PySpark - orderBy() and sort()

# Importing necessary libraries
from pyspark.sql import SparkSession
from pyspark.sql import functions as f

# Create a spark session
spark = SparkSession.builder.appName(
  'pyspark - example join').getOrCreate()

# Define data in  a dataframe
dataframe = [
    ("Sam", "Software Engineer", "IND", 10000),
    ("Raj", "Data Scientist", "US", 41000),
    ("Jonas", "Sales Person", "UK", 230000),
    ("Peter", "CTO", "Ireland", 50000),
    ("Hola", "Data Analyst", "Australia", 111000),
    ("Ram", "CEO", "Iran", 300000),
    ("Lekhana", "Advertising", "UK", 250000),
    ("Thanos", "Marketing", "UIND", 114000),
    ("Nick", "Data Engineer", "Ireland", 680000),
    ("Wade", "Data Engineer", "IND", 70000)
]

# Column names of dataframe
columns = ["Name", "Job", "Country", "salary"]

# Create the spark dataframe
df = spark.createDataFrame(data=dataframe, schema=columns)

# Printing the dataframe
df.show()

# Order the data by ascending order 
# of Salary
df.orderBy(['Salary'], ascending = [True]).show()

# or
# df.orderBy(f.col("Salary").asc()).show()

# or
# df.orderBy(['Salary']).show()

# Order the data by dec order 
# of Salary
df.orderBy(['Salary'], ascending = [False]).show()

# Sort the dataframe by descending order
# of 'Job' and whenever there is conflict
# in 'Job', it'll be resolved by ordering 
# based on ascending order of 'Salary'
df.orderBy(f.col("Job").desc(),f.col("Salary").asc()).show()

# or
# df.orderBy(["Job", "Salary"],ascending = [False, True]).show()

# Sort the dataframe by ascending 
# order of 'Name'
df.sort(["Name"],ascending = [True]).show()

# Sort the dataframe by scendding order of 'Name'
df.sort(["Name"],ascending = [False]).show()

# Sort the dataframe by acendding order of 'Name'
df.sort(["Name","salary"],ascending = [True]).show()