Data wrangling gives data a coherent shape, which makes it more usable. More than 80 percent of existing data is raw and data wrangling techniques give data scientists a way to find the most useful information so it can be mined for real-world insights.
What Is an Example of Data Wrangling?
Data wrangling examples include merging multiple data sources into a single data set, identifying gaps in data and removing outliers.
Data wrangling uses a variety of processes to transform raw data into easily understandable and ready-to-use formats, with methods varying from project to project. This flexibility allows an organization to maintain a backlog of accessible data so insights can be more easily unearthed from within a data set.
Data wrangling is also known as data cleansing, data remediation and data munging. If a company wants to standardize dates within a data set where entries vary in formatting, data wrangling tools make that possible at scale.
Data Wrangling: 4 Common Uses
- Merging multiple data sources into a single data set
- Identifying extreme outliers in data and removing them to allow for proper analysis
- Identifying gaps in data, such as empty spreadsheet cells, and removing or filling them
- Cleaning up inconsistent values and tags
What Is Data Wrangling vs. ETL?
Data wrangling is the act of extracting data and converting it to a workable format, while ETL (extract, transform, load) is a process for data integration.
While data wrangling involves extracting raw data for further processing in a more usable form, it is a less systematic process than ETL. Data wrangling and ETL have a variety of uses and should be applied in different instances.
- Data wrangling is better suited for business managers and data analysts looking to uncover insights from data, while IT professionals tend to prefer ETL pipelines to ensure data transmits easily from source to target.
- ETL has more uses when working with structured data, while data wrangling is best for raw data.
- Data wrangling has more uses than ETL when combing through large batches of data.
- ETL is good for extracting enterprise data on a regular basis.
What Are Data Wrangling Tools?
NumPy, Pandas, Dplyr, JSOnline, Excel, OpenRefine, Tabula are all examples of data wrangling tools.
Data wrangling is most often accomplished with Python through the use of tools like NumPy, Pandas, Matplotlib, Plotly and Theano, as well as in R by using Dplyr, Mafritty, JSOnline, Purrr and Splitstackshape. The most basic data wrangling software is Excel Power Query, which facilitates manual wrangling. Google DataPrep is another data wrangling tool that enables exploration, cleaning and preparation, while DataWrangler is perfect for cleaning and transformation. OpenRefine introduces programming capabilities into the mix to allow advanced data manipulation. Finally, Tabula is a tool that includes multiple functions and works with all forms of data.