Data cleaning is an important part of data manipulation and analysis. We need to clean data with any null values, unknown characters, etc. Data cleaning is a time taking process which cannot be neglected because when we are preparing data for the machine learning model the data should be cleaned otherwise we won’t be able to generate useful insights. Or predictions.
We can apply different functions on the pandas dataframe which can help us in cleaning the data which in turn cleans the data, remove junk values, etc. But before that, we need to perform data analysis and know what all we need to do, what are the junk values, what are the datatypes of different columns in order to perform different operations for different datatypes. But what if we can automate this cleaning process? It can save a lot of time.
Datacleaner is an open-source python library which is used for automating the process of data cleaning. It is built using Pandas Dataframe and scikit-learn data preprocessing features. The contributors are actively updating it with new features. Some of the current features are:
- Dropping columns with null values
- Replacing null values with a mean(numerical data) and median(categorical data)
- Encoding non-numerical values with numerical equivalents.
In this article, we will see how datacleaner automates the process of data cleaning to save time and effort.
#data analysis #data cleaning #python
