Advertisements I have lots of csv files. After using spark sql, I want to make the one csv file. For example I have news1.csv, news2.csv, news3.csv, ect in S3. I download into spark sql from s3, and createDataframe. After using spark sql, I want to upload s3 with only one csv file. At first I… Read More How can I make only one file in spark to s3?
Advertisements I wonder if anyone point me in the right direction with the following problem. In a rather large pyspark dataframe with about 50 odd columns, two of them represent say ‘make’ and ‘model’. Something like 21234234322(unique id) .. .. .. Nissan Navara .. .. .. 73647364736 .. .. .. BMW X5 .. .. ..… Read More pyspark dataframe limiting on multiple columns
Advertisements I’m trying to start pyspark using VSCode but i am getting the follow errors: Java not found and JAVA_HOME environment variable is not set. Install Java and set JAVA_HOME to point to the Java installation directory. Traceback (most recent call last): File "c:\Users\Erevos\Desktop\Pyspark\LearnSpark.py", line 5, in <module> spark = SparkSession.builder.appName("MyApp").getOrCreate() File "C:\Users\Erevos\AppData\Local\Programs\Python\Python310\lib\site-packages\pyspark\sql\session.py", line 477,… Read More RuntimeError: Java gateway process exited before sending its port number after setting JAVA_HOME
Advertisements I have this Python code written in pandas, I need to write the same in Pyspark: Source_df_write[‘default_flag1’]=Source_df_write.apply(lambda x: ‘T’ if ((x[‘A’]==1) or (x[‘crr’] in (‘sss’,’tttt’)) or (x[‘reg’]==’T’)) else ‘F’, axis=1) >Solution : You can use when and otherwise: import pyspark.sql.functions as F Source_df_write.withColumn("default_flag1", F.when( (F.col("A") == 1) | (F.col("crr").isin(["sss","tttt"])) | (F.col("reg") == ‘T’), "T"… Read More Python lambda to pyspark
Advertisements I have a pyspark dataframe with an infered schema that looks like the below. How would I define this schema in pyspark? root |– active: string (nullable = true) |– activeText: string (nullable = true) |– addOns: array (nullable = true) | |– element: struct (containsNull = true) | | |– addOnID: string (nullable… Read More How to define a pyspark schema with an array
Advertisements I have a pyspark dataframe and a separate list of column names. I want to check and see if any of the list column names are missing, and if they are, I want to create them and fill with null values. Is there a straightforward way to do this in pyspark? I can do… Read More Check if columns exist and if not, create and fill with NaN using PySpark
Advertisements I am converting a PySpark dataframe into SQL and am having a hard time converting .withColumn("portalcount", when(((F.col("tCounts") == 3) & (F.col("Type1").contains("pizza"))) & ((~(F.col("Type1").contains("singleside"))) | (~(F.col("Type1").contains("side")))), 2) .when(((F.col("tCounts") == 3) & (F.col("Type1").contains("pizza"))) & ((F.col("Type1").contains("singleside")) | (F.col("Type1").contains("side"))), 1) to CASE WHEN (tCounts = 3 AND Type1 IN ‘pizza’) AND (Type1 NOT IN ‘singleside’ OR Type1 NOT… Read More Case when for statement with multiple grouped conditions converted from Pyspark
Advertisements I wanted to convert from 03FEB23 format to yyyy-mm-dd in python how can I do it? Use the below code: from pyspark.sql.functions import * df=spark.createDataFrame([["1"]],["id"]) df.select(current_date().alias("current_date"), \ date_format("03MAR23","yyyy-MMM-dd").alias("yyyy-MMM-dd")).show() >Solution : from datetime import datetime date_str = ’03FEB23′ date = datetime.strptime(date_str, ‘%d%b%y’) formatted_date = date.strftime(‘%Y-%m-%d’) print(formatted_date) # Output: 2023-02-03
Advertisements In a pyspark dataframe, I have a column which has list values, for example: [1,2,3,4,5,6,7,8] I would like to convert the above as [[1,2,3,4] , [5,6,7,8]] given 4 for every column value. Please let me know, how can I achieve this. Thanks for your help in advance. >Solution : You can use transform function… Read More Pyspark: Convert list to list of lists
Advertisements Assume I have two Dataframes: DF1: DATA1, DATA1, DATA2, DATA2 DF2: DATA2 I want to exclude all existence of data in DF2 while keeping duplicates in DF1, what should I do? Expected result: DATA1, DATA1 >Solution : Use left anti When you join two DataFrame using Left Anti Join (leftanti), it returns only columns… Read More pySpark check Dataframe contains in another Dataframe