Data Science – Statistics: Percentiles

In statistics, percentiles are used to give you a number that describes the value below which a given percentage of the data points fall. Essentially, percentiles help us understand how a particular value compares to the rest of the data.

25%, 50%, and 75% Percentiles

Let’s break down what these specific percentiles represent:

• 25% Percentile: This means that 25% of the data points are below this value. For example, if the 25% percentile of Average_Pulse is 100 beats per minute, then 25% of the training sessions had an average pulse of 100 or lower.
• 50% Percentile (Median): This is the middle value of the data set. Half of the data points fall below this value, and half fall above it.
• 75% Percentile: This means that 75% of the data points are below this value. For example, if the 75% percentile of Average_Pulse is 111 beats per minute, then 75% of the training sessions had an average pulse of 111 or lower.

By understanding percentiles, we can gain insights into the distribution of data and better analyze trends, making informed decisions easier.

Example: Finding the 10% Percentile for Max_Pulse

Let’s walk through an example of how to calculate the 10% percentile for the Max_Pulse using Python:

import numpy as np

# Isolate the variable Max_Pulse from the dataset
Max_Pulse = full_health_data["Max_Pulse"]

# Calculate the 10% percentile of Max_Pulse
percentile10 = np.percentile(Max_Pulse, 10)

# Print the result
print(percentile10)

Explanation of the Code:

• Max_Pulse refers to the “Max_Pulse” column in the dataset full_health_data.
• np.percentile() is the NumPy function used to calculate the desired percentile. In this case, we are calculating the 10% percentile.

The output from the Python code will give the 10% percentile for Max_Pulse. For example, if the 10% percentile is 120, it means that 10% of all the training sessions had a maximum pulse of 120 or lower.

Understanding the Interpretation

In the context of Max_Pulse, a 10% percentile of 120 means that 10% of the training sessions had a maximum pulse rate of 120 beats per minute or lower. This gives us a clear picture of how the rest of the data points are distributed relative to the lowest 10% of sessions.

By using percentiles, we can easily quantify where a specific value stands in relation to the overall dataset and uncover important insights into the data distribution.