Python Pandas Full Tutorial for Beginners_training
Learn how to use pandas for data analysis in Python, starting from the absolute basics and progressing to real-world data manipulation. This tutorial is designed for beginners and covers everything from installing pandas and setting up Jupyter notebooks in VS Code to working with Series and DataFrames, handling missing data, filtering, grouping, merging, and visualizing data. You will practice with both toy examples and a real car dataset, gaining hands-on experience with essential pandas techniques for data cleaning, summarization, and analysis.
By the end of this lesson, you will be able to load CSV files, explore and summarize data, filter rows and columns, handle missing values, group and aggregate data, merge multiple tables, and apply custom functions. The step-by-step approach ensures you understand both the concepts and the practical code behind each operation, making it easy to apply these skills to your own projects.
00:00 Introduction to pandas and setup
02:22 Understanding Series: the building block
06:47 Series indexing, filtering, and math
09:09 Creating Series from dictionaries
11:48 Automatic index alignment in Series
14:58 DataFrames: working with full tables
16:53 Building and customizing DataFrames
18:53 Selecting columns and rows
21:00 Adding, changing, and removing columns
24:25 Dropping rows and columns with drop
27:06 DataFrames from nested dictionaries
29:15 Combining tables with merge
33:40 Stacking tables with concat
35:49 Real-world data: loading and exploring a CSV
38:41 Summarizing and describing data
40:32 Filtering and visualizing real data
42:15 Grouping and aggregating with groupby
46:12 Visualizing group counts
47:22 Searching and string filtering
48:38 Arithmetic between DataFrames and Series (broadcasting)
51:46 Indexing, reindexing, and slicing
55:16 DataFrame indexing and boolean filtering
59:39 loc and iloc: label vs position selection
61:06 Advanced: hierarchical indexing (multi-index)
65:51 Applying custom functions with apply and map
67:27 Handling missing data: detection, filling, and dropping
75:37 Missing data in DataFrames and cleanup strategies
79:07 Index immutability and common errors
81:32 Catching errors gracefully
82:26 Best practices: reusable functions for analysis
83:58 Mini project: analyzing car fuel efficiency
89:23 Saving results and round-trip workflow
91:23 Recap and next steps
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