I have an initial PySpark dataframe from which I would like to take the MIN and MAX from a date column and then create a new PySpark dataframe with a timeseries (daily date), using the MIN and MAX from my initial dataframe.
I will use it to then join with my initial dataframe and find missing days (null in the rest of the column of my inital DF).
I tried in many different ways to build the timeseries DF, but it doesn't seem to work in PySpark. Any suggestions?
Max column's value can be extracted like this:
df.agg(F.max('col_name')).head()[0]
Date range df can be created like this:
df2 = spark.sql("SELECT sequence(to_date('2000-01-01'), to_date('2000-02-02'), interval 1 day) as date_col").withColumn('date_col', F.explode('date_col'))
And then join.
Full example:
from pyspark.sql import SparkSession, functions as F
spark = SparkSession.builder.getOrCreate()
df1 = spark.createDataFrame([(1, '2022-04-01'),(2, '2022-04-05')], ['id', 'df1_date']).select('id', F.col('df1_date').cast('date'))
df1.show()
# +---+----------+
# | id| df1_date|
# +---+----------+
# | 1|2022-04-01|
# | 2|2022-04-05|
# +---+----------+
min_date = df1.agg(F.min('df1_date')).head()[0]
max_date = df1.agg(F.max('df1_date')).head()[0]
df2 = spark.sql(f"SELECT sequence(to_date('{min_date}'), to_date('{max_date}'), interval 1 day) as df2_date").withColumn('df2_date', F.explode('df2_date'))
df3 = df2.join(df1, df1.df1_date == df2.df2_date, 'left')
df3.show()
# +----------+----+----------+
# | df2_date| id| df1_date|
# +----------+----+----------+
# |2022-04-01| 1|2022-04-01|
# |2022-04-02|null| null|
# |2022-04-03|null| null|
# |2022-04-04|null| null|
# |2022-04-05| 2|2022-04-05|
# +----------+----+----------+