How do I locate difference between 2 dataframe columns ?
This is causing issues when I join 2 dataframes.
df1_cols = df1.columns
df2_cols = df2.columns
This will return columns for 2 dataframe in 2 list variables.
Thanks
df.columns returns a list here, so you can use any tool in python to compare with another list, i.e. df2_cols. e.g. You can use set to check the common columns in the two DataFrames
df1_cols = df1.columns
df2_cols = df2.columns
set(df1_cols).intersection(set(df2_cols)) # check common columns
set(df1_cols) - set(df2_cols) # check columns in df1 but not in df2
set(df2_cols) - set(df1_cols) # check columns in df2 but not in df1
Related
I have a Dataframe resulting from a join of two Dataframes: df1 and df2 into df3. All the columns found in df2 are also in df1, but their contents differ. I'd like to remove all the df1 columns which names are in df2.columns from the join. Would there be a way to do this without using a var?
Currently I've done this
var ret = df3
df2.columns.foreach(coln => ret = ret.drop(df2(coln)))
but what I really want is just a shortcut for
df3.drop(df1(df2.columns(1))).drop(df1(df2.columns(2)))....
without using a var.
Passing a list of columns is not an option, don't know if it's because I'm using spark 2.2
EDIT:
Important note: I don't know in advance the columns of df1 and df2
This is possible to achieve while you are performing the join itself. Please try the below code
val resultDf=df1.alias("frstdf").join(broadcast(df2).alias("scndf"), $"frstdf.col1" === $"scndf.col1", "left_outer").selectExpr("scndf.col1","scndf.col2"...)//.selectExpr("scndf.*")
This would only contain the columns from the second data frame. Hope this helps
A shortcut would be:
val ret = df2.columns.foldLeft(df3)((acc,coln) => acc.drop(df2(coln)))
I would suggest to remove the columns before the join. Alternatively, select only the columns from df3 which come from df2:
val ret = df3.select(df2.columns.map(col):_*)
I am new to Spark.
I have loaded a CSV file into a Spark DataFrame, say OriginalDF
Now I want to
1. filter out some columns from it and create a new dataframe of the originalDF
2. create a dataFrame out of the extracted columns
How can these 2 dataframes be created in spark scala?
using select, you can select what columns you want.
val df2 = OriginalDF.select($"col1",$"col2",$"col3")
using filter you should able to filter the rows.
val df3 = OriginalDF.where($"col1" < 10)
another way to filter data is using where. Both filter and where are synonyms so you can use them interchangeably.
val df3 = OriginalDF.filter($"col1" < 10)
Note select and filter returns a new dataframe as a result.
I have a pysaprk dataframe with 100 cols:
df1=[(col1,string),(col2,double),(col3,bigint),..so on]
I have another pyspark dataframe df2 with same col count and col names but different datatypes.
df2=[(col1,bigint),(col2,double),(col3,string),..so on]
how do i make the dataypes of all the cols in df2 same as ones present in the dataframe df1 for their respective cols?
It should happen iteratively and if the datatypes match then it should not change
If as you said the column names match and columns count match, then you can simply loop in the schema of df1 and cast the columns to dataTypes of df1
df2 = df2.select([F.col(c.name).cast(c.dataType) for c in df1.schema])
You can use the cast function:
from pyspark.sql import functions as f
# get schema for each DF
df1_schema=df1.dtypes
df2_schema=df2.dtypes
# iterate through cols to cast columns which differ in type
for (c1, d1), (c2,d2) in zip(df1_schema, df2_schema):
# check if datatypes are the same, otherwise cast
if d1!=d2:
df2=df2.withColumn(c2, f.col(c2).cast(d2))
I have to join two dataframes, which is very similar to the task given here Joining two DataFrames in Spark SQL and selecting columns of only one
However, I want to select only the second column from df2. In my task, I am going to use the join function for two dataframes within a reduce function for a list of dataframes. In this list of dataframes, the column names will be different. However, in each case I would want to keep the second column of df2.
I did not find anywhere how to select a dataframe's column by their numbered index. Any help is appreciated!
EDIT:
ANSWER
I figured out the solution. Here is one way to do this:
def joinDFs(df1: DataFrame, df2: DataFrame): DataFrame = {
val df2cols = df2.columns
val desiredDf2Col = df2cols(1) // the second column
val df3 = df1.as("df1").join(df2.as("df2"), $"df1.time" === $"df2.time")
.select($"df1.*",$"df2.$desiredDf2Col")
df3
}
And then I can apply this function in a reduce operation on a list of dataframes.
var listOfDFs: List[DataFrame] = List()
// Populate listOfDFs as you want here
val joinedDF = listOfDFs.reduceLeft((x, y) => {joinDFs(x, y)})
To select the second column in your dataframe you can simply do:
val df3 = df2.select(df2.columns(1))
This will first find the second column name and then select it.
If the join and select methods that you want to define in reduce function is similar to Joining two DataFrames in Spark SQL and selecting columns of only one Then you should do the following :
import org.apache.spark.sql.functions._
d1.as("d1").join(d2.as("d2"), $"d1.id" === $"d2.id").select(Seq(1) map d2.columns map col: _*)
You will have to remember that the name of the second column i.e. Seq(1) should not be same as any of the dataframes column names.
You can select multiple columns as well but remember the bold note above
import org.apache.spark.sql.functions._
d1.as("d1").join(d2.as("d2"), $"d1.id" === $"d2.id").select(Seq(1, 2) map d2.columns map col: _*)
Suppose there is one column in dataframe and there is similar schema column in another dataframe. how to check check the values consisting in the columns are same or not without joining them as there is not common attribute.
DF1
serial_nm
abc
mnc
pqr
DF2
ser_nm
hgf
mnc
uio
pqr
lok
And i want third DF3 as output
DF3
mnc
pqr
I tried this
val DF3 = DF1.filter(DF1("serial_nm") === DF2("ser_nm"))
But its not working
Please Help
Thanks..!!
I believe you can use a join. Consider using it like this:
val DF3 = DF1.join(DF2, DF1("serial_nm") === DF2("ser_nm"))
or
val DF3 = DF1.join(DF2).where(DF1("serial_nm") === DF2("ser_nm"))
Both approaches are quivalent.
Note: To avoid problems with ambiguous columns, one option is to rename them before the join:
val df2_renamed = DF2
.withColumnRenamed("mnc", "df2_mnc")
.withColumnRenamed("pqr", "df2_pqr")