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Returning multiple columns from a single pyspark dataframe

By : John Flynn
Date : July 29 2020, 02:00 PM
Hope this helps You can transform first to JSON string by replacing simple quotes by double quotes, then use from_json to convert it into a struct or map column.
If you know the schema of the dict you can do it like this:
code :
data = [
    (1,   2,  "{'c': 1, 'd': 2}"),
    (3,   4,  "{'c': 7, 'd': 0}"),
    (5,   6,  "{'c': 5, 'd': 4}")

df = spark.createDataFrame(data, ["a", "b", "dic"])

schema = StructType([
    StructField("c", StringType(), True),
    StructField("d", StringType(), True)

df = df.withColumn("dic", from_json(regexp_replace(col("dic"), "'", "\""), schema))

df.select("a", "b", "dic.*").show(truncate=False)

#|a  |b  |c  |d  |
#|1  |2  |1  |2  |
#|3  |4  |7  |0  |
#|5  |6  |5  |4  |
df = df.withColumn("dic", from_json(regexp_replace(col("dic"), "'", "\""), MapType(StringType(), StringType())))\
       .select("a", "b", explode("dic"))\
       .groupBy("a", "b")\

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PySpark PCA: how to convert dataframe rows from multiple columns to a single column DenseVector?

By : Igor Fisl
Date : March 29 2020, 07:55 AM
hope this fix your issue I'd like to perform principle component analysis (PCA), using PySpark (Spark 1.6.2), on numerical data that exists in a Hive table. I'm able to import the Hive table to a Spark dataframe: , You should use a VectorAssembler. If data is similar to this:
code :
from pyspark.sql import Row

data = sc.parallelize([
    Row(par001=1.1, par002=5.5, par003=8.2),
    Row(par001=0.0, par002=5.7, par003=4.2)
from pyspark.ml.feature import VectorAssembler
assembler = VectorAssembler(inputCols=data.columns, outputCol="features")
from pyspark.mllib.linalg import Vectors, VectorUDT
from pyspark.sql.functions import udf, array
  udf(Vectors.dense, VectorUDT())(*data.columns)

PySpark dataframe filter on multiple columns

By : BradBeech
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , Using Spark 2.1.1 , doing the following should solve your issue
code :
from pyspark.sql.functions import col
df.filter((!col("Name2").rlike("[0-9]")) | (col("Name2").isNotNull))

How to pivot a DataFrame in PySpark on multiple columns?

By : V Sh
Date : March 29 2020, 07:55 AM
this one helps. As mentionned in the comment, here is a solution to pivot your data :
You should concat your columns a_id and b_id under a new column c_id and group by date then pivot on c_id and use values how to see fit.

How can I sum multiple columns in a spark dataframe in pyspark?

By : user2067216
Date : March 29 2020, 07:55 AM
it should still fix some issue I've got a list of column names i want to sum , Try this:
code :
df = df.withColumn('result', sum(df[col] for col in df.columns))

Pyspark - How to concatenate columns of multiple dataframes into columns of one dataframe

By : Tony
Date : March 29 2020, 07:55 AM
it fixes the issue
You're doing it the correct way. Unfortunately without a primary key, spark is not suited for this type of operation.
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