Percentiles are used in statistics to provide a value below which a given percentage of the data points fall.
Let’s explain this with examples using Average_Pulse.
The 25th percentile of Average_Pulse means that 25% of all training sessions have an average pulse of 100 beats per minute or less. Conversely, 75% of the sessions have an average pulse of 100 beats per minute or more. The 75th percentile of Average_Pulse means that 75% of all training sessions have an average pulse of 111 beats per minute or less. Conversely, 25% of the sessions have an average pulse of 111 beats per minute or more.
The following example demonstrates how to do this in Python:
| import numpy as np Max_Pulse= full_health_data[“Max_Pulse”] percentile10 = np.percentile(Max_Pulse, 10) print(percentile10) |
Max_Pulse = full_health_data[“Max_Pulse”] – Isolate the Max_Pulse variable from the full health data set.
np.percentile() is used to calculate the 10% percentile of Max_Pulse.
The 10% percentile of Max_Pulse is 120, meaning that 10% of all training sessions have a Max_Pulse of 120 or lower.
