In the Python ecosystem, package distribution plays a crucial role in sharing and reusing code efficiently. While Python’s built-in package manager, pip, allows us to install packages effortlessly, sometimes it becomes necessary to distribute our own Python packages. In such cases, wheel files prove to be a valuable asset. A wheel file is a built […]
Creating a Wheel File in Python: Simplifying Package Distribution Read More »
In this post, we will learn how to create a requirements.txt file for a python project. The requirements.txt file contains the list of all the packages needed to execute the Python project. It is very helpful, especially during the deployment. Using the requirement.txt file, we can automate the deployment of the project to a different
Create requirements.txt file in Python automatically Read More »
In today’s digital age, organizations deal with large amounts of sensitive data that includes PII data such as names, addresses, phone numbers, and email addresses. Protecting this data is critical to prevent identity theft and other types of fraud, and PII detection is a key step in the process. In this post, we will discuss
PII Data Identification using Presidio Open Source ML Library Read More »
In this post, we discussed how to fill a null value with the previous not-null value in a Spark Dataframe. We have also discussed how to extract the non-null values per group from a spark dataframe. Now, in this post, we will learn how to fill a null value with the next available not-null value
Fill null with the next not null value – Spark Dataframe Read More »
In the previous post, we discussed how to extract the non-null values per group from a spark dataframe. Now, in this post, we will learn how to fill the null values with the previous not-null value in a spark dataframe using the backfill method. To demonstrate this with the help of an example, we will
Fill null with the previous not null value – Spark Dataframe Read More »
Suppose, we need to get the first non-null value from a Dataframe from each partition. Certainly, we want to get only the first not null value from each column regardless of the rows. That means a not-null value from column A from row 5 can be stitched with another not-null value of column B from
Get the first non-null value per group Spark dataframe Read More »
In the previous post, we discussed the Try, Success, Failure exception handling method. Now, in this post, we will discuss the use of Scala’s Option, Some, None pattern and its usage. Scala is a high-level programming language combining object-oriented and functional programming in one place. It is a very powerful programming language that can be
Scala Option, Some, None – Exception and Null handling Read More »
In this post, we will discuss the Scala’s functional error handling method using Try, Success, Failure. We know that Scala is a high-level programming language that combines both object-oriented and functional programming in one place. It runs on JVM so it can be mixed seamlessly with Java. Scala’s static types helps to identify bugs at
Scala Try, Success, Failure – Functional error handling Read More »
This post will teach us how to execute a scala file in Spark without creating a jar file. We know that a scala source code file has an extension of .scala. Also, we need to create or package the source code into a jar file to execute an application written in Scala. We can create
Execute Scala file in Spark without creating a jar Read More »
Pandas is one of the most popular Python libraries used by Data Scientists/Data Engineers for data wrangling and data analysis. Also, Pandas provide DataFrames (a table-like structure that stores data in rows and columns) to deal with structured datasets. These DataFrames are very similar to Spark’s DataFrames. However, Pandas dataframes are limited to a single
