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4 changes: 2 additions & 2 deletions docs/integration-spark.md
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Expand Up @@ -73,7 +73,7 @@ from whyspark import new_profiling_session
raw_df = spark.read.option("header", "true").csv("/databricks-datasets/timeseries/Fires/Fire_Department_Calls_for_Service.csv")
df = raw_df.withColumn("call_date", to_timestamp(col("Call Date"), "MM/dd/YYYY"))

profiles = new_profiling_session(newProfilingSession("profilingSession"), name="fire_station_calls", time_colum="call_date") \
profiles = df.new_profiling_session(newProfilingSession("profilingSession"), name="fire_station_calls", time_colum="call_date") \
.groupBy("City", "Priority") \
.aggProfiles()
pdf = profiles.toPandas() # you get a Pandas dataset profile of whylogs
Expand All @@ -85,4 +85,4 @@ You can then extract and analyze individual profiles:
from whylogs import DatasetProfile
prof = DatasetProfile.parse_delimited(pdf['why_profile'][0])[0]
# prof is a whylogs DatasetProfile that can be analyzed using utilities such as whylogs.viz
```
```