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The miceforest library is a Python tool for imputing missing data in a dataset using an iterative series of predictive models. In each iteration, every variable with missing values is imputed using the other variables. The iterations proceed until convergence appears to have been met.
In the example above, the correlation between A and B is brought much closer to the original data after imputing A using B and C, and then imputing B using A and C.
You can use Python dataclasses with Python match statements to create cleaner and more readable code. This approach can be particularly useful when setting conditions based on multiple attributes of a class.
Timely detection and notification of data anomalies are crucial for stakeholders to address potential issues promptly.
Kestra, an open-source orchestrator, simplifies this process by enabling you to create a workflow using a YAML file.
In the given example, a DuckDB query is used to identify anomalies, and if any are detected, an email with the anomalous rows in a CSV file is sent to relevant parties.
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