I have this ligne of code that should create a dataframe from list of columns that not contain a string. I tried this but it doesn't work:
val exemple = hiveObj.sql("show tables in database").select("tableName")!==="ABC".collect()
Try using the filter method:
import org.apache.spark.sql.functions._
import spark.implicits._
val exemple = hiveObj.sql("your query here").filter($"columnToFilter" =!= "ABC").show
NOTE: the inequality operator =!=is only available for Spark 2.0.0+. If you're using an older version, you must use !==. You can see the documentation here.
If you need to filter several columns you can do so:
.filter($"columnToFilter" =!= "ABC" and $"columnToFilter2" =!= "ABC")
another alternative answer to my question:
val exemple1 = hiveObj.sql("show tables in database").filter(!$"tableName".contains("ABC")).show()
Related
I have a dataframe and a list of columns like this:
import spark.implicits._
import org.apache.spark.sql.functions._
val df = spark.createDataFrame(Seq(("Java", "20000"), ("Python", "100000"))).toDF("language","users_count")
val data_columns = List("language","users_count").map(x=>col(s"$x"))
Why does this work:
df.select(data_columns:_ *).show()
But not this?
df.select($"language", data_columns:_*).show()
Gives the error:
error: no `: _*' annotation allowed here
(such annotations are only allowed in arguments to *-parameters)
And how do I get it to work so I can use _* to select all columns in a list, but I also want to specify some other columns in the select?
Thanks!
Update:
based on #chinayangyangyong answer below, this is how I solved it:
df.select( $"language" +: data_columns :_*)
It is because there is no method on Dataframe with the signature select(col: Column, cols: Column*): DataFrame, but there is one with the signature select(col: Column*): DataFrame, which is why your first example works.
Interestingly, your second example would work if you were using String to select the columns since there is a method select(col: String, cols: String*): DataFrame.
df.select(data_columns.head, data_columns.tail:_*),show()
I have a spark scala dataframe and need to filter the elements based on condition and select the count.
val filter = df.groupBy("user").count().alias("cnt")
val **count** = filter.filter(col("user") === ("subscriber").select("cnt")
The error i am facing is value select is not a member of org.apache.spark.sql.Column
Also for some reasons count is Dataset[Row]
Any thoughts to get the count in a single line?
DataSet[Row] is DataFrame
RDD[Row] is DataFrame so no need to worry.. its dataframe
see this for better understanding... Difference between DataFrame, Dataset, and RDD in Spark
Regarding select is not a member of org.apache.spark.sql.Column its purely compile error.
val filter = df.groupBy("user").count().alias("cnt")
val count = filter.filter (col("user") === ("subscriber"))
.select("cnt")
will work since you are missing ) braces which is closing brace for filter.
You are missing ")" before .select, Please check below code.
Column class don't have .select method, you have to invoke select on Dataframe.
val filter = df.groupBy("user").count().alias("cnt")
val **count** = filter.filter(col("user") === "subscriber").select("cnt")
I'm using scala.
I have a dataframe with millions of rows and multiple fields. One of the fields is a string field containing thing like this:
"Snow_KC Bingfamilies Conference_610507"
How do I reverse the contents of just this field for all the rows in the dataframe?
Thanks.
Doing a quick search on the Scaladoc, I found this reverse function which does exactly that.
import org.apache.spark.sql.{functions => sqlfun}
val df1 = ...
val df2 = df1.withColumn("columnName", sqlfun.reverse($"columnName"))
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" === $"d2.id").select(Seq(1, 2) map d2.columns map col: _*)
I have a Dataframe that I read from a CSV file with many columns like: timestamp, steps, heartrate etc.
I want to sum the values of each column, for instance the total number of steps on "steps" column.
As far as I see I want to use these kind of functions:
http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.functions$
But I can understand how to use the function sum.
When I write the following:
val df = CSV.load(args(0))
val sumSteps = df.sum("steps")
the function sum cannot be resolved.
Do I use the function sum wrongly?
Do Ι need to use first the function map? and if yes how?
A simple example would be very helpful! I started writing Scala recently.
You must first import the functions:
import org.apache.spark.sql.functions._
Then you can use them like this:
val df = CSV.load(args(0))
val sumSteps = df.agg(sum("steps")).first.get(0)
You can also cast the result if needed:
val sumSteps: Long = df.agg(sum("steps").cast("long")).first.getLong(0)
Edit:
For multiple columns (e.g. "col1", "col2", ...), you could get all aggregations at once:
val sums = df.agg(sum("col1").as("sum_col1"), sum("col2").as("sum_col2"), ...).first
Edit2:
For dynamically applying the aggregations, the following options are available:
Applying to all numeric columns at once:
df.groupBy().sum()
Applying to a list of numeric column names:
val columnNames = List("col1", "col2")
df.groupBy().sum(columnNames: _*)
Applying to a list of numeric column names with aliases and/or casts:
val cols = List("col1", "col2")
val sums = cols.map(colName => sum(colName).cast("double").as("sum_" + colName))
df.groupBy().agg(sums.head, sums.tail:_*).show()
If you want to sum all values of one column, it's more efficient to use DataFrame's internal RDD and reduce.
import sqlContext.implicits._
import org.apache.spark.sql.functions._
val df = sc.parallelize(Array(10,2,3,4)).toDF("steps")
df.select(col("steps")).rdd.map(_(0).asInstanceOf[Int]).reduce(_+_)
//res1 Int = 19
Simply apply aggregation function, Sum on your column
df.groupby('steps').sum().show()
Follow the Documentation http://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html
Check out this link also https://www.analyticsvidhya.com/blog/2016/10/spark-dataframe-and-operations/
Not sure this was around when this question was asked but:
df.describe().show("columnName")
gives mean, count, stdtev stats on a column. I think it returns on all columns if you just do .show()
Using spark sql query..just incase if it helps anyone!
import org.apache.spark.sql.SparkSession
import org.apache.spark.SparkConf
import org.apache.spark.sql.functions._
import org.apache.spark.SparkContext
import java.util.stream.Collectors
val conf = new SparkConf().setMaster("local[2]").setAppName("test")
val spark = SparkSession.builder.config(conf).getOrCreate()
val df = spark.sparkContext.parallelize(Seq(1, 2, 3, 4, 5, 6, 7)).toDF()
df.createOrReplaceTempView("steps")
val sum = spark.sql("select sum(steps) as stepsSum from steps").map(row => row.getAs("stepsSum").asInstanceOf[Long]).collect()(0)
println("steps sum = " + sum) //prints 28