@@ -421,12 +421,10 @@ def calculate_cluster_features(df: pd.DataFrame, cluster_id_vars_on_2nd_level: l
421421
422422 if "tst_median" in df .columns :
423423 df ["tst_median" ] = pd .to_datetime (df ["tst_median" ], errors = "coerce" , utc = True )
424- df ["tst_median_ns" ] = df ["tst_median" ].view ("int64" )
424+ df ["tst_median_ns" ] = df ["tst_median" ].astype ("int64" )
425425 else :
426426 df ["tst_median_ns" ] = pd .Series (index = df .index , dtype = "float64" )
427427
428- if "oday" in df .columns :
429- df ["oday" ] = pd .to_datetime (df ["oday" ], errors = "coerce" )
430428
431429 clust_counts = df .drop_duplicates (
432430 subset = [
@@ -450,17 +448,14 @@ def calculate_cluster_features(df: pd.DataFrame, cluster_id_vars_on_2nd_level: l
450448
451449 if "tst_median_ns" in median_vars .columns :
452450 median_vars ["tst_median" ] = pd .to_datetime (median_vars ["tst_median_ns" ], utc = True )
451+ median_vars ["tst_median" ] = median_vars ["tst_median" ].dt .tz_convert ("Europe/Helsinki" )
453452 median_vars = median_vars .drop (columns = ["tst_median_ns" ])
454453
455454 res = median_vars .merge (clust_counts , on = cluster_id_vars_on_2nd_level , how = "outer" )
456455 res = res .merge (clust_delay_feats , on = cluster_id_vars_on_2nd_level , how = "outer" )
457456
458- if "oday" in df .columns :
459- res ["oday_min" ] = df ["oday" ].min ()
460- res ["oday_max" ] = df ["oday" ].max ()
461- else :
462- res ["oday_min" ] = pd .NaT
463- res ["oday_max" ] = pd .NaT
457+ res ["oday_min" ] = df ["oday" ].min ()
458+ res ["oday_max" ] = df ["oday" ].max ()
464459
465460 return res
466461
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