In most practical applictions, there is almost no difference. However, they are implemented by calls to different underlying functions (source) and thus are not exactly the same. We can illustrate with a small example: df = spark.createDataFrame( [(1,’a’, 0), (2,’b’,None), (None,’c’,3)], [‘col’, ‘2col’, ‘third col’] ) df.show() #+—-+—-+———+ #| col|2col|third col| #+—-+—-+———+ #| 1| a| … Read more
You can use select!: julia> df = DataFrame(A = 1:4, B = [“M”, “F”, “F”, “M”], C = 2:5) 4×3 DataFrame |——-|—|—–|—| | Row # | A | B | C | | 1 | 1 | “M” | 2 | | 2 | 2 | “F” | 3 | | 3 | 3 | … Read more
You can import the csv file into a dataframe with a predefined schema. The way you define a schema is by using the StructType and StructField objects. Assuming your data is all IntegerType data: from pyspark.sql.types import StructType, StructField, IntegerType schema = StructType([ StructField(“member_srl”, IntegerType(), True), StructField(“click_day”, IntegerType(), True), StructField(“productid”, IntegerType(), True)]) df = spark.read.csv(“user_click_seq.csv”,header=False,schema=schema) … Read more
Spark >= 2.4 If needed, schema can be determined using schema_of_json function (please note that this assumes that an arbitrary row is a valid representative of the schema). import org.apache.spark.sql.functions.{lit, schema_of_json, from_json} import collection.JavaConverters._ val schema = schema_of_json(lit(df.select($”jsonData”).as[String].first)) df.withColumn(“jsonData”, from_json($”jsonData”, schema, Map[String, String]().asJava)) Spark >= 2.1 You can use from_json function: import org.apache.spark.sql.functions.from_json import org.apache.spark.sql.types._ … Read more
A zero length array defined using only [] will lack sufficient type information. julia> typeof([]) Array{None,1} So to avoid that problem is to simply indicate the type. julia> typeof(Int64[]) Array{Int64,1} And you can apply that to your DataFrame problem julia> df = DataFrame(A = Int64[], B = Int64[]) 0x2 DataFrame julia> push!(df, [3 6]) julia> … Read more
You’ll need to use a left_anti join in this case. The left anti join is the opposite of a left semi join. It filters out data from the right table in the left table according to a given key : largeDataFrame .join(smallDataFrame, Seq(“some_identifier”),”left_anti”) .show // +—————+———-+ // |some_identifier|first_name| // +—————+———-+ // | 222| mary| // … Read more
First thing is DataFrame was evolved from SchemaRDD. Yes.. conversion between Dataframe and RDD is absolutely possible. Below are some sample code snippets. df.rdd is RDD[Row] Below are some of options to create dataframe. 1) yourrddOffrow.toDF converts to DataFrame. 2) Using createDataFrame of sql context val df = spark.createDataFrame(rddOfRow, schema) where schema can be from … Read more
It’s more or less the same: spark_df.groupBy(‘column_name’).count().orderBy(‘count’) In the groupBy you can have multiple columns delimited by a , For example groupBy(‘column_1’, ‘column_2’)
What’s wrong with result = dataframe.mul(series, axis=0) ? https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mul.html#pandas.DataFrame.mul
Actions vs Transformations Collect (Action) – Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data. spark-sql doc select(*cols) (transformation) – Projects a set of expressions and returns a new DataFrame. Parameters: … Read more