A Short Introduction to Data Scrubbing in Python


It has been estimated that data scientists spend 70% of their time on identifying, cleansing, and integrating data and only 20% — on actually doing the analysis. It’s all due to the difficulties of locating data that is scattered among many applications. There is the need to rearrange data to make it easier to consume, as well as keep it up-to-date.

Analysis of data and transforming it into meaningful insights is vital for a company's success. Data wrangling, which is also called data munging, is the process of transforming, cleaning, and organizing raw data. After this procedure, the data can be ready for further analysis and integration. It helps to detect and remove corrupt records from a database, identify incomplete and inaccurate parts of the data and then replace, modify, or delete them.

The importance of data scrubbing

Gartner’s research shows that organizations believe that poor data quality can be responsible for an average of $15 million per year in losses. Data scrubbing is crucial in an organization; however, it can be quite time-consuming. The valuable insights this process is producing will set your company to success, as it offers many benefits for decision-making. When data is transformed into a common format, it can be used multiple times, serving various purposes.

With the rise of cloud computing, storage, and more complex analytics, the terms ‘wrangling’ and ‘munging’ evolved even further. Today both of these terms refer specifically to the initial collection, preparation, and refinement of raw data.

The difference between data munging and data cleaning

Data wrangling is the method of converting data from one format into another. It helps to make more meaningful data and use it for analysis. This process includes various important aspects such as data quality, merging of different sources, managing data, and others. Data munging is a methodology for taking data with many errors to better quality and structure, which is required for modern analytics processes.

Data cleaning is the process of finding and removing incorrect data from a data source. It includes activities such as removing typographical errors, validating and correcting values according to the existing list of entities. Data cleaning can also include tasks like harmonizing and standardizing data.

Generally, the process of data cleaning helps to clean the data set and to provide data inconsistency to different data sets. After correcting or removing corrupt, incorrect, or unnecessary data from a data set, the quality of data analysis is increased and results are more accurate. Data cleaning is often grouped with processes, such as data cleansing, data scrubbing, and data preparation, which turn your problematic data into clean data.

Both data munging and data cleaning serve as important parts of the data pre-processing in machine learning and deep learning algorithms.

Common forms of data cleansing

Due to the wide variety of verticals, use cases, user types, and systems utilizing business data, the specifics of data munging can take on different forms, including:

1. Data exploration

Data munging usually starts with data exploration. This is a general rule: munging always begins with some degree of data discovery. No matter whether a data analyst is looking at completely new data in initial data analysis (IDA) or begins the search for novel associations in existing records in exploratory data analysis (EDA), data munging will start with data exploration.

2. Data transformation

Once a sense of data contents and structure has been established, it must be transformed to new formats, which will be appropriate for downstream processing. This step involves un-nesting hierarchical JSON data, denormalizing disparate tables to access relevant information from one place, or reshaping and aggregating time series data to the dimensions.

3. Data enrichment

When data is ready for consumption, additional enrichment steps can be performed. Data enrichment includes finding other external sources of information to expand the scope of existing data.

4. Data validation

This step is considered to be one of the most important in the data munging process. Here the data is ready to be used, however certain there is still the need to perform several common-sense or sanity checks to ensure the processed data is valid before actually analyzing it. Data validation allows users to discover various problems, typos, and even the corruption caused by computational failure.

Data munging in Python

When it comes to specific tools used for data munging, professionals can choose from a great variety of options. The most basic data munging operations can be performed in tools like Excel. You can search for typos, use pivot tables, or the occasional informational visualization, etc. However, it’s not the most powerful and flexible solution. Data engineers and analysts choose a powerful programming language, as it’s much more effective compared to a generic tool like Excel.

Python is considered the most flexible programming language, which can be used for data munging. A great advantage of using Python is that it has one of the largest collections of third-party libraries. They are especially rich in data processing and analysis tools such as Pandas, NumPy, and SciPy, which help to simplify many complex data munging tasks. Pandas is one of the fastest-growing data munging libraries, but there are also many other tools in the Python ecosystem.

Compared to other programming languages, Python is easier to learn, as it has simpler, intuitive formatting, as well as a focus on legible English-language-adjacent syntax. Python offers wide applicability, rich libraries, and good online support. This makes the language very useful beyond the data processing cases, everywhere from web development to workflow automation.


Data munging is the common process for transforming unusable data into a useful one. Without some degree of data wrangling, raw data will not be ready for any kind of downstream consumption. Powerful and versatile tools, like Python, are making it easy for anyone to do data scrubbing more effectively and faster. By using powerful tools for proper data analysis, companies can stay ahead of the competition and gain numerous benefits, which can result in positive outcomes.

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