How to read most commonly used file formats in Data Science (using Python)?

1201904 12 Mar, 2019
11 min read


If you have been part of the data science (or any data!) industry, you would know the challenge of working with different data types. Different formats, different compression, different parsing on different systems – you could be quickly pulling your hair! Oh and I have not talked about the unstructured data or semi-structured data yet.

For any data scientist or data engineer, dealing with different formats can become a tedious task. In real-world, people rarely get neat tabular data. Thus, it is mandatory for any data scientist (or a data engineer) to be aware of different file formats, common challenges in handling them and the best / efficient ways to handle this data in real life.

This article provides common formats a data scientist or a data engineer must be aware of. I will first introduce you to different common file formats used in the industry. Later, we’ll see how to read these file formats in Python.

P.S. In rest of this article, I will be referring to a data scientist, but the same applies to a data engineer or any data science professional.


Table of Contents

  1. What is a file format?
  2. Why should a data scientist understand different file formats?
  3. Different file formats and how to read them in Python?
    1. Comma-separated values
    2. XLSX
    3. ZIP
    4. Plain Text (txt)
    5. JSON
    6. XML
    7. HTML
    8. Images
    9. Hierarchical Data Format
    10. PDF
    11. DOCX
    12. MP3
    13. MP4


1. What is a file format?

A file format is a standard way in which information is encoded for storage in a file. First, the file format specifies whether the file is a binary or ASCII file. Second, it shows how the information is organized. For example, comma-separated values (CSV) file format stores tabular data in plain text.

To identify a file format, you can usually look at the file extension to get an idea. For example, a file saved with name “Data” in “CSV” format will appear as “Data.csv”. By noticing “.csv” extension we can clearly identify that it is a “CSV” file and data is stored in a tabular format.


2. Why should a data scientist understand different file formats?

Usually, the files you will come across will depend on the application you are building. For example, in an image processing system, you need image files as input and output. So you will mostly see files in jpeg, gif or png format.

As a data scientist, you need to understand the underlying structure of various file formats, their advantages and dis-advantages. Unless you understand the underlying structure of the data, you will not be able to explore it. Also, at times you need to make decisions about how to store data.

Choosing the optimal file format for storing data can improve the performance of your models in data processing.

Now, we will look at the following file formats and how to read them in Python:

  • Comma-separated values
  • XLSX
  • ZIP
  • Plain Text (txt)
  • JSON
  • XML
  • HTML
  • Images
  • Hierarchical Data Format
  • PDF
  • DOCX
  • MP3
  • MP4


3. Different file formats and how to read them in Python

3.1 Comma-separated values

Comma-separated values file format falls under spreadsheet file format.

What is Spreadsheet File Format?

In spreadsheet file format, data is stored in cells. Each cell is organized in rows and columns. A column in the spreadsheet file can have different types. For example, a column can be of string type, a date type or an integer type. Some of the most popular spreadsheet file formats are Comma Separated Values ( CSV ), Microsoft Excel Spreadsheet ( xls ) and Microsoft Excel Open XML Spreadsheet ( xlsx ).

Each line in CSV file represents an observation or commonly called a record. Each record may contain one or more fields which are separated by a comma.

Sometimes you may come across files where fields are not separated by using a comma but they are separated using tab. This file format is known as TSV (Tab Separated Values) file format.

The below image shows a CSV file which is opened in Notepad.


Reading the data from CSV in Python

Let us look at how to read a CSV file in Python. For loading the data you can use the “pandas” library in python.

import pandas as pd

df = pd.read_csv(“/home/Loan_Prediction/train.csv”)

Above code will load the train.csv file in DataFrame df.


3.2 XLSX files

XLSX is a Microsoft Excel Open XML file format. It also comes under the Spreadsheet file format. It is an XML-based file format created by Microsoft Excel. The XLSX format was introduced with Microsoft Office 2007.

In XLSX data is organized under the cells and columns in a sheet. Each XLSX file may contain one or more sheets. So a workbook can contain multiple sheets.

The below image shows a “xlsx” file which is opened in Microsoft Excel.

In above image, you can see that there are multiple sheets present (bottom left) in this file, which are Customers, Employees, Invoice, Order. The image shows the data of only one sheet – “Invoice”.


Reading the data from XLSX file

Let’s load the data from XLSX file and define the sheet name. For loading the data you can use the Pandas library in python.

import pandas as pd

df = pd.read_excel(“/home/Loan_Prediction/train.xlsx”, sheetname = “Invoice”)

Above code will load the sheet “Invoice” from “train.xlsx” file in DataFrame df.


3.3 ZIP files

ZIP format is an archive file format.

What is Archive File format?

In Archive file format, you create a file that contains multiple files along with metadata. An archive file format is used to collect multiple data files together into a single file. This is done for simply compressing the files to use less storage space.

There are many popular computer data archive format for creating archive files. Zip, RAR and Tar being the most popular archive file format for compressing the data.

So, a ZIP file format is a lossless compression format, which means that if you compress the multiple files using ZIP format you can fully recover the data after decompressing the ZIP file. ZIP file format uses many compression algorithms for compressing the documents. You can easily identify a ZIP file by the .zip extension.


Reading a .ZIP file in Python

You can read a zip file by importing the “zipfile” package. Below is the python code which can read the “train.csv” file that is inside the “”.

import zipfile
archive = zipfile.ZipFile('', 'r')
df ='train.csv')

Here, I have discussed one of the famous archive format and how to open it in python. I am not mentioning other archive formats. If you want to read about different archive formats and their comparisons you can refer this link.


3.4 Plain Text (txt) file format

In Plain Text file format, everything is written in plain text. Usually, this text is in unstructured form and there is no meta-data associated with it. The txt file format can easily be read by any program. But interpreting this is very difficult by a computer program.

Let’s take a simple example of a text File.

The following example shows text file data that contain text:

In my previous article, I introduced you to the basics of Apache Spark, different data representations 
(RDD / DataFrame / Dataset) and basics of operations (Transformation and Action). We even solved a machine 
learning problem from one of our past hackathons. In this article, I will continue from the place I left in 
my previous article. I will focus on manipulating RDD in PySpark by applying operations 
(Transformation and Actions).

Suppose the above text written in a file called text.txt and you want to read this so you can refer the below code.

text_file = open("text.txt", "r")
lines =


3.5 JSON file format

JavaScript Object Notation(JSON) is a text-based open standard designed for exchanging the data over web. JSON format is used for transmitting structured data over the web. The JSON file format can be easily read in any programming language because it is language-independent data format.

Let’s take an example of a JSON file

The following example shows how a typical JSON file stores information of employees.

   "Employee": [



         "Name": "Ankit",

         "Sal": "1000",



         "Name": "Faizy",

         "Sal": "2000",





Reading a JSON file

Let’s load the data from JSON file. For loading the data you can use the pandas library in python.

import pandas as pd

df = pd.read_json(“/home/kunal/Downloads/Loan_Prediction/train.json”)


3.6 XML file format

XML is also known as Extensible Markup Language. As the name suggests, it is a markup language. It has certain rules for encoding data. XML file format is a human-readable and machine-readable file format. XML is a self-descriptive language designed for sending information over the internet. XML is very similar to HTML, but has some differences. For example, XML does not use predefined tags as HTML.

Let’s take the simple example of XML File format.

The following example shows an xml document that contains the information of an employee.

<?xml version="1.0"?>


<company>Anlytics Vidhya</company>



The  “<?xml version=”1.0″?>” is  a  XML declaration at the start of the file (it is optional). In this deceleration, version specifies the XML version and encoding specifies the character encoding used in the document. <contact-info> is a tag in this document. Each XML-tag needs to be closed.


Reading XML in python

For reading the data from XML file you can import xml.etree. ElementTree library.

Let’s import an xml file called train and print its root tag.

import xml.etree.ElementTree as ET
tree = ET.parse('/home/sunilray/Desktop/2 sigma/train.xml')
root = tree.getroot()
print root.tag


3.7 HTML files

HTML stands for Hyper Text Markup Language. It is the standard markup language which is used for creating Web pages. HTML is used to describe structure of web pages using markup. HTML tags are same as XML but these are predefined. You can easily identify HTML document subsection on basis of tags such as <head> represent the heading of HTML document. <p> “paragraph” paragraph in HTML. HTML is not case sensitive.

The following example shows an HTML document.

<!DOCTYPE html>
<title>Page Title</title>
<body><h1>My First Heading</h1>
<p>My first paragraph.</p></body>

Each tag in HTML is enclosed under the angular bracket(<>).  The <!DOCTYPE html> tag defines that document is in HTML format. <html> is the root tag of this document.  The <head> element contains heading part of this document. The <title>, <body>, <h1>, <p> represent the title, body, heading and paragraph respectively in the HTML document.


Reading the HTML file

For reading the HTML file, you can use BeautifulSoup library. Please refer to this tutorial, which will guide you how to parse HTML documents. Beginner’s guide to Web Scraping in Python (using BeautifulSoup)


3.8 Image files

Image files are probably the most fascinating file format used in data science. Any computer vision application is based on image processing. So it is necessary to know different image file formats.

Usual image files are 3-Dimensional, having RGB values. But, they can also be 2-Dimensional (grayscale) or 4-Dimensional (having intensity) – an Image consisting of pixels and meta-data associated with it.

Each image consists one or more frames of pixels. And each frame is made up of two-dimensional array of pixel values. Pixel values can be of any intensity.  Meta-data associated with an image, can be an image type (.png) or pixel dimensions.

Let’s take the example of an image by loading it.

from scipy import misc
f = misc.face()
misc.imsave('face.png', f) # uses the Image module (PIL)
import matplotlib.pyplot as plt

Now, let’s check the type of this image and its shape.

type(f) , f.shape
numpy.ndarray,(768, 1024, 3)

If you want to read about image processing you can refer this article. This article will teach you image processing with an example – Basics of Image Processing in Python


3.9 Hierarchical Data Format (HDF)

In Hierarchical Data Format ( HDF ), you can store a large amount of data easily. It is not only used for storing high volumes or complex data but also used for storing small volumes or simple data.

The advantages of using HDF are as mentioned below:

  • It can be used in every size and type of system
  • It has flexible, efficient storage and fast I/O.
  • Many formats support HDF.

There are multiple HDF formats present. But, HDF5 is the latest version which is designed to address some of the limitations of the older HDF file formats. HDF5 format has some similarity with  XML. Like XML, HDF5 files are self-describing and allow users to specify complex data relationships and dependencies.

Let’s take the example of an HDF5 file format which can be identified using .h5 extension.

Read the HDF5 file

You can read the HDF file using pandas. Below is the python code can load the train.h5 data into the “t”.

t = pd.read_hdf(‘train.h5’)


3.10 PDF file format

PDF (Portable Document Format) is an incredibly useful format used for interpretation and display of text documents along with incorporated graphics. A special feature of a PDF file is that it can be secured by a password.

Here’s an example of a pdf file.

Reading a PDF file

On the other hand, reading a PDF format through a program is a complex task. Although there exists a library which do a good job in parsing PDF file, one of them is PDFMiner. To read a PDF file through PDFMiner, you have to:

  • Download PDFMiner and install it through the website
  • Extract PDF file by the following code <pdf_file>.pdf


3.11 DOCX file format

Microsoft word docx file is another file format which is regularly used by organizations for text based data. It has many characteristics, like inline addition of tables, images, hyperlinks, etc. which helps in making docx an incredibly important file format.

The advantage of a docx file over a PDF file is that a docx file is editable. You can also change a docx file to any other format.

Here’s an example of a docx file:

Reading a docx file

Similar to PDF format, python has a community contributed library to parse a docx file. It is called python-docx2txt.

Installing this library is easy through pip by:

pip install docx2txt

To read a docx file in Python use the following code:

import docx2txt
text = docx2txt.process("file.docx")


3.12 MP3 file format

MP3 file format comes under the multimedia file formats. Multimedia file formats are similar to image file formats, but they happen to be one the most complex file formats.

In multimedia file formats, you can store variety of data such as text image, graphical, video and audio data. For example, A multimedia format can allow text to be stored as Rich Text Format (RTF) data rather than ASCII data which is a plain-text format.

MP3 is one of the most common audio coding formats for digital audio. A mp3 file format uses the MPEG-1 (Moving Picture Experts Group – 1) encoding format which is a standard for lossy compression of video and audio. In lossy compression, once you have compressed the original file, you cannot recover the original data.

A mp3 file format compresses the quality of audio by filtering out the audio which can not be heard by humans. MP3 compression commonly achieves 75 to 95% reduction in size, so it saves a lot of space.

mp3 File Format Structure

A mp3 file is made up of several frames. A frame can be further divided into a header and data block. We call these sequence of frames an elementary stream.

A header in mp3 usually, identify the beginning of a valid frame and a data blocks contain the (compressed) audio information in terms of frequencies and amplitudes. If you want to know more about mp3 file structure you can refer this link.


Reading the multimedia files in python

For reading or manipulating the multimedia files in Python you can use a library called PyMedia.


3.13 MP4 file format

MP4 file format is used to store videos and movies. It contains multiple images (called frames), which play in form of a video as per a specific time period. There are two methods for interpreting a mp4 file. One is a closed entity, in which the whole video is considered as a single entity. And other is mosaic of images, where each image in the video is considered as a different entity and these images are sampled from the video.

Here’s is an example of mp4 video

Reading an mp4 file

MP4 also has a community built library for reading and editing mp4 files, called MoviePy.

You can install the library from this link. To read a mp4 video clip, in Python use the following code.

from moviepy.editor import VideoFileClip
clip = VideoFileClip(‘<video_file>.mp4’)

You can then display this in jupyter notebook as below



End Notes

In this article, I have introduced you to some of the basic file formats, which are used by data scientist on a day to day basis. There are many file formats I have not covered. Good thing is that I don’t need to cover all of them in one article.

I hope you found this article helpful. I would encourage you to explore more file formats. Good luck! If you still have any difficulty in understanding a specific data format, I’d like to interact with you in comments. If you have any more doubts or queries feel free to drop in your comments below.

Learn, compete, hack and get hired

1201904 12 Mar, 2019

Frequently Asked Questions

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

Responses From Readers


J Holmes
J Holmes 03 Mar, 2017

Nice reference - thank you.

Mantej Singh Dhanjal
Mantej Singh Dhanjal 03 Mar, 2017

Wow, simple and wonderful article. Thank you.

Jagannath Bulusu
Jagannath Bulusu 10 Mar, 2017

Very powerful aspect explained in such a simple and nice way. Thank you very much Ankit. Very very helpful. Regards, Jagan

Abhishek sharma
Abhishek sharma 16 Mar, 2017

Its help a lot to me.. Thankyou so much for this article

Tej 27 Mar, 2017

Hi Ankit, Thanks for a well researched article, It was quite useful. Is there a repository where I can access the data files (xls, xmls and others) used in this example? Thanks, Tej

Steven 08 Jan, 2018

Simple and coincse

nireesha 11 Jan, 2018

HI Ankit can you please tell me how to parse information from any file format into json format and also could u tell me is there any chance of using machine learning here

Mala 05 Feb, 2018

Good Explanation !

dave boland
dave boland 21 Feb, 2018

I liked the article. Would like to ask if it would be possible to update the article with the pros and cons of using JSON, XML, or a database manager to store data. I'm doing an application in Python and we are debating this very question - which file format is best for our needs (as well as we know them anyway). My thoughts so far are: csv: Simple, reliable for a few thousand records, but not suited to complex data relationships. JSON: Simple to use and read (human), can handle more complex relationships, good choice when csv won't due. XML: Not as simple, but not difficult. Still human readable. Can work with thousands of records. Will handle more complex data like accounting (gnuCash for example). database manager: most complicated solution, takes more work to implement, but can work with millions of records. Lets user query the data to find relationships and present them in an orderly manner. Other thoughts?

deepti 06 Apr, 2018

how do we read a dataset which is in text format