Databricks Flatten Nested JSON to Dataframe with PySpark - pyspark

I am trying to Convert a nested JSON to a flattened DataFrame.
I have read in the JSON as follows:
df = spark.read.json("/mnt/ins/duedil/combined.json")
The resulting dataframe looks like the following:
I have made a start on flattening the dataframe as follows
display(df.select ("companyId","countryCode"))
The above will display the following
I would like to select 'fiveYearCAGR" under the following: "financials:element:amortisationOfIntangibles:fiveYearCAGR"
Can someone let me know how to add to the select statement to retrieve the fiveYearCAGR?


Your financials is an array so if you want to extract something within the financials, you need some array transformations.
One example is to use transform.
from pyspark.sql import functions as F
df.select(
"companyId",
"countryCode",
F.transform('financials', lambda x: x['amortisationOfIntangibles']['fiveYearCAGR']).alias('fiveYearCAGR')
)
This will return the fiveYearCAGR in an array. If you need to flatten it further, you can use explode/explode_outer.

Related

Pivot by year and also get the sum of all amounts Pyspark

I have data like this
I want output like this
How do I achieve this?

One way of doing is: pivot, create an array and sum values within the array
from pyspark.sql.functions import *
s =df.groupby('id').pivot('year').agg(sum('amount'))#Pivot
(s.withColumn('x', array(*[x for x in s.columns if x!='id']))#create array
.withColumn('x', expr("reduce(x,cast(0 as bigint),(c,i)-> c+i)"))#sum
).show()
OR use pysparks inbuilt aggregate function
s =df.groupby('id').pivot('year').agg(sum('amount'))#Pivot
(s.withColumn('x', array(*[x for x in s.columns if x!='id']))#create array
.withColumn('x', expr("aggregate(x,cast(0 as bigint),(c,i)-> c+i)"))#sum
).show()

How do you use aggregated values within PySpark SQL when() clause?

I am trying to learn PySpark, and have tried to learn how to use SQL when() clauses to better categorize my data. (See here: https://sparkbyexamples.com/spark/spark-case-when-otherwise-example/) What I can't seem to get addressed is how to insert actual scalar values into the when() conditions for comparison's sake explicitly. It seems the aggregate functions return more tabular values than actual float() types.
I keep getting this error message unsupported operand type(s) for -: 'method' and 'method' When I tried running functions to aggregate another column in the original data frame I noticed the result didn't seem to be a flat scaler as much as a table (agg(select(f.stddev("Col")) gives a result like: "DataFrame[stddev_samp(TAXI_OUT): double]") Here is a sample of what I am trying to accomplish if you want to replicate, and I was wondering how you might get aggregate values like the standard deviation and mean within the when() clause so you can use that to categorize your new column:
samp = spark.createDataFrame(
[("A","A1",4,1.25),("B","B3",3,2.14),("C","C2",7,4.24),("A","A3",4,1.25),("B","B1",3,2.14),("C","C1",7,4.24)],
["Category","Sub-cat","quantity","cost"])
psMean = samp.agg({'quantity':'mean'})
psStDev = samp.agg({'quantity':'stddev'})
psCatVect = samp.withColumn('quant_category',.when(samp['quantity']<=(psMean-psStDev),'small').otherwise('not small')) ```

psMean and psStdev in your example are dataframes, you need to use collect() method to extract the scalar values
psMean = samp.agg({'quantity':'mean'}).collect()[0][0]
psStDev = samp.agg({'quantity':'stddev'}).collect()[0][0]

You could also create one variable with all stats as pandas DataFrame and reference to it later in pyspark code:
from pyspark.sql import functions as F
stats = (
samp.select(
F.mean("quantity").alias("mean"),
F.stddev("quantity").alias("std")
).toPandas()
)
(
samp.withColumn('quant_category',
F.when(
samp['quantity'] <= stats["mean"].item() - stats["std"].item(),
'small')
.otherwise('not small')
)
.toPandas()
)

Call function on Dataframe's columns has error TypeError: Column is not iterable

I am using Databricks with Spark 2.4. and i am coding Python
I have created this function to convert null to empty string
def xstr(s):
if s is None:
return ""
return str(s)
Then I have below code
from pyspark.sql.functions import *
lv_query = """
SELECT
SK_ID_Site, Designation_Site
FROM db_xxx.t_xxx
ORDER BY SK_ID_Site
limit 2"""
lvResult = spark.sql(lv_query)
a = lvResult1.select(map(xstr, col("Designation_Site")))
display(a)
I have this error : TypeError: Column is not iterable
what i need to do here is to call a function for each row that i have in my Dataframe. i would like to pass columns as parameters and have a result.

That's not how spark works. You cannot apply direct python code to a spark dataframe content.
There are already builtin functions that do the job for you.
from pyspark.sql import functions as F
a = lvResult1.select(
F.when(F.col("Designation_Site").isNull(), "").otherwise(
F.col("Designation_Site").cast("string")
)
)
In case you want some more complex functions that you cannot do with the builtin functions, you can use an UDF but it may impact a lot your performances (better check for existing builtin functions before building your own UDF).

Remove Array Column from Array pyspark

Consider I have the following data structure in a pyspark dataframe:
arr1:array
element:struct
string1:string
arr2:array
element:string
string2: string
How can I remove the arr2 from my dataframe?

You can use the drop function only. The way to select the nested columns is with .
Like window.start and window.end. You can access your arr2 as arr1.element.arr2.
df.drop(df.element.arr2)

PySpark - iterate rows of a Data Frame

I need to iterate rows of a pyspark.sql.dataframe.DataFrame.DataFrame.
I have done it in pandas in the past with the function iterrows() but I need to find something similar for pyspark without using pandas.
If I do for row in myDF: it iterates columns.DataFrame
Thanks

You can use select method to operate on your dataframe using a user defined function something like this :
columns = header.columns
my_udf = F.udf(lambda data: "do what ever you want here " , StringType())
myDF.select(*[my_udf(col(c)) for c in columns])
then inside the select you can choose what you want to do with each column .