Merge pull request #87403 from v-thepet/patch-7

LocBug: Update tutorial-opendatasets-automl.md

Author: PRMerger14

articles/open-datasets/tutorial-opendatasets-automl.md

@@ -1260,7 +1260,7 @@ weather_df.head(10)
 
 Again call `pandas.Series.dt.normalize` on the `datetime` field in the weather data so it matches the time key in `taxi_holidays_df`. Delete the unneeded columns, and filter out records where the temperature is `NaN`.
 
-Next group the weather data so that you have daily aggregated weather values. Define a dict `aggregations` to define how to aggregate each field at a daily level. For `snowDepth` and `temperature` take the mean and for `precipTime` and `precipDepth` take the daily maximum. Use the `groupby()` function along with the aggregations to group the data. Preview the data to ensure there is one record per day.
+Next group the weather data so that you have daily aggregated weather values. Define a dict named `aggregations` to define how to aggregate each field at a daily level. For `snowDepth` and `temperature` take the mean and for `precipTime` and `precipDepth` take the daily maximum. Use the `groupby()` function along with the aggregations to group the data. Preview the data to ensure there is one record per day.
 
 ```python
 weather_df["datetime"] = weather_df["datetime"].dt.normalize()