How This Workflow Works
This workflow demonstrates a straightforward process for cleaning a dataset by handling missing values, extracting important fields, and then visualizing the cleaned data to highlight key distributions and trends.
Key Features:
- Remove or address missing and incomplete data to improve reliability and analysis
- Extract and standardize relevant fields for easier analysis
- Visualize distributions and trends to support decision-making
- Present data in accessible charts for quick insights
Step-by-step:
1. Identify and Address Missing Data:
The workflow first examines the dataset to find columns and rows with significant missing information. It removes columns with >70% missing values and imputes remaining missing values in other columns, ensuring the remaining data is more complete and trustworthy.
2. Extract and Standardize Key Fields:
Next, it focuses on extracting relevant details, such as isolating the year from date fields and converting data types for consistency, making the dataset ready for visualization.
3. Visualize and Share Insights:
Finally, the cleaned and structured data is visualized using charts. These visualizations display distributions such as income categories and government accounting types, as well as trends like the number of population censuses over time, making it easier to interpret and communicate findings.