CODEWITHSUNDEEP
pythonapache-sparkpysparkapache-spark-sql
Porter James
Porter James

Reputation: 186

Get Last Monday in Spark

I am using Spark 2.0 with the Python API.

I have a dataframe with a column of type DateType(). I would like to add a column to the dataframe containing the most recent Monday.

I can do it like this:

reg_schema = pyspark.sql.types.StructType([
    pyspark.sql.types.StructField('AccountCreationDate', pyspark.sql.types.DateType(), True),
    pyspark.sql.types.StructField('UserId', pyspark.sql.types.LongType(), True)
])
reg = spark.read.schema(reg_schema).option('header', True).csv(path_to_file)
reg = reg.withColumn('monday',
    pyspark.sql.functions.when(pyspark.sql.functions.date_format(reg.AccountCreationDate,'E') == 'Mon',
        reg.AccountCreationDate).otherwise(
    pyspark.sql.functions.when(pyspark.sql.functions.date_format(reg.AccountCreationDate,'E') == 'Tue',
        pyspark.sql.functions.date_sub(reg.AccountCreationDate, 1)).otherwise(
    pyspark.sql.functions.when(pyspark.sql.functions.date_format(reg.AccountCreationDate, 'E') == 'Wed',
        pyspark.sql.functions.date_sub(reg.AccountCreationDate, 2)).otherwise(
    pyspark.sql.functions.when(pyspark.sql.functions.date_format(reg.AccountCreationDate, 'E') == 'Thu',
        pyspark.sql.functions.date_sub(reg.AccountCreationDate, 3)).otherwise(
    pyspark.sql.functions.when(pyspark.sql.functions.date_format(reg.AccountCreationDate, 'E') == 'Fri',
        pyspark.sql.functions.date_sub(reg.AccountCreationDate, 4)).otherwise(
    pyspark.sql.functions.when(pyspark.sql.functions.date_format(reg.AccountCreationDate, 'E') == 'Sat',
        pyspark.sql.functions.date_sub(reg.AccountCreationDate, 5)).otherwise(
    pyspark.sql.functions.when(pyspark.sql.functions.date_format(reg.AccountCreationDate, 'E') == 'Sun',
        pyspark.sql.functions.date_sub(reg.AccountCreationDate, 6))
        )))))))

However, this seems like a lot of code for something that should be rather simple. Is there a more concise way of doing this?

Upvotes: 10

Views: 6976

Answers (3)

Anant100
Anant100

Reputation: 1

import pyspark.sql.functions as f

df = df.withColumn('days_from_monday', f.dayofweek(f.col('transaction_timestamp'))-2)      
df = df.withColumn('transaction_week_start_date', f.expr("date_sub(transaction_timestamp, days_from_monday)"))

Upvotes: 0

philomine
philomine

Reputation: 91

I found out that pyspark's function trunc also works.

import pyspark.sql.functions as f

df = spark.createDataFrame([
    (datetime.date(2020, 10, 27), ),
    (datetime.date(2020, 12, 21), ),
    (datetime.date(2020, 10, 13), ),
    (datetime.date(2020, 11, 11), ),
], ["date_col"])
df = df.withColumn("first_day_of_week", f.trunc("date_col", "week"))

Upvotes: 4

zero323
zero323

Reputation: 330073

You can determine next date using next_day and subtract a week. Required functions can be imported as follows:

from pyspark.sql.functions import next_day, date_sub

And as:

def previous_day(date, dayOfWeek):
    return date_sub(next_day(date, "monday"), 7)

Finally an example:

from pyspark.sql.functions import to_date

df = sc.parallelize([
    ("2016-10-26", )
]).toDF(["date"]).withColumn("date", to_date("date"))

df.withColumn("last_monday", previous_day("date", "monday"))

With result:

+----------+-----------+
|      date|last_monday|
+----------+-----------+
|2016-10-26| 2016-10-24|
+----------+-----------+

Upvotes: 13

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