3. pyspark interview questions and answers for experienced | databricks interview question & answer

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In this video, I discussed PySpark scenario based interview questions and answers.

PySpark advanced interview questions answers?
databricks interview questions and answers?

Create dataframe:
======================================================
data1=[(1,"Steve"),(2,"David"),(3,"John"),(4,"Shree"),(5,"Helen")]
data2=[(1,"SQL",90),(1,"PySpark",100),(2,"SQL",70),(2,"PySpark",60),(3,"SQL",30),(3,"PySpark",20),(4,"SQL",50),(4,"PySpark",50),(5,"SQL",45),(5,"PySpark",45)]

schema1=["Id","Name"]
schema2=["Id","Subject","Mark"]

display(df1)
display(df2)
-----------------------------------------------------------------------------------------------------------------------
display(df_join)
---------------------------------------------------------------------------------------------------------------------
(sum('Mark')/count('*')).alias('Percentage')
)
display(df_per)
-------------------------------------------------------------------------------------------------------------------

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37. schema comparison in pyspark | How to Compare Two DataFrames in PySpark | pyspark interview:

Learn PySpark, an interface for Apache Spark in Python. PySpark is often used for large-scale data processing and machine learning.

Azure Databricks Tutorial Platlist:

Azure data factory tutorial playlist:

ADF interview question & answer:

1. pyspark introduction | pyspark tutorial for beginners | pyspark tutorial for data engineers:

2. what is dataframe in pyspark | dataframe in azure databricks | pyspark tutorial for data engineer:

3. How to read write csv file in PySpark | Databricks Tutorial | pyspark tutorial for data engineer:

4. Different types of write modes in Dataframe using PySpark | pyspark tutorial for data engineers:

5. read data from parquet file in pyspark | write data to parquet file in pyspark:

6. datatypes in PySpark | pyspark data types | pyspark tutorial for beginners:

7. how to define the schema in pyspark | structtype & structfield in pyspark | Pyspark tutorial:

8. how to read CSV file using PySpark | How to read csv file with schema option in pyspark:

9. read json file in pyspark | read nested json file in pyspark | read multiline json file:

10. add, modify, rename and drop columns in dataframe | withcolumn and withcolumnrename in pyspark:

11. filter in pyspark | how to filter dataframe using like operator | like in pyspark:

12. startswith in pyspark | endswith in pyspark | contains in pyspark | pyspark tutorial:

13. isin in pyspark and not isin in pyspark | in and not in in pyspark | pyspark tutorial:

14. select in PySpark | alias in pyspark | azure Databricks #spark #pyspark #azuredatabricks #azure

15. when in pyspark | otherwise in pyspark | alias in pyspark | case statement in pyspark:

16. Null handling in pySpark DataFrame | isNull function in pyspark | isNotNull function in pyspark:

17. fill() & fillna() functions in PySpark | how to replace null values in pyspark | Azure Databrick:

18. GroupBy function in PySpark | agg function in pyspark | aggregate function in pyspark:

19. count function in pyspark | countDistinct function in pyspark | pyspark tutorial for beginners:

20. orderBy in pyspark | sort in pyspark | difference between orderby and sort in pyspark:

21. distinct and dropduplicates in pyspark | how to remove duplicate in pyspark | pyspark tutorial:

[ssunitech]

df_final=df_per.select('*',
(when(df_per.Percentage>=70, 'Distintion')
.when((df_per.Percentage<70) & (df_per.Percentage>=60), 'First Class')
.when((df_per.Percentage<60) & (df_per.Percentage>=50), 'Second Class')
.when((df_per.Percentage<50) & (df_per.Percentage>=40), 'Third Class')
.when((df_per.Percentage<40), 'Fail')).alias('Result')
)
display(df_final)

[himangibari]

I didn't got how calculate perc by dividing count *

[satishgs]

why we have to divide count('*')?