How to Use Python to Turn Unstructured Data Into Structured Data


Tons of data are being generated today, and arranging that info in a readable way is a tough nut to crack. The way in which unstructured figures are piling up makes it very tricky for us to create structured information. Therefore, one requires to have a proper skill set to execute these filters quickly and efficiently. The following steps will assist you in figuring out the right way to do it.

Define Type System


The very step towards structuring data is defining the relationship between the various types of data you are collecting. Classify the entities to figure out which of them have multiple roles and which entities may fall into a similar category. These things will assist you in figuring out the right groups of all the figures presented in front of you.

If your type of system is very complex, you would require a data science expert who has taken advanced, as structuring these types of complex figures requires expertise in the field.


This step is indeed crucial in structuring data. Annotation can be as easy as highlighting and marking the entity and then matching it to any entry or the reference you want to associate it with. You can also add co-references into a particular data if you wish to do it. After defining the type system, you would be required to sort the figures according to their length. Texts that may fall between a paragraph to around 2000 words can be separated.

This set of data is arranged in packets that need to be distributed to a network of annotations, who can work on it. Moreover, the conflict that may happen due to overlapping or mixing figures can be resolved by a set of guidelines that you need to prepare for annotations.

Design a Pre-Annotator


Upon completing the annotation process, you may encounter some common styles of annotation in your document. You may be wondering if there was any way in which you could automate these patterns and execute them in one go. Here is the instance where the pre-annotator comes into the picture. The pre-annotator is a tool to create your pattern of annotations and automate the process. The annotations made by these pre-annotators can also be modified if you encounter any issues with them. Here are a few things that you need to do to set a pre-annotator.

Create a Dictionary

Create an array of all the words that you want to associate with a particular type of entity. For instance, you can automate that whenever there is an occurrence of an umbrella, the annotator should recognize it as a personal utility. Moreover, the user also gets the option to add synonyms to the dictionary. Later, the annotator will detect and structure all the data according to the collection of words in the dictionary, along with their synonyms.

Furthermore, if you think that you would need to have two dictionaries to automate the sorting of your data, you can have multiple dictionaries.

Define a Set of Rules

In annotations, rules are very crucial, and if you have messed with the rules, your whole cluster of data will be at risk. Let’s understand this with an example. Whenever your annotator detects the name of a car, it should annotate the number next to it as a serial number. To make this operation, you need to set a rule indicating that the set of characters that will follow the name of a car should be annotated as a model number.

Employ Regular Expressions 

A regular expression is a tool that can be used to sort certain pieces of data into one group. Using regular expressions, you can set a sorting pattern for the annotator. Numerous coding sequences need to be employed in framing these regular expressions.

Coding in Python


You need to use two basic types of codes to sort your figures and convert them into structured data. These code sequences are –

Extracting Data

The following code will help you in finding a specific type of figures. This process helps in segregating unstructured data.

Determining the Frequency of Data

In order to find the exact frequency of the type of figures you want to have in your final set of data, you must use the following code in Python.

Word Tokenization 

This process is used to split large paragraphs or sentences into words. The Natural Language Toolkit (NLTK) is required to extract words from lengthy sentences. Therefore, you must have this library in your Python program to execute word tokenization. Use this code to perform tokenization:

import nltk

word_data = “(paragraph)”

nltk_tokens = nltk.word_tokenize(word_data)

print (nltk_tokens)

Tokenize Sentences

If you desire to extract sentences and not words from the given text, then you need to use a different code, which is used to tokenize sentences. To execute this, you must use the method sent_tokenize and Natural Language Toolkit (NLTK) library. The code for it is as follows:


Porter Stemming

When a stemming tool searches for a word, and it comes across with words that have the same roots, then we need to use a program that can identify the same type of words based on their root. For instance, if the control comes across words, such as hope, hopeful, and hopefulness, then it should be able to consider all of them as arising from the root word hope. You can achieve this by using a Porter Stemming Algorithm by the following method:

You can put any sentence in the variable ‘word_data’ enclosed within (“).


This is an advanced process that is required in sorting unstructured figures. If this code comes across words such as shirts, pants, and saree, it will club them together in a group named clothing. You can achieve this by:

You can put any sentence in the variable ‘word_data’ enclosed within (“).

Benefits of Structuring Data 

There are several benefits of structuring data; some of them are-

  • It helps in improving your content strategy.
  • It helps in making an effective hiring strategy.
  • After optimizing and decluttering the figures, you will discover that you will be able to enhance accessibility to various databases, making it easier for you to locate the required.
  • Make your business advertisements presentable to the audience in a much better fashion; this will improve the visibility of your business.
  • Structured data help organizations in presenting themselves to the public in a more precise manner. Your business model becomes more acceptable for the public when they know its underlying objectives.

Final Remarks

Structured data is a type of figures that can be included in the backend of a website and is essential for indexing your website in the search engine. The structure of figures will also help you get a much clearer image of your analytics.

Suppose you are thinking of pursuing a course to structure the unstructured figures in your system. In that case, you can apply for the reputed MIT data science certificate course, offered in collaboration with Great Learning.