

Python Data Cleansing blog aims to deliver a brief introduction to the operations of data cleansing and how we can carry out different data operations with Python Programming. For this purpose, we will use two libraries- pandas and numpy. Moreover, we will discuss different ways to cleanse the missing data. Missing data is always a problem in real-life scenarios, like machine learning and data mining face severe issues in the accuracy of their model predictions because of the poor quality of data caused by missing values.
The missing value treatment is a major point of focus to make their models more accurate and valid. We’ll be explaining the importance of data cleansing any why individuals and businesses need good data cleansing techniques. The data cleansing process is usually done all at once and it can take quite a while if the information has been piling up for years. That is why it is important for businesses and individuals to do data cleansing operations or tasks in a regular interval of time. In this blog we will be discussing; when and why is data missed? how to check the missing values, cleaning, and filling of missed data.
What is Data Cleansing?
Its a process of detecting and correcting inaccurate records from a record database, identifying inaccurate or irrelevant information of data and replacing or modifying. Data cleansing may be performed with data wrangling tools through scripting. After data cleansing, a data set will be consistent with other data sets into or system as we desired. Data cleaning is different from data validation. In data, validation data is rejected from the system at entry-level and it's performed at the time of entry, instead of batches of data.
DIFFERENCE BETWEEN DATA CLEANING AND DATA VALIDATION
Data cleaning
Data validation
Data validation helps primarily to ensure data sent to connected applications is complete, accurate and secure. That is achieved through checks and rules.
Few types of data validation include:
The Few Data cleansing approaches are..
It’s important to understand the source of missing data...
To perform data analysis need data cleaning techniques, so that our data is ready for analysis. Data scientists usually spend a very large portion of their time on this step,
Different types of data will require different types of cleaning.
Remove Unwanted observations:
Fix Structural Errors:
Filter Unwanted Outliers:
Handle Missing Data:
Missing categorical data
Missing numeric data
After completing all the cleansing steps, you'll have a robust dataset, and you can perform or play with data easily. This can really save you from a ton of headaches down the road.
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Learning Video: Python Data Cleansing| Practical Machine Learning
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