Python for Data Analysis - Data Wrangling (数据整理)

Welcome to Lesson 3 of the Python for Data Analysis series! 📊
This video will guide you through the essential techniques of data wrangling — the process of cleaning, transforming, and organizing raw data into a usable format.

Key Concepts Covered

• 🧹 Data Cleaning: Handling missing values, duplicates, and inconsistencies

  # Example: Removing duplicates
  df.drop_duplicates(inplace=True)
  

  

• 🔄 Data Transformation: Normalizing, encoding, and reshaping data

  # Example: Converting categorical data to numerical
  df = pd.get_dummies(df, columns=['category'])
  

  

• 🧩 Data Merging: Combining datasets using merge() or concat()

  # Example: Merging two DataFrames
  merged_df = pd.merge(df1, df2, on='key')
  

  

Practical Tips

• Always start with df.head() to inspect data structure
• Use describe() for statistical summaries
• Leverage pandas libraries for efficient data manipulation

For deeper insights into data wrangling tools, check our Python Data Tools Guide.
Stay tuned for the next lesson on data visualization! 📈