The python is an object-oriented high-level programming language that is in wide use now. It is a dynamic programming language. Python provides increased productivity to its users. That is why the developers love working in python. Python language is used for Data Analysis, Machine Learning, Web Development, Software testing, prototyping, etc. The simple syntax of python also makes it a favorite of the developers. Python is versatile. This versatile feature enables it to use for different tasks like web development, machine learning, etc. Another great advantage of python is that it is beginner-friendly. So it can be helpful for entry-level developers to create projects using Python.
What is Data Cleansing?
Data cleansing is a process that is related to the data set. In a data set, there will be corrupt or incorrect, or incomplete data. Fixing these data is termed data cleaning. Data cleaning can be termed as fixing the incorrect, corrupt, duplicate, or incomplete data from a data set. In data cleaning these corrupt data sometimes be fixed or it will be removed. So in every data, the process of data cleansing can be applied. Data cleaning is entirely different from data transformation as data cleaning is removing unwanted data and the latter is transforming or converting data from one format to another. There are some steps involved in the data cleaning process. They are:
- Remove duplicate data
The first and foremost step is the removal of data. In a data set, there will be many duplications. These duplicate data must be removed from the data set. In the process of data collection, the duplication of data will happen. The removal of irrelevant data is also done in this step. When the data is collected there may be a lot of irrelevant and duplicate data.
- Fix Errors
In this step, the structural errors are removed. There will be incorrect symbols, spellings, incorrect capitalization will happen. The raw data will have many errors. The data cleaning step is where the data is formatted. So in this step, the structural errors are fixed.
- Fix unwanted data
In the data there will be unwanted observations will be there. These observations don’t fit the data. So in this step, the unwanted data are fixed or removed. There will be improper data entry in the data set. These data have to be fixed or has to be removed.
- Missing Data Handling
Data collection is a hectic and tough process. So there will be duplication or unwanted or even missing data will happen. So the data missed has to be handled properly. The missed data cannot be ignored as it may be important to the business. The missing data can be handled in different ways. It can be dropped. But it has to be done carefully. The dropped data would not affect the remaining set of data. Another way is to assume the missed data based on the other observations. This is mostly done.
- Validation of Data
In this step, after all the cleaning process checks the data is valid. Look for the data which is enough for decision-making or not. The quality of data was also checked in this process.
Data Cleaning in Python
Data Cleaning in Python is done with the help of tools like Pandas and NumPy. As we discussed earlier the data cleaning in python is also done for certain misappropriations. They are:
- Missing Data
- Irregular Data
- Unnecessary Data
- Inconsistent Data
For cleaning these irregularities or fixing them, python follows certain steps. They are:
- Importing Libraries
- Input Customer Feedback
- Find out the missing data
- Look for Duplicates
- Detect Unwanted outliers
- Check Casing
For example, Pandas is the tool commonly used by python to clean data. In pandas, the input values are given to find out the errors and it gives the output with errors and cleans them as per the user’s instructions.
Python is a high-level object-oriented versatile programming language. This language is widely used by developers. The process of data cleaning is done by python using tools like NumPy and Pandas. So in a data set, if there are any errors or duplication, or missing data, these tools help to fix or remove them.
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