Fill in the Blanks: Mastering PySpark Data Transformation Techniques


Fill in the Blanks Mastering PySpark Data Transformation TechniquesOnline version Drills to master data transformation techniques in PySpark by Good Sam 1 windowSpec = Window.partitionBy("product_id").orderBy("date").rowsBetween(-6, 0 df = df.withColumn("rolling_avg", avg(col("sales")).over(windowSpec from pyspark.sql.functions import avg, col from pyspark.sql.window import Window Scenario 5 : Calculating Rolling Averages for Time - Series Data Data Transformation Techniques : Using Window Functions Problem : You need to calculate a 7 - day rolling average of sales figures for each product in your dataset to smooth out daily fluctuations and highlight longer - term trends . Solution : from pyspark . sql . window import Window from pyspark . sql . functions import avg , col # Define a window specification windowSpec = Window . partitionBy ( " product_id " ) . orderBy ( " date " ) . rowsBetween ( - 6 , 0 ) # Calculate the rolling average using the defined window specification df = df . withColumn ( " rolling_avg " , avg ( col ( " sales " ) ) . over ( windowSpec ) ) - - - - # Define a window specification ) # Calculate the rolling average using the defined window specification ) ) Explanation : Window Specification : Window . partitionBy ( " product_id " ) groups the data by product , ensuring the average is computed per product . orderBy ( " date " ) ensures that the data within each group is considered in chronological order . rowsBetween ( - 6 , 0 ) defines the window to consider the current row and the 6 preceding rows ( 7 days in total ) , which is crucial for calculating a rolling average . Rolling Average Calculation : avg ( col ( " sales " ) ) . over ( windowSpec ) computes the average sales over the defined window for each row . This technique is particularly useful in time - series analysis where you want to analyze trends over a moving time window . 2 from pyspark.sql.functions import lower, regexp_replace cleaned_df = df.withColumn("clean_text", lower(regexp_replace(df text"], "[^a-zA-Z0-9\s Scenario 1 : Cleaning Text Data Problem : You need to clean text data in a DataFrame column by removing whitespace , converting text to lowercase , and eliminating punctuation . Solution : from pyspark . sql . functions import lower , regexp_replace cleaned_df = df . withColumn ( " clean_text " , lower ( regexp_replace ( df [ " text " ] , " [ ^a - zA - Z0 - 9 \ s ] " , " " ) ) ) [ " ] " , " " ) ) ) Explanation : lower ( ) converts all text to lowercase . regexp_replace ( ) removes all characters except alphanumeric characters and spaces . This method is used to clean up text data by standardizing its format , making it easier to process or analyze . 3 from pyspark.sql.functions import avg, coalesce avg_value = df.select(avg(df["numeric_column"]).alias("avg filled_df = df.withColumn("numeric_column", coalesce(df["numeric_column"], lit(avg_value collect()[0]["avg Scenario 3 : Handling Null Values in Numeric Data Problem : You want to replace null values in a numeric column with the column's average value . Solution : from pyspark . sql . functions import avg , coalesce # Calculate the average of the column , assuming nulls are ignored in the calculation avg_value = df . select ( avg ( df [ " numeric_column " ] ) . alias ( " avg " ) ) . collect ( ) [ 0 ] [ " avg " ] # Replace nulls with the average value filled_df = df . withColumn ( " numeric_column " , coalesce ( df [ " numeric_column " ] , lit ( avg_value ) ) ) # Calculate the average of the column , assuming nulls are ignored in the calculation " ) ) . " ] # Replace nulls with the average value ) ) ) Explanation : avg ( ) calculates the average , collect ( ) retrieves it as a scalar value , and coalesce ( ) replaces nulls with this average . This approach ensures that your dataset is free of nulls , which can skew analysis or result in errors during further data processing . 4 converted_df = df.withColumn("date", to_date(col("date_string"), "MM/dd/yyyy from pyspark.sql.functions import col, to_date Scenario 2 : Converting Data Types Problem : You have a DataFrame with a column 'date_string' in string format that you need to convert to a Date type . Solution : from pyspark . sql . functions import col , to_date converted_df = df . withColumn ( " date " , to_date ( col ( " date_string " ) , " MM / dd / yyyy " ) ) " ) ) Explanation : to_date ( ) converts a string column to a date format , specified by the date pattern " MM / dd / yyyy " . This conversion is essential for proper date handling in time - series analysis or date - based filtering . 5 unique_df = df.dropDuplicates(["column1", "column2 Scenario 4 : Removing Duplicates Based on Specific Columns Problem : You need to de - duplicate records in a DataFrame , considering only a subset of columns for identifying duplicates . Solution : unique_df = df . dropDuplicates ( [ " column1 " , " column2 " ] ) " ] ) Explanation : dropDuplicates ( ) with specific column names ensures that rows are considered duplicates only if the specified columns match . This method is useful when you want to remove duplicates based on key attributes without considering the entire row . 6 optimized_join_df = df_large.join(broadcast(df_small), df_large["key"] == df_small["key from pyspark.sql.functions import broadcast Scenario 6 : Optimizing Joins with Broadcast Variables Best Practices for Scalable Data Processing Problem : You need to join a large DataFrame with a small DataFrame efficiently in a Spark job to prevent excessive shuffling and optimize the processing time . Solution : from pyspark . sql . functions import broadcast # Assuming df_large is the large DataFrame and df_small is the small DataFrame optimized_join_df = df_large . join ( broadcast ( df_small ) , df_large [ " key " ] = = df_small [ " key " ] ) # Assuming df_large is the large DataFrame and df_small is the small DataFrame " ] ) Explanation : Broadcast Join : Using broadcast ( df_small ) is a technique where the small DataFrame is broadcasted to all the worker nodes in the cluster . This means every node has a local copy of the smaller DataFrame , which reduces the need for shuffling the data of the larger DataFrame across the network when performing the join . Performance Benefit : Broadcast joins are highly beneficial when the right - hand side DataFrame is significantly smaller than the left - hand side DataFrame . It minimizes the volume of data transfer and can drastically improve the performance of the join operation . This method is an example of leveraging Spark's broadcast capabilities to optimize join operations in large - scale data environments . It's a powerful technique for improving the efficiency and speed of data processing tasks that involve combining datasets of disparate sizes .

1

windowSpec = Window.partitionBy("product_id").orderBy("date").rowsBetween(-6, 0 df = df.withColumn("rolling_avg", avg(col("sales")).over(windowSpec from pyspark.sql.functions import avg, col from pyspark.sql.window import Window

Scenario 5 : Calculating Rolling Averages for Time - Series Data

Data Transformation Techniques : Using Window Functions

Problem : You need to calculate a 7 - day rolling average of sales figures for each product in your dataset to smooth out daily fluctuations and highlight longer - term trends .

Solution :

from pyspark . sql . window import Window
from pyspark . sql . functions import avg , col

# Define a window specification
windowSpec = Window . partitionBy ( " product_id " ) . orderBy ( " date " ) . rowsBetween ( - 6 , 0 )

# Calculate the rolling average using the defined window specification
df = df . withColumn ( " rolling_avg " , avg ( col ( " sales " ) ) . over ( windowSpec ) )

- - - -

# Define a window specification
)

# Calculate the rolling average using the defined window specification
) )

Explanation :

Window Specification : Window . partitionBy ( " product_id " ) groups the data by product , ensuring the average is computed per product . orderBy ( " date " ) ensures that the data within each group is considered in chronological order . rowsBetween ( - 6 , 0 ) defines the window to consider the current row and the 6 preceding rows ( 7 days in total ) , which is crucial for calculating a rolling average .

Rolling Average Calculation : avg ( col ( " sales " ) ) . over ( windowSpec ) computes the average sales over the defined window for each row . This technique is particularly useful in time - series analysis where you want to analyze trends over a moving time window .

2

from pyspark.sql.functions import lower, regexp_replace cleaned_df = df.withColumn("clean_text", lower(regexp_replace(df text"], "[^a-zA-Z0-9\s

Scenario 1 : Cleaning Text Data
Problem : You need to clean text data in a DataFrame column by removing whitespace , converting text to lowercase , and eliminating punctuation .

Solution :

from pyspark . sql . functions import lower , regexp_replace

cleaned_df = df . withColumn ( " clean_text " , lower ( regexp_replace ( df [ " text " ] , " [ ^a - zA - Z0 - 9 \ s ] " , " " ) ) )

[ " ] " , " " ) ) )

Explanation :

lower ( ) converts all text to lowercase .
regexp_replace ( ) removes all characters except alphanumeric characters and spaces . This method is used to clean up text data by standardizing its format , making it easier to process or analyze .

3

from pyspark.sql.functions import avg, coalesce avg_value = df.select(avg(df["numeric_column"]).alias("avg filled_df = df.withColumn("numeric_column", coalesce(df["numeric_column"], lit(avg_value collect()[0]["avg

Scenario 3 : Handling Null Values in Numeric Data
Problem : You want to replace null values in a numeric column with the column's average value .

Solution :

from pyspark . sql . functions import avg , coalesce

# Calculate the average of the column , assuming nulls are ignored in the calculation
avg_value = df . select ( avg ( df [ " numeric_column " ] ) . alias ( " avg " ) ) . collect ( ) [ 0 ] [ " avg " ]

# Replace nulls with the average value
filled_df = df . withColumn ( " numeric_column " , coalesce ( df [ " numeric_column " ] , lit ( avg_value ) ) )

# Calculate the average of the column , assuming nulls are ignored in the calculation
" ) ) . " ]

# Replace nulls with the average value
) ) )

Explanation :

avg ( ) calculates the average , collect ( ) retrieves it as a scalar value , and coalesce ( ) replaces nulls with this average . This approach ensures that your dataset is free of nulls , which can skew analysis or result in errors during further data processing .

4

converted_df = df.withColumn("date", to_date(col("date_string"), "MM/dd/yyyy from pyspark.sql.functions import col, to_date

Scenario 2 : Converting Data Types
Problem : You have a DataFrame with a column 'date_string' in string format that you need to convert to a Date type .

Solution :

from pyspark . sql . functions import col , to_date

converted_df = df . withColumn ( " date " , to_date ( col ( " date_string " ) , " MM / dd / yyyy " ) )

" ) )

Explanation :

to_date ( ) converts a string column to a date format , specified by the date pattern " MM / dd / yyyy " . This conversion is essential for proper date handling in time - series analysis or date - based filtering .

5

unique_df = df.dropDuplicates(["column1", "column2

Scenario 4 : Removing Duplicates Based on Specific Columns
Problem : You need to de - duplicate records in a DataFrame , considering only a subset of columns for identifying duplicates .

Solution :

unique_df = df . dropDuplicates ( [ " column1 " , " column2 " ] )

" ] )

Explanation :

dropDuplicates ( ) with specific column names ensures that rows are considered duplicates only if the specified columns match . This method is useful when you want to remove duplicates based on key attributes without considering the entire row .

6

optimized_join_df = df_large.join(broadcast(df_small), df_large["key"] == df_small["key from pyspark.sql.functions import broadcast

Scenario 6 : Optimizing Joins with Broadcast Variables
Best Practices for Scalable Data Processing

Problem : You need to join a large DataFrame with a small DataFrame efficiently in a Spark job to prevent excessive shuffling and optimize the processing time .

Solution :

from pyspark . sql . functions import broadcast

# Assuming df_large is the large DataFrame and df_small is the small DataFrame
optimized_join_df = df_large . join ( broadcast ( df_small ) , df_large [ " key " ] = = df_small [ " key " ] )

# Assuming df_large is the large DataFrame and df_small is the small DataFrame
" ] )

Explanation :

Broadcast Join : Using broadcast ( df_small ) is a technique where the small DataFrame is broadcasted to all the worker nodes in the cluster . This means every node has a local copy of the smaller DataFrame , which reduces the need for shuffling the data of the larger DataFrame across the network when performing the join .
Performance Benefit : Broadcast joins are highly beneficial when the right - hand side DataFrame is significantly smaller than the left - hand side DataFrame . It minimizes the volume of data transfer and can drastically improve the performance of the join operation .
This method is an example of leveraging Spark's broadcast capabilities to optimize join operations in large - scale data environments . It's a powerful technique for improving the efficiency and speed of data processing tasks that involve combining datasets of disparate sizes .

Mastering PySpark Data Transformation Techniques

Use these credentials to integrate the game into an LMS compatible with LTI 1.1 or LTI 1.3 such as Canvas, Moodle, or Blackboard. This way, the scores will be automatically saved into the platform’s gradebook.

Mastering PySpark Data Transformation Techniques