A comprehensive guide to mastering Pandas, the essential Python library for data manipulation and analysis.

📚 Course Overview

Pandas is a powerful tool for working with structured data. This course covers:

  • Data structures: Series and DataFrame
  • Data cleaning: Handling missing values and duplicates
  • Data analysis: Aggregation, filtering, and transformation
  • Data visualization: Integrating with Matplotlib and Seaborn
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🔍 Core Concepts

  1. DataFrame creation: Using pd.read_csv() or pd.DataFrame()
  2. Indexing: .loc[], .iloc[], and .at[]
  3. Operations: .head(), .tail(), .describe(), .info()
  4. File I/O: Reading and writing CSV, Excel, and SQL files
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🧱 Hands-on Practice

  • Exercise 1: Load a dataset and explore its structure
  • Exercise 2: Clean and preprocess data using Pandas
  • Exercise 3: Perform groupby operations and visualization
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🚀 Advanced Topics

  • Time series analysis: Resampling and shifting data
  • Merge and concatenate: Combining datasets with .merge() or .concat()
  • Performance optimization: Using categorical data types and apply()
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📚 Expand Reading

For deeper insights into Python fundamentals, check out our Python基础 course.

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