update samples from Release-103 as a part of 1.34.0 SDK stable release

2021-09-07 17:11:26 +00:00
1 changed files with 2 additions and 10 deletions
--- a/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/normalize.py
+++ b/how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/normalize.py
@@ -28,21 +28,13 @@ replaced_distance_vals_df = (replaced_stfor_vals_df.replace({"distance": ".00"},

 normalized_df = replaced_distance_vals_df.astype({"distance": 'float64'})

-
-def time_to_us(time_str):
-    hh, mm , ss = map(int, time_str.split(':'))
-    return (ss + 60 * (mm + 60 * hh)) * (10**6)
-
-
 temp = pd.DatetimeIndex(normalized_df["pickup_datetime"])
-normalized_df["pickup_date"] = pd.to_datetime(temp.date)
+normalized_df["pickup_date"] = temp.date
 normalized_df["pickup_time"] = temp.time
-normalized_df["pickup_time"] = normalized_df["pickup_time"].apply(lambda x: time_to_us(str(x)))

 temp = pd.DatetimeIndex(normalized_df["dropoff_datetime"])
-normalized_df["dropoff_date"] = pd.to_datetime(temp.date)
+normalized_df["dropoff_date"] = temp.date
 normalized_df["dropoff_time"] = temp.time
-normalized_df["dropoff_time"] = normalized_df["dropoff_time"].apply(lambda x: time_to_us(str(x)))

 del normalized_df["pickup_datetime"]
 del normalized_df["dropoff_datetime"]