Tag: Python

When working with financial data, efficient aggregation and analysis are essential for generating meaningful insights. A common question among developers and data analysts is whether the yFinance Python library, a popular tool for retrieving historical stock market data, allows grouping or aggregation of data directly via its API. The short answer is: no, yFinance does

When developing data pipelines or applications that store time-based records in Amazon DynamoDB, developers frequently encounter serialization errors when working with Python’s datetime objects. Understanding how to properly store temporal data in DynamoDB is essential to avoid runtime issues and to enable meaningful queries. The Problem DynamoDB, as a NoSQL database, supports a limited set

In Python, decorators are a powerful feature for applying common logic to multiple functions without duplicating code. They allow you to extend or modify the behavior of functions, methods, or classes in a declarative and clean way. What Is a Decorator? A decorator is a function that takes another function as input and returns a

Partitioning is a crucial optimization technique in big data environments like Databricks. By partitioning datasets, we can significantly improve query performance and reduce computation time. This post will walk through an exercise on partitioning data in Databricks, using a real-world dataset. Exercise: Managing Partitions in Databricks Objective Step 1: Load Data into Databricks For this

When working with text data in big data environments, measuring the similarity between strings can be essential. One of the most commonly used metrics for this is the Levenshtein distance, which calculates the number of insertions, deletions, and substitutions required to transform one string into another. In this post, we’ll demonstrate how to implement a

When working with sentiment analysis, having structured data in a PySpark DataFrame can be very useful for processing large datasets efficiently. In this post, we will create a PySpark DataFrame containing sample text opinions, which can then be analyzed using NLP techniques. Setting Up PySpark First, ensure you have PySpark installed. If not, install it

Introduction Window functions are powerful tools in SQL and PySpark that allow us to perform calculations across a subset of rows related to the current row. In this blog post, we’ll explore how to use window functions in PySpark to rank products based on their sales and filter those with sales above the category average.

When working with large datasets in PySpark, grouping data and applying aggregations is a common task. In this post, we’ll explore how to group data by a specific column and use aliases for the resulting aggregated columns to improve readability and clarity. Problem Statement Consider the following sample dataset: IdCompra Fecha IdProducto Cantidad Precio IdProveedor

When working with datetime objects in Python, you may encounter the error: This error occurs when comparing two datetime objects where one contains timezone information (offset-aware) and the other does not (offset-naive). To resolve this, you must ensure both datetime objects are either offset-aware or offset-naive before making the comparison. Making a Datetime Offset-Aware in