In [1]:
from tabula import read_pdf

df = read_pdf("data.pdf")
In [2]:
df
Out[2]:
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
In [3]:
# set pages option
read_pdf("data.pdf", pages=3)
Out[3]:
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 [4]:
# pass pages as string
read_pdf("data.pdf", pages="1-2,3")
Out[4]:
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

In [5]:
# extract all pages
read_pdf("data.pdf", pages="all")
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 0.0 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

In [6]:
# set area option
read_pdf("data.pdf", area=(126,149,212,462), pages=2)
Out[6]:
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
In [7]:
# read pdf as JSON
read_pdf("data.pdf", output_format="json")
Out[7]:
[{'data': [[{'height': 0.0, 'left': 0.0, 'text': '', 'top': 0.0, 'width': 0.0},
    {'height': 4.980000019073486,
     'left': 253.13,
     'text': 'mpg',
     'top': 128.59,
     'width': 20.36700439453125},
    {'height': 4.980000019073486,
     'left': 283.9,
     'text': 'cyl',
     'top': 128.59,
     'width': 13.915863037109375},
    {'height': 4.980000019073486,
     'left': 313.23,
     'text': 'disp',
     'top': 128.59,
     'width': 19.3109130859375},
    {'height': 4.980000019073486,
     'left': 346.83,
     'text': 'hp',
     'top': 128.59,
     'width': 12.783050537109375},
    {'height': 4.980000019073486,
     'left': 369.86,
     'text': 'drat',
     'top': 128.59,
     'width': 20.029754638671875},
    {'height': 4.980000019073486,
     'left': 411.73,
     'text': 'wt',
     'top': 128.59,
     'width': 12.568695068359375},
    {'height': 4.980000019073486,
     'left': 439.4,
     'text': 'qsec',
     'top': 128.59,
     'width': 19.508636474609375},
    {'height': 4.980000019073486,
     'left': 469.4,
     'text': 'vs',
     'top': 128.59,
     'width': 10.634918212890625},
    {'height': 4.980000019073486,
     'left': 490.54,
     'text': 'am',
     'top': 128.59,
     'width': 15.031646728515625},
    {'height': 4.980000019073486,
     'left': 515.78,
     'text': 'gear',
     'top': 128.59,
     'width': 19.665771484375},
    {'height': 4.980000019073486,
     'left': 546.03,
     'text': 'carb',
     'top': 128.59,
     'width': 18.849998474121094}],
   [{'height': 4.980000019073486,
     'left': 154.69,
     'text': 'Mazda RX4',
     'top': 140.94,
     'width': 53.80647277832031},
    {'height': 4.980000019073486,
     'left': 254.23,
     'text': '21.0',
     'top': 140.94,
     'width': 19.141342163085938},
    {'height': 4.980000019073486,
     'left': 291.37,
     'text': '6',
     'top': 140.94,
     'width': 6.565704345703125},
    {'height': 4.980000019073486,
     'left': 308.31,
     'text': '160.0',
     'top': 140.94,
     'width': 24.1099853515625},
    {'height': 4.980000019073486,
     'left': 342.96,
     'text': '110',
     'top': 140.94,
     'width': 16.456024169921875},
    {'height': 4.980000019073486,
     'left': 370.44,
     'text': '3.90',
     'top': 140.94,
     'width': 19.177490234375},
    {'height': 4.980000019073486,
     'left': 400.1,
     'text': '2.620',
     'top': 140.94,
     'width': 24.16973876953125},
    {'height': 4.980000019073486,
     'left': 434.75,
     'text': '16.46',
     'top': 140.94,
     'width': 24.1492919921875},
    {'height': 4.980000019073486,
     'left': 473.61,
     'text': '0',
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    {'height': 4.980000019073486,
     'left': 498.85,
     'text': '1',
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     'left': 529.09,
     'text': '4',
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     'text': '4',
     'top': 140.94,
     'width': 4.980000019073486}],
   [{'height': 4.980000019073486,
     'left': 154.69,
     'text': 'Mazda RX4 Wag',
     'top': 153.29,
     'width': 76.44853210449219},
    {'height': 4.980000019073486,
     'left': 254.23,
     'text': '21.0',
     'top': 153.29,
     'width': 19.139389038085938},
    {'height': 4.980000019073486,
     'left': 291.37,
     'text': '6',
     'top': 153.29,
     'width': 6.5650634765625},
    {'height': 4.980000019073486,
     'left': 308.31,
     'text': '160.0',
     'top': 153.29,
     'width': 24.1099853515625},
    {'height': 4.980000019073486,
     'left': 342.96,
     'text': '110',
     'top': 153.29,
     'width': 16.456024169921875},
    {'height': 4.980000019073486,
     'left': 370.44,
     'text': '3.90',
     'top': 153.29,
     'width': 19.177490234375},
    {'height': 4.980000019073486,
     'left': 400.1,
     'text': '2.875',
     'top': 153.29,
     'width': 24.16973876953125},
    {'height': 4.980000019073486,
     'left': 434.75,
     'text': '17.02',
     'top': 153.29,
     'width': 24.1492919921875},
    {'height': 4.980000019073486,
     'left': 473.61,
     'text': '0',
     'top': 153.29,
     'width': 6.57476806640625},
    {'height': 4.980000019073486,
     'left': 498.85,
     'text': '1',
     'top': 153.29,
     'width': 6.618804931640625},
    {'height': 4.980000019073486,
     'left': 529.09,
     'text': '4',
     'top': 153.29,
     'width': 6.63311767578125},
    {'height': 4.980000019073486,
     'left': 559.89,
     'text': '4',
     'top': 153.29,
     'width': 4.980000019073486}],
   [{'height': 4.980000019073486,
     'left': 154.69,
     'text': 'Datsun 710',
     'top': 165.65,
     'width': 51.26588439941406},
    {'height': 4.980000019073486,
     'left': 254.23,
     'text': '22.8',
     'top': 165.65,
     'width': 19.136825561523438},
    {'height': 4.980000019073486,
     'left': 291.37,
     'text': '4',
     'top': 165.65,
     'width': 6.564239501953125},
    {'height': 4.980000019073486,
     'left': 308.31,
     'text': '108.0',
     'top': 165.65,
     'width': 24.109954833984375},
    {'height': 4.980000019073486,
     'left': 347.94,
     'text': '93',
     'top': 165.65,
     'width': 11.498016357421875},
    {'height': 4.980000019073486,
     'left': 370.44,
     'text': '3.85',
     'top': 165.65,
     'width': 19.178375244140625},
    {'height': 4.980000019073486,
     'left': 400.1,
     'text': '2.320',
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     'width': 24.169769287109375},
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     'left': 434.75,
     'text': '18.61',
     'top': 165.65,
     'width': 24.1492919921875},
    {'height': 4.980000019073486,
     'left': 473.61,
     'text': '1',
     'top': 165.65,
     'width': 6.57476806640625},
    {'height': 4.980000019073486,
     'left': 498.85,
     'text': '1',
     'top': 165.65,
     'width': 6.618804931640625},
    {'height': 4.980000019073486,
     'left': 529.09,
     'text': '4',
     'top': 165.65,
     'width': 6.63311767578125},
    {'height': 4.980000019073486,
     'left': 559.89,
     'text': '1',
     'top': 165.65,
     'width': 4.980000019073486}],
   [{'height': 4.980000019073486,
     'left': 154.69,
     'text': 'Hornet 4 Drive',
     'top': 178.0,
     'width': 66.93666076660156},
    {'height': 4.980000019073486,
     'left': 254.23,
     'text': '21.4',
     'top': 178.0,
     'width': 19.127243041992188},
    {'height': 4.980000019073486,
     'left': 291.37,
     'text': '6',
     'top': 178.0,
     'width': 6.561126708984375},
    {'height': 4.980000019073486,
     'left': 308.31,
     'text': '258.0',
     'top': 178.0,
     'width': 24.109893798828125},
    {'height': 4.980000019073486,
     'left': 342.96,
     'text': '110',
     'top': 178.0,
     'width': 16.456024169921875},
    {'height': 4.980000019073486,
     'left': 370.44,
     'text': '3.08',
     'top': 178.0,
     'width': 19.177490234375},
    {'height': 4.980000019073486,
     'left': 400.1,
     'text': '3.215',
     'top': 178.0,
     'width': 24.16973876953125},
    {'height': 4.980000019073486,
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In [8]:
# You can convert from pdf into JSON, CSV, TSV
from tabula import convert_into

convert_into("data.pdf", "test.json", output_format="json")
!cat test.json
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In [9]:
convert_into("data.pdf", "test.tsv", output_format="tsv")
!cat test.tsv
































In [10]:
convert_into("data.pdf", "test.csv", output_format="csv")
!cat test.csv