Pandas Day 51 | Excel, JSON & DateTime in Pandas | Read Excel, JSON, Date Filtering & Time Series

In this Pandas Data Science tutorial, we learn how to work with different data sources and handle dates using Pandas. This session focuses on practical skills that are commonly used in real-world Data Science projects. 📌 Topics Covered: ✅ Reading Excel files with pd.read_excel() ✅ Exporting DataFrames to Excel with to_excel() ✅ Working with multiple Excel sheets ✅ Understanding JSON and its structure ✅ Reading JSON with pd.read_json() ✅ Writing JSON with to_json() ✅ CSV vs Excel vs JSON ✅ Real-world data import workflow ✅ Why dates are important in Data Science ✅ Converting dates with pd.to_datetime() ✅ Extracting Year, Month and Day ✅ Extracting Day of the Week ✅ Filtering data by dates ✅ Calculating date differences ✅ Sales time-series analysis ✅ Monthly sales analysis using groupby() 📂 Practice Dataset: The practice files include CSV, JSON and Excel versions of the same sales dataset, so you can practice importing and analyzing data from different formats. 🎯 What You Will Build: By the end of this lesson, you will be able to import real-world data from CSV, Excel and JSON files, clean and work with dates, filter time periods, calculate date differences, and perform basic time-series analysis using Pandas. This tutorial is part of the Data Science with Python & Pandas learning series. #Pandas #Python #DataScience #PythonPandas #Excel #JSON #DateTime #TimeSeries #DataAnalysis #MachineLearning