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 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....
Introduction One of the key tasks in data analysis is grouping data to gain insights and make informed decisions. In this tutorial, we will show you how to group the rows of a Pandas DataFrame and apply different aggregations on the grouped data. In order to do this, we will...
Introduction In this tutorial, we want to write a Pandas DataFrame to an Excel file. In order to do this, we use the to_excel() method of Pandas. Import Libraries First, we import the following python modules: import pandas as pd Create Pandas DataFrame Next, we create a Pandas DataFrame...
Introduction In this tutorial, we want to read an Excel file into a Pandas DataFrame. In order to do this, we use the read_excel() function of Pandas. Import Libraries First, we import the following python modules: import pandas as pd Excel File We consider the sheet "Frameworks"...
Introduction In this tutorial, we want to write a Pandas DataFrame to a CSV file. In order to do this, we use the to_csv() method of Pandas. Import Libraries First, we import the following python modules: import pandas as pd Create Pandas DataFrame Next, we create a Pandas DataFrame...