Using the drop Method

The drop method is the most direct way to remove one or more columns from a DataFrame. It takes a single column name as a string or multiple column names as a list of strings and returns a new DataFrame without the specified columns.

Example:

codefrom pyspark.sql import SparkSession 
    
# Create a Spark session 
spark = SparkSession.builder.appName('DropColumnsExample').getOrCreate() 

# Create a DataFrame 
data = [("John", "Doe", 29, "Male", 3500), 
        ("Jane", "Doe", 34, "Female", 4500), 
        ("Sam", "Smith", 23, "Male", 3000)] 
        
columns = ["First Name", "Last Name", "Age", "Gender", "Salary"] 
df = spark.createDataFrame(data, schema=columns) 

# Drop 'Last Name' and 'Age' columns 
df_dropped = df.drop('Last Name', 'Age') 
df_dropped.show() 

Using the select Method

The select method selects a set of columns from a DataFrame, returning a new DataFrame with only the selected columns. To drop columns, use this method to select only the columns you want to keep.

Example:

code# Select 'First Name', 'Gender', and 'Salary' columns 
df_selected = df.select('First Name', 'Gender', 'Salary') 
df_selected.show() 

Using the drop Method with a List of Column Names

Instead of passing column names as separate arguments to the drop method, you can also pass a list of column names that you want to drop.

Example:

code# Drop 'Last Name' and 'Age' columns using a list of column names 
columns_to_drop = ['Last Name', 'Age'] 
df_dropped = df.drop(*columns_to_drop) 
df_dropped.show() 

Using SQL Queries

PySpark allows you to run SQL queries on DataFrames. You can use this feature to select only the columns you want, effectively dropping the others.

Example:

code# Register the DataFrame as a temporary view 
df.createOrReplaceTempView("tempView") 

# Select columns using SQL query 
df_selected = spark.sql("SELECT `First Name`, Gender, Salary FROM tempView") 
df_selected.show() 

In this example, we first register the DataFrame as a temporary view. Then, we run a SQL query to select only the columns we want to keep, effectively dropping the others.

Conclusion

Removing columns from a DataFrame is a common data manipulation task, and PySpark provides user-friendly methods for achieving this. Depending on your use case, you can use the drop method, the select method, or the drop method with a list of column names to drop multiple columns from a PySpark DataFrame.

In this blog post, we focused on various ways to drop multiple columns from a PySpark DataFrame and provided examples for each method. With this knowledge, you should be able to drop columns from a PySpark DataFrame with ease. Happy data processing!

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