Data Cleaning & Python: 28 Techniques to Transform Your Skills! [OpenAI, Pandas, Matplotlib, Numpy]

In this comprehensive tutorial, we delve deep into the powerful capabilities of the Pandas library, your go-to tool for data manipulation and analysis in Python. Learn essential data importing techniques that streamline the process of bringing data into your workflow, as well as cutting-edge cleaning strategies to transform messy datasets into tidy structures perfect for analysis. Whether you're a beginner or an experienced data scientist, this video is packed with practical examples and code snippets to enhance your data handling skills. 0:00:40 - 1 Introduction to Pandas and Data Importing 0:11:32 - 2 Data Cleaning Techniques for Tabular Data 0:21:09 - 3 Persisting Data in Various Formats 0:30:41 - 4 Introduction to JSON and Data Importing 0:37:23 - 5 Data Preparation Techniques with Spark 0:48:14 - 6 Advanced Data Cleaning Strategies in Practice 0:57:11 - 7 Getting a First Look at Your Data 1:05:40 - 8 Selecting and Organizing Columns 1:13:56 - 9 Generating Summary Statistics for Continuous Variables 1:24:17 - 10 Introduction to Anomaly Detection 1:32:16 - 11 Anomaly Detection Techniques 1:41:12 - 12 Interpreting and Evaluating Results 1:48:46 - 13 Introduction to Visualizations and Their Importance 1:58:20 - 14 Exploring Different Types of Visualizations 2:08:39 - 15 Overview of Pandas Series Operations 2:18:23 - 16 Advanced Techniques in Data Manipulation 2:26:41 - 17 Identifying Missing Values 2:36:57 - 18 Imputation Techniques 2:44:38 - 19 Introduction to Feature Engineering 2:50:02 - 20 Advanced Techniques in Feature Engineering 2:59:11 - 21 Introduction to Data Cleaning Techniques 3:07:22 - 22 Advanced Aggregation Methods in Pandas 3:17:18 - 23 Combining DataFrames Vertically 3:25:51 - 24 Doing Many-to-Many Merges 3:33:40 - 25 Introduction to Tidy Data 3:41:33 - 26 Advanced Techniques in Data Reshaping 3:51:10 - 27 Introduction to Data Cleaning Techniques 4:01:52 - 28 Advanced Data Cleaning with Pipelines and Classes Business contact: [email protected]