The columns attribute in Spark is used to retrieve the list of column names in a DataFrame. It provides a quick and easy way to inspect the structure of the DataFrame and access the names of all columns. This is particularly useful for debugging, data exploration, and dynamic column access.

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1. Syntax

PySpark: 
df.columns
Spark SQL:
  • There is no direct equivalent in Spark SQL, but you can use DESCRIBE table_name to achieve similar results.

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2. Return Type

  • Returns a list of strings, where each string is the name of a column in the DataFrame.

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3. Key Features

  • Column Names: Provides a list of all column names in the DataFrame.
  • Efficient: It is a metadata operation and does not involve data processing.
  • Dynamic Access: Useful for dynamically accessing or manipulating columns.

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4. Examples

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Example 1: Retrieving Column Names of a DataFrame

PySpark: 
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("ColumnsExample").getOrCreate()

# Create DataFrame
data = [("Anand", 25, 3000), ("Bala", 30, 4000), ("Kavitha", 28, 3500), ("Raj", 35, 4500)]
columns = ["Name", "Age", "Salary"]

df = spark.createDataFrame(data, columns)

# Retrieve column names
print(df.columns)
Output: 
['Name', 'Age', 'Salary']

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Example 2: Using Column Names for Dynamic Column Access

PySpark: 
# Access columns dynamically
for column in df.columns:
    print(f"Column: {column}, Data Type: {df.schema[column].dataType}")
Output: 
Column: Name, Data Type: StringType
Column: Age, Data Type: IntegerType
Column: Salary, Data Type: IntegerType

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Example 3: Filtering Columns Based on Name

PySpark: 
# Filter columns that start with 'A'
filtered_columns = [col for col in df.columns if col.startswith("A")]
print(filtered_columns)
Output: 
['Age']

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Example 4: Retrieving Column Names of a DataFrame with Nested Structures

PySpark: 
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, ArrayType

# Define schema with nested structures
schema = StructType([
    StructField("Name", StringType(), True),
    StructField("Age", IntegerType(), True),
    StructField("Skills", ArrayType(StringType()), True)
])

# Create DataFrame with nested data
data = [("Anand", 25, ["Java", "Python"]), 
        ("Bala", 30, ["Scala", "Spark"]), 
        ("Kavitha", 28, ["SQL", "Hadoop"])]
df = spark.createDataFrame(data, schema)

# Retrieve column names
print(df.columns)
Output: 
['Name', 'Age', 'Skills']

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Example 5: Retrieving Column Names of a DataFrame with Structs

PySpark: 
from pyspark.sql.types import StructType, StructField, StringType, IntegerType

# Define schema with structs
schema = StructType([
    StructField("Name", StringType(), True),
    StructField("Address", StructType([
        StructField("City", StringType(), True),
        StructField("State", StringType(), True)
    ]), True)
])

# Create DataFrame with struct data
data = [("Anand", ("Chennai", "Tamil Nadu")), 
        ("Bala", ("Bangalore", "Karnataka")), 
        ("Kavitha", ("Hyderabad", "Telangana"))]
df = spark.createDataFrame(data, schema)

# Retrieve column names
print(df.columns)
Output: 
['Name', 'Address']

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Example 6: Retrieving Column Names of a DataFrame with Timestamps

PySpark: 
from pyspark.sql.types import StructType, StructField, StringType, TimestampType

# Define schema with a timestamp column
schema = StructType([
    StructField("Event", StringType(), True),
    StructField("Timestamp", TimestampType(), True)
])

# Create DataFrame
data = [("Login", "2023-10-01 10:00:00"), 
        ("Logout", "2023-10-01 12:00:00")]
df = spark.createDataFrame(data, schema)

# Retrieve column names
print(df.columns)
Output: 
['Event', 'Timestamp']

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Example 7: Retrieving Column Names of a DataFrame with Maps

PySpark: 
from pyspark.sql.types import StructType, StructField, StringType, MapType, IntegerType

# Define schema with a map column
schema = StructType([
    StructField("Name", StringType(), True),
    StructField("Skills", MapType(StringType(), IntegerType()), True)
])

# Create DataFrame
data = [("Anand", {"Java": 5, "Python": 3}), 
        ("Bala", {"Scala": 4, "Spark": 2})]
df = spark.createDataFrame(data, schema)

# Retrieve column names
print(df.columns)
Output: 
['Name', 'Skills']

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5. Common Use Cases

  • Inspecting the structure of a DataFrame after reading data.
  • Dynamically accessing or manipulating columns.
  • Filtering or selecting columns based on their names.

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6. Performance Considerations

  • Using columns is lightweight and does not involve data movement or processing.
  • It is particularly useful for debugging and understanding the structure of complex DataFrames.

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7. Key Takeaways

  1. Purpose: The columns attribute is used to retrieve the list of column names in a DataFrame.
  2. Column Names: Provides a quick way to inspect the structure of the DataFrame.
  3. In Spark SQL, similar functionality can be achieved using DESCRIBE table_name.