Creating a Basic Data Cleaning Pipeline in Python

Now that we have discussed some of the popular libraries for automating data cleaning in Python, let’s dive into some of the techniques for using these libraries to clean data. Following is a structure of a basic data-cleaning pipeline that covers the most essential steps:

• Loading the CSV file: The CSV file is loaded as a data frame using the pandas module in Python.
• Preprocessing the Data: The data has multiple attributes and mostly these are not in a format that Machine Learning modules can understand. Hence following key preprocessing steps can be applied:
  • Removing duplicates: Duplicate rows in a dataset can cause errors or bias in analysis, so it’s important to remove them.
  • Correcting inconsistent data: Inconsistent data can arise due to errors in data entry or data integration. 
  • Handling outliers: Outliers can skew analysis, so it’s important to handle them appropriately. 
  • Formatting data: Data may need to be formatted to meet the requirements of the analysis. 
• Handling missing values: Missing values can cause problems with analysis, so it’s important to handle them appropriately. Here’s an example of how to handle missing values using the pandas library in Python:

The above steps include some of the significant and key ones, but, as per the requirement, one can add or remove functions and clean the data using the updated pipeline.

In Data Science and Machine Learning, Data Cleaning plays an essential role. Data Cleaning is the process of retaining only the crucial information from the output so that only relevant features are sent as input to the machine learning model. It is a very crucial step in data science and it helps in getting better results as all the noise is already removed. 

But, have you wondered that such a process can be time taking and coding a pipeline for every new dataset can be quite tiresome? Hence, it is a good idea to automate the whole process by creating a set pipeline for the process and then using it on every piece of data that one needs to clean. Such a pipeline can make the work easier as well as more refined. One will not have to worry about missing a step and all that is needed is to use that same pipeline again.

In this article, we will be working on creating a complete pipeline using multiple libraries, modules, and functions in Python to clean a CSV file.