Pandas Merge & Join Explained | Inner, Left, Right, Outer Join + CSV | Data Science Day 50

Pandas Data Science Course — Day 50 In this video, we learn how to *combine related datasets using Pandas* and work with CSV files — an essential skill for real-world Data Science and Data Analysis. Topics Covered 🔹 Why do we need to merge datasets? 🔹 Understanding Primary Key & Foreign Key 🔹 `pd.merge()` basics 🔹 Inner Join 🔹 Left Join 🔹 Right Join 🔹 Outer Join 🔹 `join()` in Pandas 🔹 Merge vs Join vs Concat 🔹 Real-world Customer + Order dataset 🔹 What is CSV? 🔹 `pd.read_csv()` 🔹 Relative & Absolute file paths 🔹 Selecting columns using `usecols` 🔹 Understanding CSV headers 🔹 Handling missing values with `isna()` and `na_values` 🔹 Saving data using `to_csv()` 🔹 Reading large CSV files using `chunksize` 🔹 Real-world customer & order analysis using `groupby()` Key Pandas Functions python pd.merge() df.join() pd.concat() pd.read_csv() df.to_csv() df.groupby() What You'll Learn By the end of this lesson, you'll understand how to take **separate datasets, connect them using keys, clean the data, and perform meaningful analysis. This is an important step toward working with real-world datasets in Data Science, Data Analysis, Machine Learning, and Business Intelligence. 📊 Practice: Download the Day 50 CSV datasets and practice the merge, join, CSV, and analysis concepts yourself. 🔥Learn → Practice → Build → Grow #Python #Pandas #DataScience #DataAnalysis #PythonPandas #PandasTutorial #MachineLearning #CSV #DataAnalytics #PythonProgramming #DataScienceCourse #PandasMerge #PandasJoin #InnerJoin #LeftJoin #RightJoin #OuterJoin #DataCleaning #DataAnalysisTutorial