Make Pandas much faster by vectorization

Based on Make Your Pandas Code Lightning Fast.

Problem:

Apply a logical condition across every row of a DataFrame. Assign the result to a new column.

Solutions:

Level 1 Looping:

• Define a function with the logic for rewarding for each row (i.e., person).
• Loop over each row of the DataFrame to apply the condition to obtain the value and assign it to a cell.

Level 2: Vectorization:

• Apply the logical conditions to the whole DataFrame.
• Assign the default values to the column.
• Assign the Series of calculated values with condition.

The implementation is in vectorize_conditions.ipynb and vectorize_conditions.py. The notebook can be run on MyBinder.org.