EasyTutorGuide
Translate:
Home › Courses › pandas

pandas

A complete pandas course for AI and data analysis covering Series, DataFrames, CSV/JSON, selection, filtering, missing values, duplicates, grouping, joins, reshaping, time data, feature preparation, quality checks, and AI-ready datasets.

Course map
Beginner → practical: This 60-chapter course starts with the basics and progresses into AI-oriented workflows. Every chapter includes simple explanations, technical words in parentheses, real-world code, expected output, step-by-step explanation, exercises, common mistakes, and Q&A.

Complete 60-Chapter Course

  1. Chapter 1: What pandas Is
  2. Chapter 2: Installing and Importing pandas
  3. Chapter 3: Series
  4. Chapter 4: DataFrames
  5. Chapter 5: Creating a DataFrame
  6. Chapter 6: Reading CSV Files
  7. Chapter 7: Reading JSON Data
  8. Chapter 8: Writing CSV Files
  9. Chapter 9: Inspecting Data with head and tail
  10. Chapter 10: DataFrame Shape and Columns
  11. Chapter 11: Data Types
  12. Chapter 12: Selecting One Column
  13. Chapter 13: Selecting Multiple Columns
  14. Chapter 14: Selecting Rows with loc
  15. Chapter 15: Selecting Rows with iloc
  16. Chapter 16: Filtering Rows
  17. Chapter 17: Multiple Conditions
  18. Chapter 18: Sorting Rows
  19. Chapter 19: Renaming Columns
  20. Chapter 20: Dropping Columns
  21. Chapter 21: Adding Columns
  22. Chapter 22: Changing Values
  23. Chapter 23: Missing Values
  24. Chapter 24: Detecting Missing Values
  25. Chapter 25: Filling Missing Values
  26. Chapter 26: Dropping Missing Values
  27. Chapter 27: Duplicate Rows
  28. Chapter 28: Removing Duplicates
  29. Chapter 29: Type Conversion
  30. Chapter 30: String Cleaning
  31. Chapter 31: String Methods
  32. Chapter 32: Date and Time Conversion
  33. Chapter 33: Extracting Date Parts
  34. Chapter 34: Replacing Values
  35. Chapter 35: Mapping Categories
  36. Chapter 36: Apply Concepts
  37. Chapter 37: GroupBy
  38. Chapter 38: GroupBy Aggregations
  39. Chapter 39: Pivot Tables
  40. Chapter 40: Value Counts
  41. Chapter 41: Merging DataFrames
  42. Chapter 42: Joining DataFrames
  43. Chapter 43: Concatenating DataFrames
  44. Chapter 44: Index Basics
  45. Chapter 45: Resetting the Index
  46. Chapter 46: Setting an Index
  47. Chapter 47: MultiIndex Concepts
  48. Chapter 48: Reshaping with melt
  49. Chapter 49: Reshaping with pivot
  50. Chapter 50: Binning Values
  51. Chapter 51: Rolling Calculations
  52. Chapter 52: Lag Features with shift
  53. Chapter 53: Categorical Data
  54. Chapter 54: One-Hot Encoding Concepts
  55. Chapter 55: Data Validation
  56. Chapter 56: Data Leakage Checks
  57. Chapter 57: Mini Project: Customer Data Cleaner
  58. Chapter 58: Mini Project: Sales Analysis
  59. Chapter 59: Mini Project: Training Dataset Builder
  60. Chapter 60: Capstone: AI Data Preparation Pipeline

Course features

  • Responsive top logo and main menu.
  • Chapter menu below the main header, with all 60 links on mobile and desktop.
  • Chapter search, font increase/decrease, night view, print, and go-to-top.
  • Google Translate area with RTL-safe behavior and no intentional horizontal page movement.
  • Original lessons, SEO metadata, structured data, course map, sitemap, robots file, and legal notice.