Guide to Data Wrangling: Meaning, Benefits, Tools & Workflow

With the world of data continually expanding, getting the correct data packaged for analysis is becoming increasingly important. Business users use data and information to make almost all business decisions. As a result, it is critical to make raw data used for analytics.

Read along with this article if you want to find more about data wrangling tools. Also, towards the end of this article, you can learn more about data wrangling in Python.

What Is Data Wrangling?

Have you ever wondered what data-wrangling means?

Data wrangling, also known as data cleaning, data remediation, or data munging, is a set of procedures for converting raw data into more usable representations. Depending on the data you’re using and the aim you’re trying to achieve, the exact procedures vary from project to project.

The following are some examples of data wrangling:

  1. Multiple data sources are combined into a single dataset for analysis.
  2. Identifying data gaps and either filling or eliminating them (for example, empty cells in a spreadsheet).
  3. Identifying extreme outliers in data and either explaining the discrepancies or removing them so that analysis can take place
  4. Deleting data that is either unnecessary or irrelevant to the project you are working on Identifying extreme outliers in data and either explaining the discrepancies or removing them so that analysis can take place

What Does A Typical Data Wrangling Workflow Include?

The steps involved in a typical data wrangling workflow are as follows:

1. Data Discovery

Discovery is the initial phase in the Data Wrangling process. This is a broad phrase for comprehending or familiarising yourself with your facts. Take a look at your data and consider how you’d like it to be organised to make it easier to consume and analyse.

2. Data Structuring

When raw data is acquired, it comes in various sizes and forms. It lacks a distinct structure, implying that there is no established model and is utterly chaotic. It needs to be reformed to fit into your company’s Analytical Model, and giving data a structure makes it easier to analyse.

3. Cleaning

The terms Data Wrangling and Data Cleaning are frequently used interchangeably. These are, however, two very different processes. Cleaning is merely one component of the total Data Wrangling process, albeit being a complex process in and of itself.

4. Enriching

Combining your raw data with data from other sources, such as internal systems, third-party providers, and so on, will enable you to collect even more data points and increase the accuracy of your analysis. Alternatively, you could just want to fill in the gaps in the data. For example, integrating two customer information databases contains client addresses, and the other does not. Enriching the data is an optional step that you should conduct only if your current data fails to suit your needs.

5. Validating

Validating data is an activity that addresses any concerns with the quality of your data so that the proper transformations can be applied. Data validation criteria necessitate repetitive programming operations that verify the following:

  • Consistency
  • Accuracy
  • Quality
  • Security
  • Authenticity

This is accomplished by determining whether the fields in the datasets are correct and whether the attributes are regularly distributed. The properties of the data are compared with established rules using preprogrammed scripts.

6. Publishing

All of the steps have been completed by this point, and the data is ready for analysis. All required is to publish the freshly Wrangled Data in a location where you and other stakeholders can readily access and use it. You can save the information in a new database or architecture. The end outcome of your efforts will be high-quality data that you can utilise to obtain insights, develop business reports, and more if the other procedures were performed successfully. You could even analyse the data further to construct larger, more complicated data structures like Data Warehouses. The possibilities are unlimited at this point.

Why Is Data Wrangling Important?

Data wrangling is critical since it is the only way to use unprocessed data. In real-world business situations, user data is often fragmented and comes from various sources. We sometimes save this information on many computers, in numerous spreadsheets (e.g., CRM), and on various platforms, resulting in redundancy, erroneous data, or missing data. The ideal way for creating a transparent and effective data management system is to have all data in a single area where it can be used. Another data automation tool to aid the data wrangling process is this one.

What Is Data Wrangling In Python?

The following functions are covered by data wrangling in python:

  • Data exploration: Data visualisation is used to study and comprehend data.
  • Missing values are handled in the following way: When working with huge data sets, missing values are typical, and care must be taken to replace them. Mean, mode, or just labelling them as NaN values can all be used to replace it.
  • Data reshaping: In this case, the data is either modified by addressing pre-existing data or transformed and manipulated to meet the requirements.
  • Filtering data: Unwanted rows and columns are filtered and deleted, resulting in a compressed format for the data.

What Are Data Wrangling Examples And Use Cases?

For a variety of applications, data wrangling techniques are utilised. The following are some of the most prevalent uses for data wrangling:

  1. Combining data from multiple sources into a single data set for analysis
  2. Identifying data gaps or empty cells and filling or deleting them
  3. Deleting data that is irrelevant or superfluous
  4. Identifying extreme outliers in data and either explaining or removing them to make analysis easier

Data wrangling tools are also used by businesses to:

  1. Identify and prevent business fraud
  2. Analyse the behaviour of customers
  3. Ensure that data modelling outputs are correct and consistent
  4. Encourage data security
  5. Ensure that your company is adhering to industry standards
  6. Recognise the business value of your data as soon as possible

What Are The Benefits Of Data Wrangling?

Listed below are some of the benefits of Data Wrangling:

  1. Data wrangling improves data usability by converting data into a suitable format for the end system.
  2. It enables users to create data flows quickly and easily using an easy user interface and plan and automate the data-flow process.
  3. Integrates various information types and sources (like databases, web services, files, etc.)
  4. Allow people to analyse vast amounts of data and exchange data-flow methodologies effortlessly.

What Are Data Wrangling Techniques And Tools?

Programming languages, software, and open-source data analytics platforms are just a few of the tools and strategies available to Data Wrangling specialists.

The tools you select will be determined by your requirements for:

  1. Data processing and organisation
  2. Cleaning and organising
  3. Getting information out of data

Here’s a collection of Data Wrangling tools that can help you extract useful insights from unstructured data:

  • Python and R
  • CSVKit
  • PythonPandas
  • MS Excel
  • Plotly
  • Excel Spreadsheets
  • Splitstackshape
  • JSOnline
  • OpenRefine
  • Tabula
  • Dplyr
  • Purrr

Data Wrangling VS Data Cleaning: What’s The Difference?

Despite their comparable approaches, data wrangling and data cleansing are two separate procedures. Data cleansing upfront ensures that downstream processes and analytics receive correct and consistent data, increasing customer confidence in the data.

Data cleaning aims to eliminate erroneous data from your data set. Data-wrangling focuses on modifying the data format, converting “raw” data into a usable format. Import’s WDI helps with data purification by detecting, analysing, and improving data quality. Data cleaning enhances the data’s quality and consistency, whereas data-wrangling structurally prepares the data for modelling.

Before modelling, data must be wrangled and purified to maximise the value of wisdom. Data cleansing was traditionally done before any data wrangling techniques were applied. This demonstrates that rather than being adversarial, the two processes are complementary. Investing in the right technology can help you develop trust in your data while also delivering data insights to the right people at the right time.


We hope we have helped you give you insight into wrangling data. You must have learned data wrangling steps and data wrangling techniques. The use of data wrangling in data science is unique. If data wrangling is something you’re looking for, find the top data analytics companies here.

Have a fantastic reading experience.