Computation times

13:48.269 total execution time for 17 files from all galleries:

Example	Time	Mem (MB)
Multiples tables: building machine learning pipelines with DataOps (../examples/data_ops/1120_multiple_tables.py)	04:19.405	0.0
Various string encoders: a sentiment analysis example (../examples/0020_text_with_string_encoders.py)	02:50.454	0.0
AggJoiner on a credit fraud dataset (../examples/0070_join_aggregation.py)	02:00.260	0.0
Tuning DataOps with Optuna (../examples/data_ops/1131_optuna_choices.py)	01:03.567	0.0
Hyperparameter tuning with DataOps (../examples/data_ops/1130_choices.py)	00:48.758	0.0
Encoding: from a dataframe to a numerical matrix for machine learning (../examples/0010_encodings.py)	00:36.740	0.0
SquashingScaler: Robust numerical preprocessing for neural networks (../examples/0100_squashing_scaler.py)	00:23.461	0.0
Spatial join for flight data: Joining across multiple columns (../examples/0060_multiple_key_join.py)	00:22.952	0.0
Interpolation join: infer missing rows when joining two tables (../examples/0080_interpolation_join.py)	00:19.099	0.0
Subsampling for faster development (../examples/data_ops/1140_subsampling.py)	00:13.803	0.0
Fuzzy joining dirty tables with the Joiner (../examples/0040_fuzzy_joining.py)	00:12.088	0.0
Hands-On with Column Selection and Transformers (../examples/0090_apply_to_cols.py)	00:09.867	0.0
Getting Started (../examples/0000_getting_started.py)	00:09.209	0.0
Introduction to machine-learning pipelines with skrub DataOps (../examples/data_ops/1110_data_ops_intro.py)	00:06.767	0.0
Use case: developing locally and deploying to production (../examples/data_ops/1150_use_case.py)	00:06.650	0.0
Handling datetime features with the DatetimeEncoder (../examples/0030_datetime_encoder.py)	00:03.918	0.0
Deduplicating misspelled categories (../examples/0050_deduplication.py)	00:01.270	0.0