| Date | Program | Description | Tags |
|---|---|---|---|
| 03/08/24 | xgboost_best_iterations.py | Dynamically select the best iterations to use on your training data with XGBoost | Data Science, Gradient Boosting, XGBoost |
| 03/15/24 | sql_with_pandas.py | Merge two Pandas dataframes with SQL | Data Engineering, Pandas, SQL |
| 03/29/24 | sql_with_polars.py | Merge two Polars dataframes with SQL | Data Engineering, Polars, SQL |
| 05/10/24 | numpy_funcs_with_polars.py | An example that converts a calculation that uses numpy functions in Pandas to Polars | Data Engineering, Numpy, Pandas, Polars |
| 06/21/24 | merge_asof_tolerance.py | How to use time tolerance to perform a complex join with merge_asof | Data Engineering, Data Science, Join, Merge, Pandas, Temporal |
| 11/01/24 | polars_selectors_api.ipynb | Examples of how to use the Polars selectors API to conveniently select columns | Data Engineering, Data Science, Polars |
| 11/15/24 | hyperparameter_autotuning.ipynb | Use Bayesian optimization from Optuna to easily perform hyperparameter autotuning | AI, Data Science, Machine Learning, Optimization |
| 02/28/25 | model_ensembling.ipynb | A simple example of how to use ensembling to combine multiple models | AI, Data Science, Machine Learning, Scikit-learn |
| 04/25/25 | timestamp_range_filtering.ipynb | How to filter timestamps in one dataframe based on valid start/end ranges in another dataframe using Polars and Pandas | Data Engineering, Data Science, Pandas, Polars |
| 07/10/25 | pivot.ipynb | Pivoting a table from long to wide, with and without aggregation, while handling duplicates in Polars and Pandas | Data Engineering, Data Science, Pandas, Polars |