Exploratory Data Analysis in 20 Minutes

Exploratory Data Analysis with Pandas in Python | Generate Your Own Dataset with ChatGPT In this tutorial, you'll learn how to perform Exploratory Data Analysis (EDA) using Python's pandas library. We walk through each step of analyzing and understanding data, making this a perfect guide for beginners or those wanting a hands-on refresher. Plus, learn how to generate your own practice dataset using ChatGPT. What's covered in this video: Generating data with ChatGPT (00:52) Installing the necessary Python libraries (02:09) Importing and previewing the dataset (03:20) Basic operations: top rows, bottom rows, shape, columns, and data types (04:56-06:26) Data cleaning: converting data types, removing columns, renaming columns, handling missing values and duplicates (06:26-13:50) Summarizing categorical data and visualizing trends (13:50-17:15) Purchase trend analysis over time (17:15-19:37) Summary of findings (19:37) Whether you're preparing for a data analyst interview or just starting out in data analytics, this video will help you build foundational EDA skills. If you find this content helpful, don't forget to like, share, and subscribe for more data analysis tutorials! ---------------------- Dataset used in this tutorial - https://www.kaggle.com/datasets/shrishtimanja/ecommerce-dataset-for-data-analysis ---------------------- 💌 Join my newsletter and get access to various freebies (Books and checklists, CV template, Portfolio Projects) - https://stan.store/KarinaDataScientist 🧠 AI to help you analyse your data - https://powerdrill.ai/?via=karina-samsonova ---------------------- Timestamps: ---------------------- 00:00 Intro 00:24 About dataset 00:52 Generate your data with ChatGPT 02:09 Libraries to install/import 02:54 Path to the file 03:20 File preview 04:05 Change display max_rows max_columns 04:56 View top rows 05:15 View bottom rows 05:35 Check shape of your data 05:50 Get a list of columns 06:00 Data types 06:26 Convert object to datetime 07:07 Statistics (.describe) 08:24 Remove columns 10:20 Rename columns 11:36 Check for NA values 12:10 Duplicates 12:36 Remove duplicates 13:50 Summaries of categorical data 14:34 Visuals for categorical data 17:15 Purchase trend over time 19:37 Summary of findings 🎥 Other videos you might be interested in ---------------------- https://youtu.be/C8OqKdWstgc ---------------------- About me ---------------------- Hi, my name is Karina and I'm a finance person turned data person. My mission is to transform intimidating tech into accessible tools. I aim to empower 1 million people to harness the power of AI, Python, SQL, and Excel to work smarter, not harder. Contact ---------------------- Youtube: youtube comments are by far the best way to get a response from me! email for business inquiries only: [email protected] ---------------------- Social Media: ---------------------- TikTok: https://www.tiktok.com/@karinadatascientist Instagram: https://www.instagram.com/karinadatascientist/ Linkedin: https://www.linkedin.com/in/karina-samsonova/ #pythonprogramming #dataanalysis #dataanalytics