05. Data Interpolation and Reshaping

Note: The video at 1:00 incorrectly shows df.ffill() for both forward and backward filling. Use df.ffill() for forward fill (previous value) and df.bfill() for backward fill (next value).

Approach to Handling Missing Data and Reshaping in Trading Applications

Data handling in trading often involves dealing with missing data and reshaping data for analysis. Here's a brief guide:

Addressing Missing Data:

• Missing Data Challenges: In time-series data, removing observations can disrupt time sequences, affecting model accuracy.
• Imputation Techniques:
  • Fill Forward: Use the previous period's data to fill gaps.
  • Fill Backward: Use subsequent data to fill in earlier gaps.
• Tools: Utilize Pandas functions F fill (fill forward) and B fill (fill backward) for efficient imputation.

Data Reshaping:

• Wide vs. Long Format:
  • Long Format: Preferred for analytics, as it simplifies calculation of metrics (e.g., grouping data by category).
  • Wide Format: Better for displaying compact data but less convenient for data analysis.
• Pivoting in Pandas:
  • Transform wide data into long format using the Pivot method.
  • Retain the original column (e.g., "team") and specify which columns to pivot (e.g., "points").

Efficient handling and reshaping data facilitate more accurate analytics and preparation for visualization.