If the date has passed, but it was three or fewer days ago. select("*", f.posexplode("repeat"). Although PySpark's decision tree implementation is easy to get started with, it is helpful. Just replace with _between, and use add_months instead of date_add: import as fĭf.withColumn("monthsDiff", f.months_between("maxDate", "minDate"))\ You can add a column date with all of the months in between minDate and maxDate, by following the same approach as my answer to this question. Suppose you had the following DataFrame: data = ĭf = spark.createDataFrame(data, ) I checked and there are just 3 months which were skipped from 2001 through 2018. For example, this snapshot is missing 2010-02: |2010-01 | That's why sometimes some months will be skipped. But anyway because I'm generating timestamps between quite a big date range (between 20) the timestamps shifting. Just as a note: Later I only need year and month values so I will ignore day and time. Is possible to somehow make it more precise? (year: ColumnOrName, month: ColumnOrName, day: ColumnOrName) source ¶. The problem is that I took as a month_step 31 days and its not really correct because some of the months have 30 days and even 28 days. ((max_date / month_step) 1) * month_step, Below code, add days and months to Dataframe column, when the input Date in yyyy-MM-dd Spark DateType format. Min_date, max_date = df.select(min_("date").cast("long"), max_("date").cast("long")).first() Spark SQL provides DataFrame function addmonths() to add or subtract months from a Date Column and dateadd(), datesub() to add and subtract days. df sql ('select from mytable where mydates lastday (addmonths (currentdate (),-1))') However, I would like the code to return the 'mydates' field with the following format. One of the solution is below: month_step = 31*60*60*24 The following PySpark code will return the following date format on the field 'mydates'. I have some DataFrame with "date" column and I'm trying to generate a new DataFrame with all monthly timestamps between the min and max date from the "date" column.
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