Handbook of Hidden Data Scientist (Python)
  • Introduction
  • Machine Learning
    • Supervised Learning
      • Features and Labels
      • Linear Decision Surface
      • Naive Bayes
      • Support Vector Machine
      • Decision Trees
      • Regressions
  • Python
  • CSV with pandas
    • Reading CSV
    • Math Operations on Column
    • Joining CSVs
    • Plot and Normalize CSV Data
  • NumPy
    • Using NumPy from pandas DataFrame
    • Create NDArray
    • Working with NDArray
    • Timing operations
  • Statistical Analysis
    • Global Statistics
    • Rolling Statistics
    • Daily Returns
    • Cumulative Returns
  • Incomplete Data
    • Pandas fillna()
  • Histograms and Scatter Plots
    • Histogram
    • Two Histograms
    • Scatter Plot
  • Visualization
    • pyplot
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Incomplete Data

This chapter is about what to do with incomplete data during analysis.

Pristine Data

People assume:

  • Perfectly recorded minute by minute

  • No gaps or missing data points

But reality is:

  • Data is amalgamation

  • For example, not all stocks trade every day

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Last updated 5 years ago

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