A PDF file is generally used to store text, images, and links. Moreover, the text data can be represented in different ways, such as lists, tables, bullet points, and links. While analyzing the data, you might encounter some situations where you have a PDF file, and you need to extract only table data from it.
This article details how to extract PDF tables in Python. Here in this tutorial, we will walk you through the Python library, Tabula-py , to extract tables from a PDF file. Also, we will explain how to write the table data to CSV files, but before we jump to the Python code, let's install the required libraries first.
Installing Dependencies
1. Python
tabula-py
Library
The
tabula-py
library is an open-source Python library that can extract tables from a pdf file and convert them into the pandas DataFrame format. This library is widely used in data analysis. To install the tabula-py library for your Python environment, run the following pip install command:
pip install tabula-py
When you run the above pip install command on your terminal or command prompt, it will install all the other required libraries, like pandas and numpy, so you do not need to install them separately.
2. A PDF File
As we will be extracting tables from a PDF file, so we require a PDF file for this tutorial. You can either use the PDF file present in your system or the URL for the PDF. For this tutorial, we have downloaded the food calories list pdf and saved it in the same directory where our Python script is located.
How to Extract PDF Tables in Python?
So let's begin with importing the required modules.
from tabula import read_pdf
Now set an identifier,
pdf_file
, that can either be the path or URL for the PDF file.
pdf_file="Food Calories List.pdf"
Now let's parse through the pdf file and extract all the tables present in the file using the
read_pdf()
function of tabula.
#list all tables
tables = read_pdf(pdf_file, pages='all')
The
read_pdf()
function will completely read the
pdf_file
and extract all the tables from the
all
pages. We can also specify the specific page number, such as
pages=3
, and grab the tables from page number 3.
If the value of the pages parameter is all, the
read_pdf()
function will return a
Python list
of pandas DataFrames. Now, we can loop through the list of all
tables
and convert all the tables from
dataframes
to
CSV
files.
table_number =1
for table in tables:
#remove Nan columns
table = table.dropna(axis="columns")
if not table.empty:
print(f"Table {table_number}")
print(table)
#convert the table dataframe into csv file
table.to_csv(f'table{table_number}.csv')
table_number += 1
-
dropna(axis="columns")is the pandas DataFrames function that will remove all NaN value columns from the data frame. -
The
.emptydata frame attribute returns the Boolean valueTrueif the data frame is empty, else it returnsFalse. -
to_csv()is also a pandas DataFrame function that converts a DataFrame data to a CSV file and saves it locally.
We have also used the
table_number
Python identifier in the above program to just count the number of non-empty tables. Now put all the code together and execute.
Program to Extract PDF Tables in Python and Convert Them Into CSV
from tabula import read_pdf, convert_into
pdf_file="Food Calories List.pdf"
#list all tables
tables = read_pdf(pdf_file, pages='all')
table_number =1
for table in tables:
#remove Nan columns
table = table.dropna(axis="columns")
if not table.empty:
print(f"Table {table_number}")
print(table)
#convert the table dataframe into csv file
table.to_csv(f'table{table_number}.csv')
table_number += 1
Output
Table 1
BREADS & CEREALS ... energy content
0 Bagel ( 1 average ) ... Medium
1 Biscuit digestives ... High
2 Jaffa cake ... Med-High
3 Bread white (thick slice) ... Medium
4 Bread wholemeal (thick) ... Low-med
5 Chapatis ... Medium
6 Cornflakes ... Med-High
7 Crackerbread ... Low Calorie
8 Cream crackers ... Low / portion
9 Crumpets ... Low-Med
10 Flapjacks basic fruit mix ... High
11 Macaroni (boiled) ... Low calorie
12 Muesli ... Med-high
13 Naan bread (normal) ... Medium
14 Noodles (boiled) ... Low calorie
15 Pasta ( normal boiled ) ... Low calorie
16 Pasta (wholemeal boiled ) ... Low calorie
17 Porridge oats (with water) ... Low calorie
18 Potatoes** (boiled) ... Low calorie
19 Potatoes** (roast) ... Medium
[20 rows x 4 columns]
Table 2
Lentils (boiled) 150 calories 100 calories Medium
0 Lettuce 4 calories 15 calories Very Low
1 Melon 14 calories 28 calories Medium
2 Mushrooms raw one\raverage 3 cals 15 cals Very low cal
3 Mushrooms (boiled) 12 calories 12 calories Low calorie
4 Mushrooms (fried) 100 calories 145 calories High
5 Olives 50 calories 80 calories Low calorie
6 Onion (boiled) 14 calories 18 calories Low calorie
7 One red Onion 49 cals 33 cals Low calorie
8 Onions spring 3 cals 25 cals Very low cal
9 Onion (fried) 86 calories 155 calories High
10 Orange 40 calories 30 calories Low calorie
11 Peas 210 calories 148 calories Medium
12 Peas dried & boiled 200 calories 120 calories Low calorie
13 Peach 35 calories 30 calories Low calorie
14 Pear 45 calories 38 calories Low calorie
15 Pepper yellow 6 cals 16 cals Very low
16 Pineapple 40 calories 40 calories Low calorie
17 Plum 30 calories 39 calories Low calorie
18 Spinach 8 calories 8 calories Low calorie
19 Strawberries (1 average) 10 calories 30 calories Low calorie
20 Sweetcorn 95 calories 130 calories Medium
21 Sweetcorn on the cob 70 calories 70 calories Low calorie
22 Tomato 30 calories 20 calories Low calorie
23 Tomato cherry 6 cals ( 3 toms) 17 Cals Very low cal
24 Tomato puree 70 calories 70 calories Low-Medium
25 Watercress 5 calories 20 calories Low calorie
Table 3
Milk & Dairy produce
0 Cheese average
1 Cheddar types average reduced
2 fat
3 Cheese spreads average
4 Cottage cheese low fat
5 Cottage cheese
6 Cream cheese
7 Cream fresh half
8 Cream fresh single
9 Cream fresh double
10 Cream fresh clotted
11 Custard
12 Eggs ( 1 average size)
13 Eggs fried
14 Fromage frais
15 Ice cream
16 Milk whole
17 Milk semi-skimmed
18 Milk skimmed
19 Milk Soya
20 Mousse flavored
21 Omelette with cheese
22 Trifle with cream
23 Yogurt natural
24 Yogurt reduced fat
Table 4
Fats & Sugars ... energy content
0 PURE FAT ... High
1 Bombay mix ... High
2 Butter ... High
3 Chewing gum ... Low calorie
4 Chocolate ... High
5 Cod liver oil ... High
6 Corn snack ... High
7 Crisps (chips US) average ... High
8 Honey ... Medium
9 Jam ... Medium
10 Lard ... High
11 Low fat spread ... High
12 Margarine ... High
13 Mars bar ... Med-High
14 Mint sweets ... High
15 Oils -corn, sunflower, olive ... High
16 Popcorn average ... High
17 Sugar white table sugar ... Medium
18 Sweets (boiled) ... Med-High
19 Syrup ... Medium
20 Toffee ... High
[21 rows x 4 columns]
Table 5
Fruit ... Water Content
0 Apple (1 average) ... 85 %
1 Apple cooking ... 88 %
2 Apricot ... 85 %
3 Avocado ... 60 %
4 Banana ... 75 %
5 Blackberries each ... 85 %
6 Blackcurrant each ... 77 %
7 Blueberries (new) 100g ... 81 %
8 Cherry each ... 83 %
9 Clementine ... 66 %
10 Currants ... 16 %
11 Damson ... 70 %
12 One average date 5g ... 14 %
13 Dates with inverted sugar 100g ... 12 %
14 Figs ... 24 %
15 Gooseberries ... 80 %
16 Grapes 100g Seedless ... 82 %
17 one average Grape 6g ... 82 %
18 Grapefruit whole ... 65 %
19 Guava ... 85 %
20 Kiwi ... 75 %
21 Lemon ... 85 %
22 Lychees ... 80 %
23 Mango ... 80 %
24 Melon Honeydew (130g) ... 90 %
25 Melon Cantaloupe (130g) ... 93 %
26 Nectarines ... 80 %
27 Olives ... 63 %
[28 rows x 4 columns]
Table 6
Orange average 35 calories 8.5 73 %
0 Orange large 350g 100 Cals 22g 75 %
1 Papaya Diced (small handful) 67 Cals (20g) 17g -
2 Passion Fruit 30 calories 3 50 %
3 Paw Paw 28 calories 6 70 %
4 Peach 35 calories 7 80 %
5 Pear 45 calories 12 77 %
6 Pineapple 50 calories 12 85 %
7 Plum 25 calories 6 79 %
8 Prunes 9 calories 2.2 37 %
9 Raisins 5 calories 1.4 13 %
10 Raspberries each 1.1 calories 0.2 87 %
11 Rhubarb 8 calories 0.8 95 %
12 Satsuma one average 112g 29 cals 6.5 88 %
13 Satsumas 100g 35 calories 8.5 88 %
14 Strawberries (1 average) 2.7 calories 0.6 90 %
15 Sultanas 5 calories 1.4 16 %
16 Tangerine 26 calories 6 60 %
17 Tomatoes (1 average size) 9 cals 2.2 93 %
18 Tomatoes Cherry (1 average size) 2 calories 0.5 90 %
When you execute the above code, you will see a similar output, and you can also check your working directory if all the tables were saved or not as CSV files.
Conclusion
In this Python tutorial, we learned how to extract PDF files in Python using the tabula library and convert them into CSV files with only a few lines of code. This is the beauty and power of Python and its libraries that we can crunch data and analyze it in a few lines of code.
We recommend you read the official documentation of tabula-py to know more about this powerful Python library. We have also written a tutorial on how to extract images and links from a PDF. You can read these articles and expand your knowledge about working with PDFs in Python:
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