How To Extract Data From Common File Formats in Python?

Sometimes work with some datasets must have mostly worked with .csv(Comma Separated Value) files only. They are really a great starting point in applying Data Science techniques and algorithms. But many of us will land up in Data Science firms or take up real-world projects in Data Science sooner or later. Unfortunately in real-world projects, the data won’t be available to us in a neat .csv file. There we have to extract data from different sources like images, pdf files, doc files, image files, etc. In this article, we will see the perfect start to tackle those situations.

Below we will see how to extract relevant information from multiple such sources.

1. Multiple Sheet Excel Files

Note that if the Excel file has a single sheet then the same method to read CSV file (pd.read_csv(‘File.xlsx’)) might work. But it won’t in the case of multiple sheet files as shown in the below image where there are 3 sheets( Sheet1, Sheet2, Sheet3). In this case, it will just return the first sheet.

Excel sheet used: Click Here.

Example: We will see how to read this excel-file.

Python3

import pandas as pd

df = pd.read_excel('Sample1.xlsx', sheet_name = 1)

df.head()

Output:

Now let’s read a selected column of the same sheet:

Python3

df=pd.read_excel('Sample1.xlsx',

sheet_name = 1, usecols = 'A : C')

df.head()

Output:

Now let’s read all sheet together:

Sheet1 contains columns A, B, C; Sheet2 contains A, B, C, D and Sheet3 contains B, D. We will see a simple example below on how to read all the 3 sheets together and merge them into common columns.

Python3

df2 = pd.DataFrame()

for i in df.keys():

df2 = pd.concat([df2, df[i]],

axis = 0)

display(df2)

Output:

2. Extract Text From Images

Now we will discuss how to extract text from images.

For enabling our python program to have Character recognition capabilities, we would be making use of pytesseract OCR library. The library could be installed onto our python environment by executing the following command in the command interpreter of the OS:-

pip install pytesseract

The library (if used on Windows OS) requires the tesseract.exe binary to be also present for proper installation of the library. During the installation of the aforementioned executable, we would be prompted to specify a path for it. This path needs to be remembered as it would be utilized later on in the code. For most installations the path would be C:\\Program Files (x86)\\Tesseract-OCR\\tesseract.exe.

Image for demonstration:

Python3

from PIL import Image

import pytesseract

image = Image.open(r'pic.png')

pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'

print(pytesseract.image_to_string(image))

Output:

GeeksforGeeks

3. Extracting text from Doc File

Here we will extract text from the doc file using docx module.

For installation:

pip install python-
docx

Image for demonstration: Aniket_Doc.docx

Example 1: First we’ll extract the title:

Python3

import docx

doc = docx.Document('csv/g.docx')

print(doc.paragraphs[0].text)

Output:

My Name Aniket

Example 2: Then we’ll extract the different texts present(excluding the table).

Python3

l=[doc.paragraphs[i].text for i in range(len(doc.paragraphs))]

l=[i for i in l if len(i)!=0]

print(l)

Output:

[‘My Name Aniket’, ‘ Hello I am Aniket’, ‘I am giving tutorial on how to extract text from MS Doc.’, ‘Please go through it carefully.’]

Example 3: Now we’ll extract the table:

Python3

table = doc.tables[0]

list1 = []

list2 = []

for i in range(len(table.rows)):

for j in range(len(table.columns)):

list1.append(table.rows[i].cells[j].paragraphs[0].text)

list2.append(list1[:])

list1.clear()

print(list2)

Output:

[['A', 'B', 'C'], ['12', 'aNIKET', '@@@'], ['3', 'SOM', '+12&']]

4. Extracting Data From PDF File

The task is to extract Data( Image, text) from PDF in Python. We will extract the images from PDF files and save them using PyMuPDF library. First, we would have to install the PyMuPDF library using Pillow.

pip install PyMuPDF Pillow

Example 1:

Now we will extract data from the pdf version of the same doc file.

Python3

import fitz

docu=fitz.open('file.pdf')

text_list=[]

for i in range(docu.pageCount):

pg=docu.loadPage(i)

pg_txt=pg.getText('text')

text_list.append(pg_txt)

text_list=[i.replace(u'\u200b','') for i in text_list[0].split('\n') if len(i.strip()) ! = 0]

print(text_list)

Output:

[‘My Name Aniket ‘, ‘ Hello I am Aniket ‘, ‘I am giving tutorial on how to extract text from MS Doc. ‘, ‘Please go through it carefully. ‘, ‘A ‘, ‘B ‘, ‘C ‘, ’12 ‘, ‘aNIKET ‘, ‘@@@ ‘, ‘3 ‘, ‘SOM ‘, ‘+12& ‘]

Example 2: Extract image from PDF.