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Join complementary dataframes, no NAs where a value is available


By : jmpgo
Date : October 17 2020, 06:10 PM
help you fix your problem An option would be to do a full_join on 'v1' and then coalesce the 'v2' columns
code :
library(dplyr)
full_join(df1, df2, by = 'v1') %>%
    transmute(v1, v2 = coalesce(v2.x, v2.y))


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dynamically join two spark-scala dataframes on multiple columns without hardcoding join conditions


By : Almas Avicena
Date : March 29 2020, 07:55 AM
I hope this helps you . In scala you do it in similar way like in python but you need to use map and reduce functions:
code :
val sparkSession = SparkSession.builder().getOrCreate()
import sparkSession.implicits._

val df1 = List("a,b", "b,c", "c,d").toDF("col1","col2")
val df2 = List("1,2", "2,c", "3,4").toDF("col1","col2")

val columnsdf1 = df1.columns
val columnsdf2 = df2.columns

val joinExprs = columnsdf1
   .zip(columnsdf2)
   .map{case (c1, c2) => df1(c1) === df2(c2)}
   .reduce(_ && _)

val dfJoinRes = df1.join(df2,joinExprs)

Concat/join/merge multiple dataframes based on row index (number) of each individual dataframes


By : Danny
Date : March 29 2020, 07:55 AM
should help you out I want to read every nth row of a list of DataFrames and create a new DataFrames by appending all the Nth rows. , For a single output dataframe, you can concatenate and sort by index:
code :
res = pd.concat([df1, df2, df3]).sort_index().reset_index(drop=True)

     A    B    C    D
0 -0.8 -2.8 -0.3 -0.1
1  1.4 -0.7  1.5 -1.3
2  0.3 -0.5 -1.6 -0.8
3 -0.1 -0.9  0.2 -0.7
4  1.6  1.4  1.4  0.2
5  0.2 -0.5 -1.1  1.6
6  0.7 -3.3 -1.1 -0.4
7 -1.4  0.2 -1.7  0.7
8 -0.3  0.7 -1.0  1.0
res = dict(tuple(pd.concat([df1, df2, df3]).groupby(level=0)))

How to left join 2 dataframes in python,if more than one matching row in 2nd data frame after filter, join with the firs


By : july
Date : March 29 2020, 07:55 AM
I hope this helps . You can use merge_asof():
code :
pd.merge_asof(df1,
              df2,
              left_on='Enter_Time',
              right_on='Transaction_Time',
              tolerance=pd.Timedelta('10m'),
              direction='forward')
#           Enter_Time Unique_Id    Transaction_Time  Amount
#0 2018-10-01 06:29:00         A                 NaT     NaN
#1 2018-10-01 06:30:00         B 2018-10-01 06:40:00   10.25
#2 2018-10-01 06:31:00         C 2018-10-01 06:40:00   10.25
#3 2018-10-01 06:32:00         D 2018-10-01 06:40:00   10.25
#4 2018-10-01 06:33:00         E 2018-10-01 06:40:00   10.25
#5 2018-10-01 08:29:00         F 2018-10-01 08:31:00    9.65
#6 2018-10-01 08:30:00         G 2018-10-01 08:31:00    9.65
#7 2018-10-01 08:31:00         H 2018-10-01 08:31:00    9.65
#8 2018-10-01 08:32:00         I 2018-10-01 08:32:00    2.84
#9 2018-10-01 08:33:00         j                 NaT     NaN
df = pd.merge_asof(df1,
                   df2,
                   left_on='Enter_Time',
                   right_on='Transaction_Time',
                   tolerance=pd.Timedelta('10m'),
                   direction='forward')

df.loc[df.duplicated(['Transaction_Time', 'Amount']), ['Transaction_Time', 'Amount']] = (np.nan, np.nan)
df
#           Enter_Time Unique_Id    Transaction_Time  Amount
#0 2018-10-01 06:29:00         A                 NaT     NaN
#1 2018-10-01 06:30:00         B 2018-10-01 06:40:00   10.25
#2 2018-10-01 06:31:00         C                 NaT     NaN
#3 2018-10-01 06:32:00         D                 NaT     NaN
#4 2018-10-01 06:33:00         E                 NaT     NaN
#5 2018-10-01 08:29:00         F 2018-10-01 08:31:00    9.65
#6 2018-10-01 08:30:00         G                 NaT     NaN
#7 2018-10-01 08:31:00         H                 NaT     NaN
#8 2018-10-01 08:32:00         I 2018-10-01 08:32:00    2.84
#9 2018-10-01 08:33:00         j                 NaT     NaN
df = pd.merge_asof(df2,
                   df1,
                   left_on='Transaction_Time',
                   right_on='Enter_Time',
                   tolerance=pd.Timedelta('10m'))

df.loc[df.duplicated(['Transaction_Time', 'Amount']), ['Transaction_Time', 'Amount']] = (np.nan, np.nan)
#     Transaction_Time  Amount          Enter_Time Unique_Id
#0 2018-10-01 06:40:00   10.25 2018-10-01 06:33:00         E
#1 2018-10-01 07:40:00    3.96                 NaT       NaN
#2 2018-10-01 08:31:00    9.65 2018-10-01 08:31:00         H
#3 2018-10-01 08:32:00    2.84 2018-10-01 08:32:00         I

Join Dataframes dynamically using Spark Scala when JOIN columns differ


By : APaschall
Date : March 29 2020, 07:55 AM
should help you out Dynamically select multiple columns while joining different Dataframe in scala spark , With aliases have to work fine:
code :
val conditionArrays = joinKeys.split("\\|").map(c => c.split(","))
val joinExpr = conditionArrays.map { case Array(a, b) => col("a." + a) === col("b." + b) }.reduce(_ and _)
left_ds.alias("a").join(right_ds.alias("b"), joinExpr, "left_outer")

How to Merge Join Multiple DataFrames in Spark Scala Efficient Full Outer Join


By : Brian Peters
Date : March 29 2020, 07:55 AM
I hope this helps . This is an old post so I'm not sure if the OP is still tuned in. Anyway, a simple way of achieving the desired result is via cogroup(). Turn each RDD into a [K,V] RDD with the date being the key, and then use cogroup. Here's an example:
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