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Handling Missing Data

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Real-world datasets are rarely perfect. Common issues include:

  • Missing values
  • Duplicate records
  • Inconsistent formats
  • Incorrect entries

Pandas provides tools such as:

df.isnull()
df.dropna()
df.fillna()

Data cleaning is often the most time-consuming stage of a project. Clean data leads to more accurate analysis and reliable conclusions.

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