this PDF:

6 Chapter 2. FAQ

tabula-py

java -jar ./target/tabula-1.0.1-jar-with-dependencies.jar -p all -a $y1,$x1,$y2,$x2 -o

˓→$csvfile $filename

given

# Note the left, top, height, and width parameters and calculate the following:

y1 = top

x1 = left

(continues on next page)

2.6. How can I ignore useless area? 7

tabula-py

(continued from previous page)

y2 = top + height

x2 = left + width

I conﬁrmed with tabula-java:

java -jar ./tabula/tabula-1.0.1-jar-with-dependencies.jar -a "337.29,226.49,472.85,384.91

˓→" table.pdf

Without -r(same as --spreadsheet) option, it does not work properly.

2.7 I faced ParserError: Error tokenizing data. C error. How

can I extract multiple tables?

This error occurs when pandas tries to extract multiple tables with diﬀerent column size at once. Use

multiple_tables option, then you can avoid this error.

2.8 I want to prevent tabula-py from stealing focus on every call on

my mac

Set java_options=["-Djava.awt.headless=true"]. kudos @jakekara

2.9 I got ? character with results on Windows. How can I avoid it?

If the encoding of PDF is UTF-8, you should set chcp 65001 on your terminal before launching a Python process.

chcp 65001

Then you can extract UTF-8 PDF with java_options="-Dfile.encoding=UTF8" option. This option will be added

with encoding='utf-8' option, which is also set by default.

# This is an example for java_options is set explicitly

df = read_pdf(file_path, java_options="-Dfile.encoding=UTF8")

Replace 65001 and UTF-8 appropriately, if the ﬁle encoding isn’t UTF-8.

2.10 I can’t extract ﬁle/directory names with space on Windows

You should escape the ﬁle/directory name yourself.

8 Chapter 2. FAQ

tabula-py

2.11 I want to use a diﬀerent tabula .jar ﬁle

You can specify the jar location via environment variable

export TABULA_JAR=".../tabula-x.y.z-jar-with-dependencies.jar"

2.12 I want to extract multiple tables from a document

You can use the following example code

df = read_pdf(file_path, multiple_tables=True)

The result will be a list of DataFrames. If you want separate tables across all pages in a document, use the pages

argument.

2.13 Table cell contents sometimes overﬂow into the next row.

You can try using lattice=True, which will often work if there are lines separating cells in the table.

2.14 I got a warning/error message from PDFBox including org.

apache.pdfbox.pdmodel.. Is it the cause of the empty dataframe?

No.

Sometimes, you might see a message like `` Jul 17, 2019 10:21:25 AM org.apache.pdfbox.pdmodel.font.PDType1Font

WARNING: Using fallback font NimbusSanL-Regu for Univers. Nothing was parsed from this one.`` This error mes-

sage came from Apache PDFBox which is used under tabula-java, and this is caused by the PDF itself. Neither tabula-py

nor tabula-java can’t handle the warning itself, except for the silent option that suppresses the warning.

2.15 java_options is ignored once read_pdf or similar funcion is

called.

Since jpype doesn’t support changing JVM options after the JVM is started, java_options is ignored once read_pdf

or similar funcion is called. If you want to change JVM options, you need to restart the Python process. See also:

https://jpype.readthedocs.io/en/latest/api.html#jpype.shutdownJVM

2.11. I want to use a diﬀerent tabula .jar ﬁle 9

tabula-py

2.16 I can’t ﬁgure out accurate extraction with tabula-py. Are there

any similar Python libraries?

I know tabula-py has limitations depending on tabula-java. Sometimes your PDF is too complex to tabula-py. If you

want to ﬁnd plan B, there are similar packages as the following:

• https://github.com/jsvine/pdfplumber

• https://camelot-py.readthedocs.io/en/master/

10 Chapter 2. FAQ

CHAPTER

THREE

CONTRIBUTING TO TABULA-PY

Interested in helping out? I’d love to have your help!

You can help by:

• Reporting a bug.

• Adding or editing documentation.

• Contributing code via a Pull Request.

• Write a blog post or spread the word about tabula-py to people who might be able to beneﬁt from using it.

3.1 Code formatting and testing

If you want to become a contributor, you can install dependency after cloning the repo as follows:

pip install -e .[dev, test]

pip install nox

For running tests and linter, run nox command.

nox .

3.2 Documentation

You can build document on your environment as follows:

pip install -e .[doc]

cd docs && make html

The documentation source is under docs/ directory and the document is published on Read the Docs automatically.

tabula-py

12 Chapter 3. Contributing to tabula-py

CHAPTER

FOUR

TABULA

4.1 High level interfaces

4.1.1 tabula.io

This module is a wrapper of tabula, which enables table extraction from a PDF.

This module extracts tables from a PDF into a pandas DataFrame via jpype.

Instead of importing this module, you can import public interfaces such as read_pdf(),

read_pdf_with_template(), convert_into(), convert_into_by_batch() from tabula module directory.

Note: If you want to use your own tabula-java JAR ﬁle, set TABULA_JAR to environment variable for JAR path.

Example

>>> import tabula

>>> dfs = tabula.read_pdf("/path/to/sample.pdf", pages="all")

tabula.io.convert_into(input_path: IO | str | PathLike, output_path: str, output_format: str = 'csv',

java_options: List[str] | None = None, pages: str | int | Iterable[int] | None = None,

guess: bool = True, area: Iterable[ﬂoat] | Iterable[Iterable[ﬂoat]] | None = None,

relative_area: bool = False, lattice: bool = False, stream: bool = False, password: str

| None = None, silent: bool | None = None, columns: Sequence[ﬂoat] | None = None,

relative_columns: bool = False, format: str | None = None, batch: str | None = None,

force_subprocess: bool = False, options: str = '') → None

Convert tables from PDF into a ﬁle. Output ﬁle will be saved into output_path.

Parameters

• 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.

tabula-py

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) – Por-

tion 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 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

14 Chapter 4. tabula

tabula-py

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.

tabula.io.convert_into_by_batch(input_dir: str, output_format: str = 'csv', java_options: List[str] | None =

None, pages: str | int | Iterable[int] | None = None, guess: bool = True,

area: Iterable[ﬂoat] | Iterable[Iterable[ﬂoat]] | None = None,

relative_area: bool = False, lattice: bool = False, stream: bool = False,

password: str | None = None, silent: bool | None = None, columns:

Sequence[ﬂoat] | None = None, relative_columns: bool = False, format:

str | None = None, output_path: str | None = None, force_subprocess: bool

= False, options: str = '') → None

Convert tables from PDFs in a directory.

Parameters

• 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) – Por-

tion of the page to analyze(top,left,bottom,right). Default is entire page.

4.1. High level interfaces 15

tabula-py

Note: If you want to use multiple area options and extract in one table, it should be better

to set multiple_tables=False for 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.

tabula.io.read_pdf(input_path: IO | str | PathLike, output_format: str | None = None, encoding: str = 'utf-8',

java_options: List[str] | None = None, pandas_options: Dict[str, Any] | None = None,

multiple_tables: bool = True, user_agent: str | None = None, use_raw_url: bool = False,

pages: str | int | Iterable[int] | None = None, guess: bool = True, area: Iterable[ﬂoat] |

Iterable[Iterable[ﬂoat]] | None = None, relative_area: bool = False, lattice: bool = False,

stream: bool = False, password: str | None = None, silent: bool | None = None, columns:

Sequence[ﬂoat] | None = None, relative_columns: bool = False, format: str | None = None,

batch: str | None = None, output_path: str | None = None, force_subprocess: bool = False,

options: str = '') → List[DataFrame] | Dict[str, Any]

Read tables in PDF.

16 Chapter 4. tabula

tabula-py

Parameters

• 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 pan-

das.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 pd.read_csv(), but 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 quot-

ing/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.

4.1. High level interfaces 17

tabula-py

• area (iterable of float, iterable of iterable of float, optional) – Por-

tion 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 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.

18 Chapter 4. tabula

tabula-py

• 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 read_pdf() only extract page 1 by default.

Notes:

As of tabula-py 2.0.0, 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

(continues on next page)

4.1. High level interfaces 19

tabula-py

(continued from previous page)

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)

>>> 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

(continues on next page)

20 Chapter 4. tabula

tabula-py

(continued from previous page)

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]

4.1. High level interfaces 21

tabula-py

tabula.io.read_pdf_with_template(input_path: IO | str | PathLike, template_path: IO | str | PathLike,

pandas_options: Dict[str, Any] | None = None, encoding: str = 'utf-8',

java_options: List[str] | None = None, user_agent: str | None = None,

use_raw_url: bool = False, pages: str | int | Iterable[int] | None = None,

guess: bool = False, area: Iterable[ﬂoat] | Iterable[Iterable[ﬂoat]] |

None = None, relative_area: bool = False, lattice: bool = False, stream:

bool = False, password: str | None = None, silent: bool | None = None,

columns: Sequence[ﬂoat] | None = None, relative_columns: bool =

False, format: str | None = None, batch: str | None = None, output_path:

str | None = None, force_subprocess: bool = False, options: str | None =

None) → List[DataFrame]

Read tables in PDF with a Tabula App template.

Parameters

• 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 quot-

ing/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) – Por-

tion 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 read_pdf()

22 Chapter 4. tabula

tabula-py

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.

4.1. High level interfaces 23

tabula-py

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

(continues on next page)

24 Chapter 4. tabula

tabula-py

(continued from previous page)

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]

4.1.2 tabula.util

Utility module providing some convenient functions.

class tabula.util.TabulaOption(pages: str | int | Iterable[int] | None = None, guess: bool = True, area:

Iterable[ﬂoat] | Iterable[Iterable[ﬂoat]] | None = None, relative_area: bool

= False, lattice: bool = False, stream: bool = False, password: str | None =

None, silent: bool | None = None, columns: Sequence[ﬂoat] | None = None,

relative_columns: bool = False, format: str | None = None, batch: str | None

= None, output_path: str | None = None, options: str | None = '',

multiple_tables: bool = True)

Bases: object

Build options for tabula-java

Parameters

• 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) – Por-

tion of the page to analyze(top,left,bottom,right). Default is entire page.

4.1. High level interfaces 25

tabula-py

Note: If you want to use multiple area options and extract in one table, it should be better

to set multiple_tables=False for 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.

• options (str, optional) – Raw option string for tabula-java.

• multiple_tables (bool, optional) – Extract multiple tables into a dataframe. Default:

True

area: Iterable[float] | Iterable[Iterable[float]] | None = None

batch: str | None = None

build_option_list() → List[str]

Convert to tabula-java option list

columns: Sequence[float] | None = None

format: str | None = None

26 Chapter 4. tabula

tabula-py

guess: bool = True

lattice: bool = False

merge(other: TabulaOption) → TabulaOption

Merge two TabulaOption. self will overwrite other ﬁelds’ values.

multiple_tables: bool = True

options: str | None = ''

output_path: str | None = None

pages: str | int | Iterable[int] | None = None

password: str | None = None

relative_area: bool = False

relative_columns: bool = False

silent: bool | None = None

stream: bool = False

tabula.util.environment_info() → None

Show environment information for reporting.

Returns

Detailed information like Python version, Java version, or OS environment, etc.

Return type

str

tabula.util.java_version() → str

Show Java version

Returns

Result of java -version

Return type

str

4.2 Internal interfaces

4.2.1 tabula.template

tabula.template.load_template(path_or_buﬀer: IO | str | PathLike) → List[TabulaOption]

Build tabula-py option from template ﬁle

Parameters

path_or_buffer (str, path object or file-like object) – File like object of Tabula

app template.

Returns

tabula-py options

Return type

dict

4.2. Internal interfaces 27

tabula-py

4.2.2 tabula.ﬁle_util

tabula.file_util.is_file_like(obj: IO | str | PathLike) → bool

Check ﬁle like object

Parameters

obj – ﬁle like object.

Returns

ﬁle like object or not

Return type

bool

tabula.file_util.localize_file(path_or_buﬀer: IO | str | PathLike, user_agent: str | None = None, suﬃx: str

= '.pdf', use_raw_url=False) → Tuple[str, bool]

Ensure localize target ﬁle.

If the target ﬁle is remote, this function fetches into local storage.

Parameters

• path_or_buffer (str) – File path or ﬁle like object or URL of target ﬁle.

• 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.

• suffix (str, optional) – File extension to check.

• use_raw_url (bool) – Use path_or_buﬀer without quoting/dequoting.

Returns

tuple of str and bool, which represents ﬁle name in local storage and temporary ﬁle ﬂag.

Return type

(str, bool)

28 Chapter 4. tabula

CHAPTER

FIVE

TABULA.ERRORS

exception tabula.errors.CSVParseError(message: Any, cause: Any)

Bases: ParserError

Error represents CSV parse error, which mainly caused by pandas.

exception tabula.errors.JavaNotFoundError

Bases: Exception

Error represents Java doesn’t exist.

tabula-py

30 Chapter 5. tabula.errors

CHAPTER

SIX

INDICES AND TABLES

• genindex

• modindex

• search

tabula-py

32 Chapter 6. Indices and tables

PYTHON MODULE INDEX

tabula.errors, 29

tabula.file_util, 28

tabula.io, 13

tabula.template, 27

tabula.util, 25

tabula-py

34 Python Module Index

INDEX

area (tabula.util.TabulaOption attribute), 26

batch (tabula.util.TabulaOption attribute), 26

build_option_list() (tabula.util.TabulaOption

method), 26

columns (tabula.util.TabulaOption attribute), 26

convert_into() (in module tabula.io), 13

convert_into_by_batch() (in module tabula.io), 15

CSVParseError, 29

environment_info() (in module tabula.util), 27

format (tabula.util.TabulaOption attribute), 26

guess (tabula.util.TabulaOption attribute), 26

is_file_like() (in module tabula.ﬁle_util), 28

java_version() (in module tabula.util), 27

JavaNotFoundError, 29

lattice (tabula.util.TabulaOption attribute), 27

load_template() (in module tabula.template), 27

localize_file() (in module tabula.ﬁle_util), 28

merge() (tabula.util.TabulaOption method), 27

module

tabula.errors, 29

tabula.file_util, 28

tabula.io, 13

tabula.template, 27

tabula.util, 25

multiple_tables (tabula.util.TabulaOption attribute),

options (tabula.util.TabulaOption attribute), 27

output_path (tabula.util.TabulaOption attribute), 27

pages (tabula.util.TabulaOption attribute), 27

password (tabula.util.TabulaOption attribute), 27

read_pdf() (in module tabula.io), 16

read_pdf_with_template() (in module tabula.io), 21

relative_area (tabula.util.TabulaOption attribute), 27

relative_columns (tabula.util.TabulaOption attribute),

silent (tabula.util.TabulaOption attribute), 27

stream (tabula.util.TabulaOption attribute), 27

tabula.errors

module, 29

tabula.file_util

module, 28

tabula.io

module, 13

tabula.template

module, 27

tabula.util

module, 25

TabulaOption (class in tabula.util), 25