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 enales you to convert PDF tables 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:
tabula-py requires java environment so let's check the java environment on your machine.
!java -version
openjdk version "11.0.3" 2019-04-16 OpenJDK Runtime Environment (build 11.0.3+7-Ubuntu-1ubuntu218.04.1) OpenJDK 64-Bit Server VM (build 11.0.3+7-Ubuntu-1ubuntu218.04.1, mixed mode, sharing) openjdk version "11.0.3" 2019-04-16 OpenJDK Runtime Environment (build 11.0.3+7-Ubuntu-1ubuntu218.04.1) OpenJDK 64-Bit Server VM (build 11.0.3+7-Ubuntu-1ubuntu218.04.1, mixed mode, sharing)
After confirming the java environment, install tabula-py by using pip.
# To be more precisely, it's better to use `{sys.executable} -m pip install tabula-py`
!pip install -q tabula-py
|████████████████████████████████| 10.4MB 4.2MB/s
Before trying tabula-py, check your environment via tabula-py environment_info()
function, which shows Python version, Java version, and your OS environment.
import tabula
tabula.environment_info()
Python version: 3.6.8 (default, Jan 14 2019, 11:02:34) [GCC 8.0.1 20180414 (experimental) [trunk revision 259383]] Java version: openjdk version "11.0.3" 2019-04-16 OpenJDK Runtime Environment (build 11.0.3+7-Ubuntu-1ubuntu218.04.1) OpenJDK 64-Bit Server VM (build 11.0.3+7-Ubuntu-1ubuntu218.04.1, mixed mode, sharing) tabula-py version: 1.4.0 platform: Linux-4.14.79+-x86_64-with-Ubuntu-18.04-bionic uname: uname_result(system='Linux', node='b5c4edf3fd8a', release='4.14.79+', version='#1 SMP Wed Dec 19 21:19:13 PST 2018', machine='x86_64', processor='x86_64') linux_distribution: ('Ubuntu', '18.04', 'bionic') mac_ver: ('', ('', '', ''), '')
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.
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 |
read_pdf()
¶Note that read_pdf()
function reads only page 1 by default. For more details, use ?read_pdf
and ?tabula.wrapper.build_options
.
help(tabula.read_pdf)
Help on function read_pdf in module tabula.wrapper: read_pdf(input_path, output_format='dataframe', encoding='utf-8', java_options=None, pandas_options=None, multiple_tables=False, **kwargs) Read tables in PDF. Args: input_path (file like obj): File like object of tareget PDF file. output_format (str, optional): Output format of this function (dataframe or json) encoding (str, optional): Encoding type for pandas. Default is 'utf-8' java_options (list, optional): Set java options like `-Xmx256m`. pandas_options (dict, optional): Set pandas options like {'header': None}. multiple_tables (bool, optional): It enables to handle multiple tables within a page. Note: If `multiple_tables` option is enabled, tabula-py uses not :func:`pd.read_csv()`, but `pd.DataFrame()`. Make sure to pass appropriate `pandas_options`. kwargs (dict): Dictionary of option for tabula-java. Details are shown in :func:`build_options()` Returns: Extracted pandas DataFrame or list.
help(tabula.wrapper.build_options)
Help on function build_options in module tabula.wrapper: build_options(kwargs=None) Build options for tabula-java Args: options (str, optional): Raw option string for tabula-java. pages (str, int, :obj:`list` of :obj:`int`, optional): An optional values specifying pages to extract from. It allows `str`,`int`, :obj:`list` of :obj:`int`. Example: '1-2,3', 'all' or [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 that 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 (:obj:`list` of :obj:`float` or :obj:`list` of :obj:`list` of :obj:`float`, optional): Portion of the page to analyze(top,left,bottom,right). Example; [269.875,12.75,790.5,561] or [[12.1,20.5,30.1,50.2], [1.0,3.2,10.5,40.2]]. Default is entire page. 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 is 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 .pdfs in the provided directory. This argument should be directory. output_path (str, optional): Output file path. File format of it is depends on `format`. Same as `--outfile` option of tabula-java. Returns: `obj`:list: Built list of options
Let's set pages
option. Here is the extraction result of page 3:
# set pages option
tabula.read_pdf(pdf_path, pages=3, stream=True)
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 |
# pass pages as string
tabula.read_pdf(pdf_path, pages="1-2,3", 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 | 0.0 | 1.0 | 4.0 | 4.0 |
1 | Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0.0 | 1.0 | 4.0 | 4.0 |
2 | Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1.0 | 1.0 | 4.0 | 1.0 |
3 | Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1.0 | 0.0 | 3.0 | 1.0 |
4 | Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0.0 | 0.0 | 3.0 | 2.0 |
5 | Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1.0 | 0.0 | 3.0 | 1.0 |
6 | Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0.0 | 0.0 | 3.0 | 4.0 |
7 | Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1.0 | 0.0 | 4.0 | 2.0 |
8 | Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1.0 | 0.0 | 4.0 | 2.0 |
9 | Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1.0 | 0.0 | 4.0 | 4.0 |
10 | Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1.0 | 0.0 | 4.0 | 4.0 |
11 | Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0.0 | 0.0 | 3.0 | 3.0 |
12 | Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0.0 | 0.0 | 3.0 | 3.0 |
13 | Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0.0 | 0.0 | 3.0 | 3.0 |
14 | Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0.0 | 0.0 | 3.0 | 4.0 |
15 | Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0.0 | 0.0 | 3.0 | 4.0 |
16 | Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0.0 | 0.0 | 3.0 | 4.0 |
17 | Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1.0 | 1.0 | 4.0 | 1.0 |
18 | Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1.0 | 1.0 | 4.0 | 2.0 |
19 | Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1.0 | 1.0 | 4.0 | 1.0 |
20 | Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1.0 | 0.0 | 3.0 | 1.0 |
21 | Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0.0 | 0.0 | 3.0 | 2.0 |
22 | AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0.0 | 0.0 | 3.0 | 2.0 |
23 | Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0.0 | 0.0 | 3.0 | 4.0 |
24 | Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0.0 | 0.0 | 3.0 | 2.0 |
25 | Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1.0 | 1.0 | 4.0 | 1.0 |
26 | Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0.0 | 1.0 | 5.0 | 2.0 |
27 | Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1.0 | 1.0 | 5.0 | 2.0 |
28 | Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0.0 | 1.0 | 5.0 | 4.0 |
29 | Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0.0 | 1.0 | 5.0 | 6.0 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
32 | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
33 | 5.1 | 3.5 | 1.4 | 0.2 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
34 | 4.9 | 3.0 | 1.4 | 0.2 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
35 | 4.7 | 3.2 | 1.3 | 0.2 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
36 | 4.6 | 3.1 | 1.5 | 0.2 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
37 | 5.0 | 3.6 | 1.4 | 0.2 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
38 | 5.4 | 3.9 | 1.7 | 0.4 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
39 | NaN | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species | NaN | NaN | NaN | NaN | NaN | NaN |
40 | 145 | 6.7 | 3.3 | 5.7 | 2.5 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
41 | 146 | 6.7 | 3.0 | 5.2 | 2.3 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
42 | 147 | 6.3 | 2.5 | 5.0 | 1.9 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
43 | 148 | 6.5 | 3.0 | 5.2 | 2.0 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
44 | 149 | 6.2 | 3.4 | 5.4 | 2.3 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
45 | 150 | 5.9 | 3.0 | 5.1 | 1.8 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
46 | len | supp | dose | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
47 | 4.2 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
48 | 11.5 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
49 | 7.3 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
50 | 5.8 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
51 | 6.4 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
52 | 10.0 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
53 | 11.2 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
54 | 11.2 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
55 | 5.2 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
56 | 7.0 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
57 | 16.5 | VC | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
58 | 16.5 | VC | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
59 | 15.2 | VC | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
60 | 17.3 | VC | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
61 | 22.5 | VC | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
62 rows × 12 columns
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
.
# extract all pages
tabula.read_pdf(pdf_path, pages="all", 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 | 0.0 | 1.0 | 4.0 | 4.0 |
1 | Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0.0 | 1.0 | 4.0 | 4.0 |
2 | Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1.0 | 1.0 | 4.0 | 1.0 |
3 | Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1.0 | 0.0 | 3.0 | 1.0 |
4 | Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0.0 | 0.0 | 3.0 | 2.0 |
5 | Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1.0 | 0.0 | 3.0 | 1.0 |
6 | Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0.0 | 0.0 | 3.0 | 4.0 |
7 | Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1.0 | 0.0 | 4.0 | 2.0 |
8 | Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1.0 | 0.0 | 4.0 | 2.0 |
9 | Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1.0 | 0.0 | 4.0 | 4.0 |
10 | Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1.0 | 0.0 | 4.0 | 4.0 |
11 | Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0.0 | 0.0 | 3.0 | 3.0 |
12 | Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0.0 | 0.0 | 3.0 | 3.0 |
13 | Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0.0 | 0.0 | 3.0 | 3.0 |
14 | Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0.0 | 0.0 | 3.0 | 4.0 |
15 | Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0.0 | 0.0 | 3.0 | 4.0 |
16 | Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0.0 | 0.0 | 3.0 | 4.0 |
17 | Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1.0 | 1.0 | 4.0 | 1.0 |
18 | Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1.0 | 1.0 | 4.0 | 2.0 |
19 | Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1.0 | 1.0 | 4.0 | 1.0 |
20 | Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1.0 | 0.0 | 3.0 | 1.0 |
21 | Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0.0 | 0.0 | 3.0 | 2.0 |
22 | AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0.0 | 0.0 | 3.0 | 2.0 |
23 | Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0.0 | 0.0 | 3.0 | 4.0 |
24 | Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0.0 | 0.0 | 3.0 | 2.0 |
25 | Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1.0 | 1.0 | 4.0 | 1.0 |
26 | Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0.0 | 1.0 | 5.0 | 2.0 |
27 | Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1.0 | 1.0 | 5.0 | 2.0 |
28 | Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0.0 | 1.0 | 5.0 | 4.0 |
29 | Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0.0 | 1.0 | 5.0 | 6.0 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
32 | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
33 | 5.1 | 3.5 | 1.4 | 0.2 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
34 | 4.9 | 3.0 | 1.4 | 0.2 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
35 | 4.7 | 3.2 | 1.3 | 0.2 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
36 | 4.6 | 3.1 | 1.5 | 0.2 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
37 | 5.0 | 3.6 | 1.4 | 0.2 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
38 | 5.4 | 3.9 | 1.7 | 0.4 | setosa | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
39 | NaN | Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species | NaN | NaN | NaN | NaN | NaN | NaN |
40 | 145 | 6.7 | 3.3 | 5.7 | 2.5 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
41 | 146 | 6.7 | 3.0 | 5.2 | 2.3 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
42 | 147 | 6.3 | 2.5 | 5.0 | 1.9 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
43 | 148 | 6.5 | 3.0 | 5.2 | 2.0 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
44 | 149 | 6.2 | 3.4 | 5.4 | 2.3 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
45 | 150 | 5.9 | 3.0 | 5.1 | 1.8 | virginica | NaN | NaN | NaN | NaN | NaN | NaN |
46 | len | supp | dose | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
47 | 4.2 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
48 | 11.5 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
49 | 7.3 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
50 | 5.8 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
51 | 6.4 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
52 | 10.0 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
53 | 11.2 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
54 | 11.2 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
55 | 5.2 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
56 | 7.0 | VC | 0.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
57 | 16.5 | VC | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
58 | 16.5 | VC | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
59 | 15.2 | VC | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
60 | 17.3 | VC | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
61 | 22.5 | VC | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
62 rows × 12 columns
multiple_tables
option¶tabula-py assumes single tabule for an output by default, because of the limitation of pandas. To avoid this issue, you can set multiple_tables
option. By using this option, read_pdf
function returns list of DataFrames.
# extract multiple from all pages
multi_tables = tabula.read_pdf(pdf_path, pages="all", multiple_tables=True)
print(multi_tables[0].head())
print(multi_tables[1].head())
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 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
If you want to set a certain part of page, you can use area
option.
# set area option
tabula.read_pdf(pdf_path, area=(126,149,212,462), pages=2)
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 |
tabula-py has capability to convert not only DataFrame but also JSON, TSV, or CSV. You can set output format with output_format
option.
# read pdf as JSON
tabula.read_pdf(pdf_path, output_format="json")
[{'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, 'width': 24.407867431640625}, {'height': 12.352005004882812, 'left': 302.33087, 'text': '258.0', 'top': 174.41753, 'width': 34.6478271484375}, {'height': 12.352005004882812, 'left': 336.9787, 'text': '110', 'top': 174.41753, 'width': 26.899566650390625}, {'height': 12.352005004882812, 'left': 363.87827, 'text': '3.08', 'top': 174.41753, 'width': 30.24810791015625}, {'height': 12.352005004882812, 'left': 394.12637, 'text': '3.215', 'top': 174.41753, 'width': 34.64752197265625}, {'height': 12.352005004882812, 'left': 428.7739, 'text': '19.44', 'top': 174.41753, 'width': 34.64801025390625}, {'height': 12.352005004882812, 'left': 463.4219, 'text': '1', 'top': 174.41753, 'width': 21.1429443359375}, {'height': 12.352005004882812, 'left': 484.56485, 'text': '0', 'top': 174.41753, 'width': 25.238006591796875}, {'height': 12.352005004882812, 'left': 509.80286, 'text': '3', 'top': 174.41753, 'width': 30.24798583984375}], [{'height': 12.351882934570312, 'left': 247.14917, 'text': '18.7', 'top': 186.76953, 'width': 30.773834228515625}, {'height': 12.351882934570312, 'left': 277.923, 'text': '8', 'top': 186.76953, 'width': 24.407867431640625}, {'height': 12.351882934570312, 'left': 302.33087, 'text': '360.0', 'top': 186.76953, 'width': 34.6478271484375}, {'height': 12.351882934570312, 'left': 336.9787, 'text': '175', 'top': 186.76953, 'width': 26.899566650390625}, {'height': 12.351882934570312, 'left': 363.87827, 'text': '3.15', 'top': 186.76953, 'width': 30.24810791015625}, {'height': 12.351882934570312, 'left': 394.12637, 'text': '3.440', 'top': 186.76953, 'width': 34.64752197265625}, {'height': 12.351882934570312, 'left': 428.7739, 'text': '17.02', 'top': 186.76953, 'width': 34.64801025390625}, {'height': 12.351882934570312, 'left': 463.4219, 'text': '0', 'top': 186.76953, 'width': 21.1429443359375}, {'height': 12.351882934570312, 'left': 484.56485, 'text': '0', 'top': 186.76953, 'width': 25.238006591796875}, {'height': 12.351882934570312, 'left': 509.80286, 'text': '3', 'top': 186.76953, 'width': 30.24798583984375}], [{'height': 12.356597900390625, 'left': 247.14917, 'text': '18.1', 'top': 199.12141, 'width': 30.773834228515625}, {'height': 12.356597900390625, 'left': 277.923, 'text': '6', 'top': 199.12141, 'width': 24.407867431640625}, {'height': 12.356597900390625, 'left': 302.33087, 'text': '225.0', 'top': 199.12141, 'width': 34.6478271484375}, {'height': 12.356597900390625, 'left': 336.9787, 'text': '105', 'top': 199.12141, 'width': 26.899566650390625}, {'height': 12.356597900390625, 'left': 363.87827, 'text': '2.76', 'top': 199.12141, 'width': 30.24810791015625}, {'height': 12.356597900390625, 'left': 394.12637, 'text': '3.460', 'top': 199.12141, 'width': 34.64752197265625}, {'height': 12.356597900390625, 'left': 428.7739, 'text': '20.22', 'top': 199.12141, 'width': 34.64801025390625}, {'height': 12.356597900390625, 'left': 463.4219, 'text': '1', 'top': 199.12141, 'width': 21.1429443359375}, {'height': 12.356597900390625, 'left': 484.56485, 'text': '0', 'top': 199.12141, 'width': 25.238006591796875}, {'height': 12.356597900390625, 'left': 509.80286, 'text': '3', 'top': 199.12141, 'width': 30.24798583984375}], [{'height': 12.351959228515625, 'left': 247.14917, 'text': '14.3', 'top': 211.47801, 'width': 30.773834228515625}, {'height': 12.351959228515625, 'left': 277.923, 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247.14917, 'text': '24.4', 'top': 223.82997, 'width': 30.773834228515625}, {'height': 12.351806640625, 'left': 277.923, 'text': '4', 'top': 223.82997, 'width': 24.407867431640625}, {'height': 12.351806640625, 'left': 302.33087, 'text': '146.7', 'top': 223.82997, 'width': 34.6478271484375}, {'height': 12.351806640625, 'left': 336.9787, 'text': '62', 'top': 223.82997, 'width': 26.899566650390625}, {'height': 12.351806640625, 'left': 363.87827, 'text': '3.69', 'top': 223.82997, 'width': 30.24810791015625}, {'height': 12.351806640625, 'left': 394.12637, 'text': '3.190', 'top': 223.82997, 'width': 34.64752197265625}, {'height': 12.351806640625, 'left': 428.7739, 'text': '20.00', 'top': 223.82997, 'width': 34.64801025390625}, {'height': 12.351806640625, 'left': 463.4219, 'text': '1', 'top': 223.82997, 'width': 21.1429443359375}, {'height': 12.351806640625, 'left': 484.56485, 'text': '0', 'top': 223.82997, 'width': 25.238006591796875}, {'height': 12.351806640625, 'left': 509.80286, 'text': 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'top': 507.96643, 'width': 30.24810791015625}, {'height': 12.35247802734375, 'left': 394.12637, 'text': '3.570', 'top': 507.96643, 'width': 34.64752197265625}, {'height': 12.35247802734375, 'left': 428.7739, 'text': '14.60', 'top': 507.96643, 'width': 34.64801025390625}, {'height': 12.35247802734375, 'left': 463.4219, 'text': '0', 'top': 507.96643, 'width': 21.1429443359375}, {'height': 12.35247802734375, 'left': 484.56485, 'text': '1', 'top': 507.96643, 'width': 25.238006591796875}, {'height': 12.35247802734375, 'left': 509.80286, 'text': '5', 'top': 507.96643, 'width': 30.24798583984375}]], 'extraction_method': 'lattice', 'height': 395.14886474609375, 'left': 247.14917, 'right': 540.05084, 'top': 125.17005, 'width': 292.90167236328125}]
You can convert files directly rather creating Python objects with convert_into()
function.
# You can convert from pdf into JSON, CSV, TSV
tabula.convert_into(pdf_path, "test.json", output_format="json")
!cat test.json
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tabula.convert_into(pdf_path, "test.tsv", output_format="tsv")
!cat test.tsv
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
tabula.convert_into(pdf_path, "test.csv", output_format="csv", stream=True)
!cat test.csv
"",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
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.
tabula.read_pdf(pdf_path, pages="1", lattice=True)
mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 |
1 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 |
2 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 |
3 | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 |
4 | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 |
5 | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 |
6 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 |
7 | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 |
8 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 |
9 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 |
10 | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 |
11 | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 |
12 | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 |
13 | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 |
14 | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 |
15 | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 |
16 | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 |
17 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 |
18 | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 |
19 | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 |
20 | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 |
21 | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 |
22 | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 |
23 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 |
24 | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 |
25 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 |
26 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 |
27 | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 |
28 | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 |
29 | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 |
30 | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 |
tabula-py can handle tabula app template, which has area options set by GUI app to reuse.
!wget -q "https://github.com/chezou/tabula-py/raw/master/tests/resources/data.tabula-template.json"
tabula.read_pdf_with_template(pdf_path, "data.tabula-template.json")
[ Unnamed: 0 mpg cyl disp hp ... qsec vs am gear carb 0 Mazda RX4 21.0 6 160.0 110 ... 16.46 0 1 4 4 1 Mazda RX4 Wag 21.0 6 160.0 110 ... 17.02 0 1 4 4 2 Datsun 710 22.8 4 108.0 93 ... 18.61 1 1 4 1 3 Hornet 4 Drive 21.4 6 258.0 110 ... 19.44 1 0 3 1 4 Hornet Sportabout 18.7 8 360.0 175 ... 17.02 0 0 3 2 5 Valiant 18.1 6 225.0 105 ... 20.22 1 0 3 1 6 Duster 360 14.3 8 360.0 245 ... 15.84 0 0 3 4 7 Merc 240D 24.4 4 146.7 62 ... 20.00 1 0 4 2 8 Merc 230 22.8 4 140.8 95 ... 22.90 1 0 4 2 9 Merc 280 19.2 6 167.6 123 ... 18.30 1 0 4 4 10 Merc 280C 17.8 6 167.6 123 ... 18.90 1 0 4 4 11 Merc 450SE 16.4 8 275.8 180 ... 17.40 0 0 3 3 12 Merc 450SL 17.3 8 275.8 180 ... 17.60 0 0 3 3 13 Merc 450SLC 15.2 8 275.8 180 ... 18.00 0 0 3 3 14 Cadillac Fleetwood 10.4 8 472.0 205 ... 17.98 0 0 3 4 15 Lincoln Continental 10.4 8 460.0 215 ... 17.82 0 0 3 4 16 Chrysler Imperial 14.7 8 440.0 230 ... 17.42 0 0 3 4 17 Fiat 128 32.4 4 78.7 66 ... 19.47 1 1 4 1 18 Honda Civic 30.4 4 75.7 52 ... 18.52 1 1 4 2 19 Toyota Corolla 33.9 4 71.1 65 ... 19.90 1 1 4 1 20 Toyota Corona 21.5 4 120.1 97 ... 20.01 1 0 3 1 21 Dodge Challenger 15.5 8 318.0 150 ... 16.87 0 0 3 2 22 AMC Javelin 15.2 8 304.0 150 ... 17.30 0 0 3 2 23 Camaro Z28 13.3 8 350.0 245 ... 15.41 0 0 3 4 24 Pontiac Firebird 19.2 8 400.0 175 ... 17.05 0 0 3 2 25 Fiat X1-9 27.3 4 79.0 66 ... 18.90 1 1 4 1 26 Porsche 914-2 26.0 4 120.3 91 ... 16.70 0 1 5 2 27 Lotus Europa 30.4 4 95.1 113 ... 16.90 1 1 5 2 28 Ford Pantera L 15.8 8 351.0 264 ... 14.50 0 1 5 4 29 Ferrari Dino 19.7 6 145.0 175 ... 15.50 0 1 5 6 30 Maserati Bora 15.0 8 301.0 335 ... 14.60 0 1 5 8 31 Volvo 142E 21.4 4 121.0 109 ... 18.60 1 1 4 2 [32 rows x 12 columns], 0 1 2 3 4 0 NaN Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa, 0 1 2 3 4 5 0 NaN Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 145 6.7 3.3 5.7 2.5 virginica 2 146 6.7 3.0 5.2 2.3 virginica 3 147 6.3 2.5 5.0 1.9 virginica 4 148 6.5 3.0 5.2 2.0 virginica 5 149 6.2 3.4 5.4 2.3 virginica, Unnamed: 0 supp dose 0 4.2 VC 0.5 1 11.5 VC 0.5 2 7.3 VC 0.5 3 5.8 VC 0.5 4 6.4 VC 0.5 5 10.0 VC 0.5 6 11.2 VC 0.5 7 11.2 VC 0.5 8 5.2 VC 0.5 9 7.0 VC 0.5 10 16.5 VC 1.0 11 16.5 VC 1.0 12 15.2 VC 1.0 13 17.3 VC 1.0]
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