DataFrame column operations

Cleaning Data with PySpark

Mike Metzger

Data Engineering Consultant

DataFrame refresher

DataFrames:

Made up of rows & columns
Immutable
Use various transformation operations to modify data

# Return rows where name starts with "M" 
voter_df.filter(voter_df.name.like('M%'))

# Return name and position only
voters = voter_df.select('name', 'position')

Common DataFrame transformations

Filter / Where

voter_df.filter(voter_df.date > '1/1/2019') # or voter_df.where(...)

Select
```
voter_df.select(voter_df.name)
```

withColumn

voter_df.withColumn('year', voter_df.date.year)

drop
```
voter_df.drop('unused_column')
```

Filtering data

Remove nulls
Remove odd entries
Split data from combined sources

Negate with ~

voter_df.filter(voter_df['name'].isNotNull())
voter_df.filter(voter_df.date.year > 1800)
voter_df.where(voter_df['_c0'].contains('VOTE'))
voter_df.where(~ voter_df._c1.isNull())

Column string transformations

Contained in pyspark.sql.functions
```
import pyspark.sql.functions as F
```

Applied per column as transformation

voter_df.withColumn('upper', F.upper('name'))

Can create intermediary columns

voter_df.withColumn('splits', F.split('name', ' '))

Can cast to other types

voter_df.withColumn('year', voter_df['_c4'].cast(IntegerType()))

ArrayType() column functions

Various utility functions / transformations to interact with ArrayType()

.size(<column>) - returns length of arrayType() column

.getItem(<index>) - used to retrieve a specific item at index of list column.

Let's practice!

Cleaning Data with PySpark