Data Cleaning 101

Data Cleaning 101

Data Cleaning 101. Data quality is crucial aspect and centre of attraction for any data science project.

What is data cleaning?

Data cleaning is a process to remove, add or modify data for analyzing and other machine learning tasks. If data cleaning is necessary, it is always done before any kind of analysis or machine learning task.

Clive Humby said, “Data is the new oil.” But we know data still needs to be refined.

Why data cleaning is necessary?

Data is considered one of the major assets of a company. Misleading or inaccurate data is risky and can be a reason for the fall of a company.

It is not necessary that data available to us is useful every-time, we must perform many operations to make it useful. So, it is a good idea to remove unnecessary data and, format and modify important data so that we can use it. In some scenarios, it is also required to add information externally by processing the available data. For example, adding a language column based on some data already exist or to generate a column with average value based on some other columns’ data.


There are many steps involved in data cleaning process. These all steps are not necessary for everyone to follow or use. To perform the data cleaning, we will use python programming language with *pandas *library.

I have used python because of its expressiveness and, it is easy to learn and understand. More importantly, python is choice of many experts for machine learning tasks because person without computer science background can easily learn it. Apart from python’s benefits; pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool and it is one of the most popular data analysis and processing tools out there.

To know your data is very important before one start data cleaning process, because what cleaning process to perform, is all depends on what kind of data one has and what is the nature of that data.

python data-management technology data-science data-cleaning

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