Showing posts with the label PySpark

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SQL Query: 3 Methods for Calculating Cumulative SUM

SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON >= GROUP BY, t1.value ORDER BY; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or

AWS CLI PySpark a Beginner's Comprehensive Guide

AWS (Amazon Web Services) and PySpark are separate technologies, but they can be used together for certain purposes. Let me provide you with a beginner's guide for both AWS and PySpark separately. AWS (Amazon Web Services): Amazon Web Services (AWS) is a cloud computing platform that offers a wide range of services for computing power, storage, databases, machine learning, analytics, and more. 1. Create an AWS Account: Go to the AWS homepage. Click on "Create an AWS Account" and follow the instructions. 2. Set Up AWS CLI: Install the AWS Command Line Interface (AWS CLI) on your local machine. Configure it with your AWS credentials using AWS configure. 3. Explore AWS Services: AWS provides a variety of services. Familiarize yourself with core services like EC2 (Elastic Compute Cloud), S3 (Simple Storage Service), and IAM (Identity and Access Management). PySpark: PySpark is the Python API for Apache Spark, a fast and general-purpose cluster computing system. It allows you

How to Handle Spaces in PySpark Dataframe Column

In PySpark, you can employ SQL queries by importing your CSV file data to a DataFrame. However, you might face problems when dealing with spaces in column names of the DataFrame. Fortunately, there is a solution available to resolve this issue. Reading CSV file to Dataframe Here is the PySpark code for reading CSV files and writing to a DataFrame. #initiate session spark = SparkSession.builder \ .appName("PySpark Tutorial") \ .getOrCreate() #Read CSV file to df dataframe data_path = '/content/Test1.csv' df =, header=True, inferSchema=True) #Create a Temporary view for the DataFrame df2.createOrReplaceTempView("temp_table") #Read data from the temporary view spark.sql("select * from temp_table").show() Output --------+-----+---------------+---+ |Student| Year|Semester1|Semester2| | ID | | Marks | Marks | +----------+-----+---------------+ | si1 |year1|62.08| 62.4| | si1 |year2|75.94| 76.75| | si