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 it… Read More Check if columns exist and if not, create and fill with NaN using PySpark
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 IN… Read More Case when for statement with multiple grouped conditions converted from Pyspark
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
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 as… Read More Pyspark: Convert list to list of lists
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 from… Read More pySpark check Dataframe contains in another Dataframe
my dataframe looks like this: | accountId | income | dateOfOrder | 123 | 60000 | 56347264327_01_20200110 | 321 | 52000 | 54346262452_01_20200218 I want to take the header dateOfOrder and change it to acct_order_dt and only use the last 8 characters which are dates in yyyymmdd. I want to preserve the order of this… Read More I have a date column in a pyspark dataframe that I want to change the title of and extract only the last 8 characters from while preserving its order
I am attempting to move a process from Pandas into Pyspark, but I am a complete novice in the latter. Note: This is an EDA process so I am not too worried about having it as a loop for now, I can optimise that at a later date. Set up: import pandas as pd import… Read More Convert python pandas iterator and string concat into pyspark
I have a column which is in the "20130623" format. I am trying to convert it into dd-mm-YYYY. I have seen various post online including here. But I only got one solution as below from datetime import datetime df = df2.withColumn("col_name", datetime.utcfromtimestamp(int("col_name")).strftime(‘%d-%m-%y’)) However, it throws an error that the input should be int type, not… Read More why am I not able to convert string type column to date format in pyspark?
from pyspark.sql import SparkSession from pyspark.sql import functions as F spark = SparkSession.builder.getOrCreate() df = spark.createDataFrame([(0.0, 1.2, -1.3), (0.0, 0.0, 0.0), (-17.2, 20.3, 15.2), (23.4, 1.4, 0.0),], [‘col1’, ‘col2’, ‘col3’]) df1 = df.agg(F.avg(‘col1’)) df2 = df.agg(F.avg(‘col2’)) df3 = df.agg(F.avg(‘col3’)) If I have a dataframe, ID COL1 COL2 COL3 1 0.0 1.2 -1.3 2 0.0 0.0… Read More Pyspark calculate average of non-zero elements for each column
What is the difference between: my_df = my_df.select(col(‘age’).alias(‘age2’)) and my_df = my_df.select(col(‘age’).withColumnRenamed(‘age’, ‘age2’)) >Solution : The second expression is not going to work, you need to call withColumnRenamed() on your dataframe. I assume you mean: my_df = my_df.withColumnRenamed(‘age’, ‘age2’) And to answer your question, there is no difference.