Learn Python Pandas in 22 Minutes (Full Guide

hat is Pandas in Python and how do you use it for data analysis? In this beginner-friendly tutorial, Felex Kuria covers the essentials of Python Pandas using Jupyter Notebook. Learn how to work with Series (1D) and DataFrames (2D), read CSV files, inspect datasets, and calculate descriptive statistics like mean, standard deviation, and quantiles. Whether you are preparing for a data science interview, working on a Python assignment, or transitioning from NumPy to Pandas, this step-by-step walkthrough gives you practical coding foundations. 📌 What You Will Learn (AEO Quick Summary): • What is the difference between a Pandas Series and a DataFrame? (1D vs 2D tabular structures) • How to create a Pandas Series from lists, dictionaries, and NumPy arrays • How to load tabular data using pd.read_csv() • How to inspect datasets using .head(), .tail(), .columns, .info(), and .shape • How to find missing data using .isnull().sum() and .isna().sum() • How to compute summary statistics using .describe(), .mean(), .std(), and .max() ⏱️ Timestamps: 00:00 - Introduction & Learning Objectives 01:23 - How to Install Pandas (conda vs pip) 02:00 - Pandas Data Structures Explained (Series vs DataFrames) 03:08 - Setting Up & Activating Jupyter Notebook 04:35 - Creating a Pandas Series from Lists 06:30 - Creating a Series from a Dictionary 07:30 - Converting NumPy Arrays to Pandas Series 08:44 - Understanding Series Dimension Errors (1D vs 2D) 10:45 - Key Series Attributes: Shape, Ndim & Index 12:30 - Accessing Series Elements by Index 13:30 - Introduction to Pandas DataFrames (2D Tables) 15:05 - How to Read CSV Files Using pd.read_csv() 16:05 - Selecting Columns & Previewing Data (.head() & .tail()) 17:42 - Inspecting Data Structure (.columns, .info(), .shape) 18:55 - Checking for Missing Values (.isnull().sum()) 20:00 - Generating Descriptive Statistics with .describe() 21:15 - Computing Mean, Standard Deviation & Min/Max If this video helped you understand Pandas, please like, comment, and subscribe to Felex Kuria for more Python, Data Science, and Cloud computing tutorials! #Python #Pandas #DataScience #PythonPandas #JupyterNotebook #DataAnalysis #MachineLearning