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Power BI - Custom Filtering in Power Query
Academy Membership Power BIBusiness Intelligence

Power BI - Custom Filtering in Power Query

Introduction Power BI offers with the Power Query Editor a powerful tool for cleaning and transforming data. One important part of data preparation is filtering your data. Filtering enables you to sort out irrelevant data and to reduce the amount of data. One important type of filtering is custom filtering....

PySpark - Change Column Types of a DataFrame

PySpark - Change Column Types of a DataFrame

Introduction Data manipulation tasks often involve converting column data types to ensure consistency and accuracy in analysis. In this tutorial, we will show you how to change column types of a PySpark DataFrame. In order to do this, we will use the cast() function of PySpark. Import Libraries First, we...

PySpark - Window Functions
Academy Membership PythonPySpark

PySpark - Window Functions

Introduction Window functions in PySpark are a powerful feature for data manipulation and analysis. They allow you to perform complex calculations on subsets of data within a DataFrame, without the need for expensive joins or subqueries. In this tutorial, we will show you how to use window functions in PySpark....

Power BI - SWITCH function in DAX

Power BI - SWITCH function in DAX

Introduction Power BI comes with the powerful formula language Data Analysis Expression (DAX) which allows the implementation of custom calculations. There are numerous operators and functions available in DAX. One essential DAX function is the SWITCH function. The SWITCH function provides an elegant way to handle multiple conditions and is...

Performance Metrics for Classification in Machine Learning: Understanding Accuracy, Precision, Recall and F1 Score
Academy Membership Machine Learning

Performance Metrics for Classification in Machine Learning: Understanding Accuracy, Precision, Recall and F1 Score

Introduction In Machine Learning, one essential step is evaluating the performance of a model. For classification models, the Confusion Matrix serves as a fundamental instrument for evaluating the performance. The Confusion Matrix provides a visualization of the results of a model. Based on the information from the Confusion Matrix, some...

Pandas - Add an ID Column to a DataFrame

Pandas - Add an ID Column to a DataFrame

Introduction One common task when working with large datasets is the need to generate unique identifiers for each record. In this tutorial, we will explore how to easily add an ID column to a Pandas DataFrame. In order to do this, we use the index attribute of a Pandas DataFrame....

How to containerize a FastAPI Application with Docker
Academy Membership FastAPIDocker

How to containerize a FastAPI Application with Docker

Introduction FastAPI, a high-performance Python web framework, coupled with Docker, a powerful containerization tool, can significantly boost the efficiency of your development workflow. In this blog post, we'll walk you through the process of setting up a FastAPI project using a Dockerfile, providing a flexible and scalable solution...

Confusion Matrix in Machine Learning: A Hands-On Explanation
Academy Membership Machine Learning

Confusion Matrix in Machine Learning: A Hands-On Explanation

Introduction In Machine Learning, one essential step is evaluating the performance of a model. For classification models, the Confusion Matrix serves as a fundamental instrument for evaluating the performance. It provides a clear and visual summary of the prediction accuracy of a model by illustrating the correspondence between the predicted...

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