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
To everyone saying that dropDuplicates keeps the first occurrence – this is not strictly correct. dropDuplicates keeps the ‘first occurrence’ of a sort operation – only if there is 1 partition. See below for some examples. However this is not practical for most Spark datasets. So I’m also including an example of ‘first occurrence’ drop … Read more
results.show(20, false) will not truncate. Check the source 20 is the default number of rows displayed when show() is called without any arguments.
You can pass the look up map or array etc. to the udf by using partial. check out this example. from functools import partial from pyspark.sql.functions import udf fruit_dict = {“O”: “Orange”, “A”: “Apple”, “G”: “Grape”} df = spark.createDataFrame([(“A”, 20), (“G”, 30), (“O”, 10)], [“Label”, “Count”]) def decipher_fruit(label, fruit_map): label_names = list(fruit_map.keys()) if label in … Read more
The indexin function does something similar to what you want: indexin(a, b) Returns a vector containing the highest index in b for each value in a that is a member of b. The output vector contains 0 wherever a is not a member of b. Since you want a boolean for each element in your … Read more
From my understanding, you can create a map based on columns from reference_df (I assumed this is not a very big dataframe): map_key = concat_ws(‘\0’, PrimaryLookupAttributeName, PrimaryLookupAttributeValue) map_value = OutputItemNameByValue and then use this mapping to get the corresponding values in df1: from itertools import chain from pyspark.sql.functions import collect_set, array, concat_ws, lit, col, create_map … 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
A DataFrame is defined well with a google search for “DataFrame definition”: A data frame is a table, or two-dimensional array-like structure, in which each column contains measurements on one variable, and each row contains one case. So, a DataFrame has additional metadata due to its tabular format, which allows Spark to run certain optimizations … Read more
Lets assume you want a data frame with the following schema: root |– k: string (nullable = true) |– v: integer (nullable = false) You simply define schema for a data frame and use empty RDD[Row]: import org.apache.spark.sql.types.{ StructType, StructField, StringType, IntegerType} import org.apache.spark.sql.Row val schema = StructType( StructField(“k”, StringType, true) :: StructField(“v”, IntegerType, false) … Read more
A DataFrame is defined well with a google search for “DataFrame definition”: A data frame is a table, or two-dimensional array-like structure, in which each column contains measurements on one variable, and each row contains one case. So, a DataFrame has additional metadata due to its tabular format, which allows Spark to run certain optimizations … Read more