1. Data prep prerequisites - Splink
Splink performs optimally with cleaned and standardized data. Here is a non-exhaustive list of suggestions for data cleaning rules to enhance matching accuracy:
Data Prerequisites ¶ Splink requires that you clean your data and assign unique IDs to rows before linking. This section outlines the additional data cleaning steps needed before loading data into Splink. Unique IDs ¶ Each input dataset must have a unique ID column, which is unique within the dataset. By default, Splink assumes this column will be called unique_id , but this can be changed with the unique_id_column_name key in your Splink settings. The unique id is essential because it enables Splink to keep track each row correctly. Conformant input datasets ¶ Input datasets must be conforman
related reading
- 2. Exploratory analysis - Splinkmoj-analytical-services.github.io
- Link type - linking vs deduping - Splinkmoj-analytical-services.github.io
- Defining and customising comparisons - Splinkmoj-analytical-services.github.io
- 3. Blocking - Splinkmoj-analytical-services.github.io
- 3. Blocking - Splinkmoj-analytical-services.github.io
- Backends overview - Splinkmoj-analytical-services.github.io
- Choosing string comparators - Splinkmoj-analytical-services.github.io
- DataCamp - Cleaning Data in Python | Joannajoannaoyzl.github.io
- What are Blocking Rules? - Splinkmoj-analytical-services.github.io
- 4. Estimating model parameters - Splinkmoj-analytical-services.github.io
- Streamlit • A faster way to build and share data appsstreamlit.io
- 聚合、联接或合并数据 - Tableauhelp.tableau.com