The main difference between data wrangling and data cleaning is that data wrangling is the process of converting and mapping data from one format to another format to use that data to perform analyzing, but data cleaning is the process of eliminating the incorrect data or to modify them.
Generally, data is important to small, medium as well as large scale business organizations. Therefore, each organization store data in various forms. They store data in text files, spreadsheets, in XML format, in databases and many other forms. The data from various sources are merged as required and analyzed to make predictions on the business. In overall, data wrangling and data cleaning are two methods we can perform on generating useful data.
Key Areas Covered
1. What is Data Wrangling
2. What is Data Cleaning
3. Difference Between Data Wrangling and Data Cleaning
-Comparison of Key Differences
Data Cleaning, Data Munging, Data Wrangling, Data Wrangler
What is Data Wrangling
Data wrangling is the process of converting and mapping data of one format to another format. The purpose of this process is to make data more useful for performing tasks such as analyzing. A data wrangler is a person who performs data wrangling and related tasks. Those include visualizing the data, training a statistical model and data aggregation.
In data wrangling, the data is first extracted from a data source in its raw format. Next, this data is sent to an algorithm or parsed into a predefined data structure. The final step is storing this data in a storage unit to use in future. Data scientists and business analysts analyze this data to make business decisions.
What is Data Cleaning
Data cleansing is the process of finding and removing incorrect and inaccurate records from a recordset or a data source and modifying or deleting this data. For example, some of the data that need cleansing are duplicate values, dummy values, absence of data and contradictory data. Moreover, this inconsistent data can occur due to corruption in transmission or storage.
Furthermore, it is possible to perform data cleaning by using data wrangling tools or by scripting. Data cleaning can include activities such as removing typographical errors or validating and correcting values against a known list of entities. It can also include tasks such as harmonizing and standardizing data. Overall, data cleaning helps to clean the data set and to provide data inconsistency to different data sets that were merged for various data sources.
Difference Between Data Wrangling and Data Cleaning
Data wrangling is the process of transforming and mapping data from one raw data form into another form with the intent of making it more appropriate and valuable for various tasks. In contrast, data cleaning is the process of detecting and removing corrupted or inaccurate records from a record set, table or database. So, this is the main difference between data wrangling and data cleaning.
Furthermore, data munging is another name for data wrangling, whereas data cleansing is another name for data cleaning.
Data wrangling and data cleaning are two processes that we can perform on data to obtain meaningful data. However, the main difference between data wrangling and data cleaning is that data wrangling is the process of converting and mapping data from one format to another format to use that data to perform analyzing while data cleaning is the process of eliminating the incorrect data or to modify them. In brief, it is possible to use data wrangling tools to perform data cleaning.