Pandas is a powerful Python library for data manipulation and analysis, providing data structures like DataFrames that simplify handling and analyzing structured data for various applications, from data cleaning to complex data analysis. Here, you will find hands-on tutorials, example projects and best practices for using Pandas.
📘 Introduction When working with data spread across multiple CSV files, combining them into one unified dataset can save time and simplify your workflow. In this guide, you’ll learn how to merge multiple CSV files into a single Pandas DataFrame using just a few lines of Python. ✅ Prerequisites Before you...
📘 Introduction JSON is one of the most widely used data formats for APIs, configurations, storage, and modern applications. Converting CSV to JSON in Python is incredibly simple using Pandas. In this tutorial, we will walk through the full process: creating a sample CSV file, loading it into a Pandas DataFrame,...
📘 Introduction In modern data workflows, Parquet is a popular columnar storage format for efficient data storage and faster analytics. Converting CSV to Parquet in Python is straightforward using Pandas and PyArrow. In this tutorial, we will walk you through the complete process: from creating a sample CSV file, reading it...
📘 Introduction If you enjoy working with pandas but wish you could use clean, powerful SQL at any time, then DuckDB is the right tool for you. With DuckDB, you can query your DataFrames instantly without having to set up a database, run a server, or change your workflow. ✅ Prerequisites Before...
📘 Introduction Streamlit makes it easy to build interactive apps — and its st.data_editor() widget lets users edit data directly inside your app. But what if a single cell needs multiple selections, like tags, skills, or categories? 🤔 That’s where st.column_config.MultiselectColumn comes in! It allows you to...
📘 Introduction Streamlit makes it super easy to build interactive apps in Python. But as your app grows — loading datasets, running expensive computations, or calling APIs — you might notice it slows down. The good news: Streamlit has a built-in caching system that stores results of expensive operations and reuses them instead...
Introduction When working with data in Python, two of the most popular tools available are Pandas and PySpark. Both offer powerful data manipulation capabilities but are designed for different use cases. Pandas is well-suited for small to moderately large datasets, offering an intuitive API for data analysis. PySpark, on the...
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 Pandas DataFrame. In order to do this, we will use the astype() method, the map() method and the to_...
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....