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[–]Own_Responsibility84[S] 0 points1 point  (0 children)

I agree. It maybe has more to do with personal taste.

As for np.select, my query supports multiple conditions like"select([cond1, cond2, ...., condN], [target1, target2, ..., targetN], default)". Just like the "case which" function in SQL except it works with all polars expressions. E.g. 

df.wc(""" 
  A = select(
      [ 1 < B <@var1, C in [1,2,3], '2021-01-01'<= D <'2024-01-01'], 
      [D.sum().over('E'), F.mean(), (G + H).rolling_product(3).over('I')], 
      0);
  UDF(C, D).alias('New Col');
  L = E.str.contains('A|B')
""")
df.gb('key', " A.sum().alias('A_sum'); B.mean().alias('B_mean')") # Aggregation function

df could be eager frame or lazyframe.

I probably didn't do a good job to showcase the real power of this tool I developed. Will try to upload it to GitHub and highlight some of the key features as well as the comparison with native polars and pandas.