What is Data Wrangling In Python?

Author: Rihan Gowda

Data wrangling is one of the most vital parts of the data science process. It is the process of acquiring, collecting, and translating raw data into a different format for faster understanding, decision-making, access, and analysis. Data wrangling is also known as data munging.

What is Data Wrangling in Python?

For Data Science and Data Analysis, data wrangling is a critical topic. Python's Pandas framework is used for data wrangling. Pandas is an open-source library designed primarily for data analysis and data science. Processes include data sorting or filtration, data grouping, and so on.

Data wrangling in python deals with the following functionalities:

Data exploration is a process that involves visualizing data representations to study, evaluate, and comprehend data.

  • Dealing with missing values: Most datasets with a large amount of data have missing values of NaN that must be managed by replacing them with the mean, mode, the column's most frequent value, or effectively removing the row with a NaN value.

  • Reshaping data: Data is changed according to requirements in this process, with new data being added or current data being modified.

  • Filtering data: Unwanted rows or columns in datasets must be eliminated or filtered from time to time.

We acquire an efficient dataset as per our requirements after dealing with the raw dataset with the above functionalities, and it can then be used for the desired purpose such as data analysis, machine learning, visualization of data, model training, and so on.

Data wrangling can take several forms, for example: Creating a single dataset for analysis from many data sources. Identifying data gaps (such as empty cells in a spreadsheet) and filling or eliminating them. Getting rid of data that isn't necessary or related to the project at hand.

Why is data wrangling so important?

Any analysis that a company conducts will be limited by the data that informs it. If data is insufficient, untrustworthy, or inaccurate, the analysis will also reduce the value of any conclusions drawn. Data wrangling aims to eliminate this risk by ensuring that data is trustworthy before it is examined and used. As a result, it is an important aspect of the analytical process.

It's crucial to remember that data wrangling, especially when done manually, may be time-consuming and resource-intensive. Many companies have policies and best practices to help employees streamline the data cleanup process, such as requiring data to include specific information or be in a particular format before being uploaded to a database.

Wondering where to learn data wrangling and other data science techniques?

Check out the IBM-certified data science course in Delhi, designed for working professionals from any domain.