Data Cleansing with Python and Pandas by Advance Analysis
We’ll be explaining 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 information has been piling up for years. That is why it is important for businesses and individual to do data cleansing operations or task in regular interval of time.
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 system at entry level and it's performed at 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 run down of entity.
- The validation might be strict.
- A few cleaning procedure will clean data by cross checking with your pre- approved data base or set.
- Data validation means checking of accuracy and quality of source data before using and importing.
- It's a process that ensures 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 ensure 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 source of missing data...
- Forgot to fill in field
- Data was lost while transferring manually from database
- A programming error
- Not choosing desired or mandatory field 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 new class for feature
- The algorithm says value was missing
Missing numeric data
- Flag observation with indicator variable
- Fill missing value with 0 just to meet requirement for no missing values
After completing all 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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