# Data Science - Statistics Percentiles

## 25%, 50% and 75% - Percentiles

Percentiles are used in statistics to give you a number that describes the value that a given percent of the values are lower than. Let us try to explain it by some examples, using Average_Pulse.

• The 25% percentile of Average_Pulse means that 25% of all of the training sessions have an average pulse of 100 beats per minute or lower. If we flip the statement, it means that 75% of all of the training sessions have an average pulse of 100 beats per minute or higher
• The 75% percentile of Average_Pulse means that 75% of all the training session have an average pulse of 111 or lower. If we flip the statement, it means that 25% of all of the training sessions have an average pulse of 111 beats per minute or higher

### Task: Find the 10% percentile for Max_Pulse

The following example shows how to do it in Python:

### Example

import numpy as np

Max_Pulse= full_health_data["Max_Pulse"]
percentile10 = np.percentile(Max_Pulse, 10)
print(percentile10)
Try it Yourself »
• Max_Pulse = full_health_data["Max_Pulse"] - Isolate the variable Max_Pulse from the full health data set.
• np.percentile() is used to define that we want the 10% percentile from Max_Pulse.

The 10% percentile of Max_Pulse is 120. This means that 10% of all the training sessions have a Max_Pulse of 120 or lower.

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