Removing Blank Strings from a PySpark Dataframe

Cleaning and preprocessing data is a crucial step before it can be used for analysis or modeling. One of the common tasks in data preparation is removing empty strings from a Spark dataframe. A Spark dataframe is a distributed collection of data that is organized into rows and columns. It can be processed using parallel and distributed algorithms, making it an efficient and powerful tool for large-scale data processing and analysis. They are a fundamental part of the Apache Spark ecosystem and are widely used in big data processing and analytics. Removing empty strings ensures the data is accurate, consistent, and ready to be used for downstream tasks.

Procedure to Remove Blank Strings from a Spark Dataframe using Python

To remove blank strings from a Spark DataFrame, follow these steps:

1. To load data into a Spark dataframe, one can use the spark.read.csv() method or create an RDD and then convert it to a dataframe using the toDF() method. 
2. Once the data is loaded, the next step is to identify the columns that have empty strings by using the df.columns attribute and the df.select() method. 
3. Then, use the df.filter() method to remove rows that have empty strings in the relevant columns. For example, df.filter(df.Name != '') can be used to filter out rows that have empty strings in the "Name" column. 
4. Finally, use the df.show() method to view the resulting dataframe and confirm that it does not have any empty strings.

Example 1.

Creating dataframe for demonestration.

# import the necessary libraries
from pyspark.sql import *
from pyspark.sql.functions import *

# create a SparkSession
spark = SparkSession.builder.appName('my_app').getOrCreate()

# create the dataframe
df = spark.createDataFrame([
    ('John', 23, 'Male'),
    ('', 25, 'Female'),
    ('Jane', 28, 'Female'),
    ('', 30, 'Male')
], ['Name', 'Age', 'Gender'])

# examine the database
df.show()