Data Cleansing with Python and Pandas by Advance Analysis
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 include removing typographical mistakes and redressing values against a rundown of an entity.
- The validation might be strict.
- A few cleaning procedures will clean data by cross-checking with your pre-approved database or set.
- Data validation means checking of accuracy and quality of source data before using and importing.
- It's a process that ensures the delivery of clean and clear data to the programs or applications and services.
- We check for integrity and data validity which is being inputted to different software and component.
- It ensures that data compliance with requirement & quality benchmark.
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:
- Code validation
- Data type validation
- Data range validation
- Constraint validation
- Structured validation
The Few Data cleansing approaches are..
- Data analysis
- Transformation workflow and mapping rules
- Backflow of cleaned data
It’s important to understand the source of missing data...
- Forgot to fill in the field
- Data was lost while transferring manually from a database
- A programming error
- Not choosing desired or mandatory fields tied to their beliefs.
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:
- This includes duplicate or irrelevant observations.
Fix Structural Errors:
- They arise during measurement, data transfer, or other types of "poor housekeeping."
Filter Unwanted Outliers:
- They can cause problems with certain types of models i.e. linear regression models and decision tree models.
Handle Missing Data:
- It's a deceptively tricky issue. You cannot simply ignore missing values in your dataset. Need to handle them with practical reason that algorithms do not accept missing values.
Missing categorical data
- You can add a new class for feature
- The algorithm says the value was missing
Missing numeric data
- Flag observation with an indicator variable
- Fill missing value with 0 just to meet the requirement for no missing values
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.
Thanks for reading...
Learning Video: Python Data Cleansing| Practical Machine Learning
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