Zeitpunkt Nutzer Delta Tröts TNR Titel Version maxTL So 28.07.2024 00:00:15 61.966 +3 3.586.611 57,9 Fosstodon 4.2.10 500 Sa 27.07.2024 00:00:14 61.963 +5 3.583.830 57,8 Fosstodon 4.2.10 500 Fr 26.07.2024 00:01:07 61.958 +1 3.580.868 57,8 Fosstodon 4.2.10 500 Do 25.07.2024 00:00:10 61.957 +1 3.577.506 57,7 Fosstodon 4.2.10 500 Mi 24.07.2024 00:01:07 61.956 0 3.574.077 57,7 Fosstodon 4.2.10 500 Di 23.07.2024 00:00:03 61.956 -3 3.570.705 57,6 Fosstodon 4.2.10 500 Mo 22.07.2024 00:01:10 61.959 +1 3.567.825 57,6 Fosstodon 4.2.10 500 So 21.07.2024 00:01:07 61.958 +1 3.564.861 57,5 Fosstodon 4.2.10 500 Sa 20.07.2024 00:01:10 61.957 +1 3.561.604 57,5 Fosstodon 4.2.10 500 Fr 19.07.2024 13:57:34 61.956 0 3.558.474 57,4 Fosstodon 4.2.10 500
Paul McGuire (@ptmcg) · 05/2022 · Tröts: 156 · Folger: 287
So 28.07.2024 06:22
I just released v3.0.0 of #littletable, a pure Python lightweight alternative to pandas for easy CSV/JSON/Excel import/export, text search, query and pivot for smallish data sets (up to 1MM records or so). Schemaless, littletable works against a list of objects using the object attributes as columns. littletable is compatible with Python 3.9-13. Small installation footprint (single .py file), can be dropped into tight runtime environments. #python #littletable #dataanalysis #dataanalysistools
import littletable as lt catalog_data = """\ sku,description,unitofmeas,unitprice BRDSD-001,Bird seed,LB,3 BBS-001,Steel BB's,LB,5 MGNT-001,Magnet,EA,8""" catalog = lt.Table("catalog") catalog.create_index("sku", unique=True) catalog.csv_import(catalog_data, transforms={'unitprice': int}) catalog.present()
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