v0.4.0

New features

Now you can utilize the FeatureCollection.calculate method to compute feature based on group ids.

Specifically, 2 arguments wre atted to this FeatureCollection.calculate method:

• group_by_all: creates groups that contains all rows corresponding to the group value
  • Note that this is +/- identical as passing df.groupby(group_by_all) as data to the .calculate method -> (which is now also a valid input for the data argument)
• group_by_consecutive: creates groups that contain consecutive rows for the group value

Note: Both grouped feature extraction approaches ignore NaNs in the group_by column.

Curious? :Look at our verbose example notebook - grouping feature extraction

What's Changed

• 🎍 improving loggers as described in #66 by @jonasvdd in #73
• 🧹 some necessary maintenance by @jvdd in #80
• 🪵 log % duration + output_names for FeatureCollection by @jvdd in #83
• ✨ validate integration with antropy by @jvdd in #88
• 🎉 validate nolds integration by @jvdd in #94
• 〰️ remove isort and use ruff instead by @jvdd in #99
• ⬆️ support Python 3.11 by @jvdd in #87
• 🎉 validate pyentrp integration by @jvdd in #95
• 🐛 support functools.partial by @jvdd in #104
• 👷 build: create codeql.yml by @NielsPraet in #106
• Build/codspeed setup by @NielsPraet in #107
• ⬆️ update antropy dependency + disable py 3.7 tests by @jvdd in #108
• ⬆️ update dependencies by @jvdd in #111
• ✨ feat: Feature extraction with an identifier by @NielsPraet in #109
• ⬆️ soften pandas lock by @jvdd in #115
• 🚀 Python 3.12 support by @jvdd in #116

New Contributors

• @NielsPraet made their first contribution in #106

Full Changelog: v0.3.0...v0.4.0