I have huge data in my dataframe now i want to try to insert the new column with 6 digit random.
i've tried with lit(randrange(99999)) is not working as expected, it's producing lesser than the 6 digit as well static value been produced to entire dataframe.
you can use built-in spark function rand() to get desired result.
spark.sql("select ceil(rand() * 1000000)").show()
To add this to new column:
Assuming df as your dataframe:
df.withColumn("random6digit",ceil(rand() * 1000000))
I have a table with a few key columns created with nvarchar(80) => unicode.
I can list the full dataset with SELECT * statement (Table1) and can confirm the values I need to filter are there.
However, I can't get any results from that table if I filter rows by using as input an alphabet char on any column.
Columns in table1 stores values in cyrilic characters.
I know it must have to do with character encoding => what I see in the result list is not what I use as input characters.
Unicode nvarchar type should resolve automatically this character type mismatch.
What do you suggest me to do in order to get results?
Thank you very much.
Paulo
I need to order a pyspark sql dataframe by ascending order of day and month. However, due to the format of the UTC stamp, this is happening:
How can I add the zero behind the single numbers and solve this? I'm programming in pyspark. This is the code I used:
data_grouped = data.groupby('month','day').agg(mean('parameter')).orderBy(["month", "day"], ascending=[1, 1])
data_grouped.show()
You can cast the ordering columns to integer:
import pyspark.sql.functions as F
data_grouped = data.groupby('month','day').agg(F.mean('parameter')) \
.orderBy(F.col("month").cast("int"), F.col("day").cast("int"))
Given a Spark dataframe that I have
val df = Seq(
("2019-01-01",100),
("2019-01-02",101),
("2019-01-03",102),
("2019-01-04",103),
("2019-01-05",102),
("2019-01-06",99),
("2019-01-07",98),
("2019-01-08",100),
("2019-01-09",47)
).toDF("day","records")
I want to add a new column to this so that I get an average value of last N records on a given day. For example, if N=3, then on a given day, that value should be average of last 3 values EXCLUDING the current record
For example, for day 2019-01-05, it would be (103+102+101)/3
How I can use efficiently use over() clause in order to do this in Spark?
PySpark solution.
Window definition should be 3 PRECEDING AND 1 FOLLOWING which translates to positions (-3,-1) with both boundaries included.
from pyspark.sql import Window
from pyspark.sql.functions import avg
w = Window.orderBy(df.day)
df_with_rsum = df.withColumn("rsum_prev_3_days",avg(df.records).over(w).rowsBetween(-3, -1))
df_with_rsum.show()
The solution assumes there is one row per date in the dataframe without missing dates in between. If not, aggregate the rows by date before applying the window function.
I have two dataframes with one column, values as
df1:
0000D3447E
0000D3447E39S
5052722161014
5052722161021
5052722161038
00000E2377
00000E2378
0000F2892E
0000F2892EDI1
8718934652999
8718934653095
8718934653002
8718934653118
8718934653019
8718934653125
8718934653132
00000E2387
df2:
0000D3447E
0000D3447E39S
5052722161014
5052722161021
5052722161038
00000E2377
00000E2378
0000F2892E
00000E2387
when I merge these two dataframes I don't get the merged values which starts with "00000E"
I used:
df1[['PartNumber']] = df2[['PartNumber']].astype(str)
To convert the column dtype to string. But, still I'm not able get the merged values.
my question is how can I get these values which start with 00000E when I merge the two dataframes.