Flash cards
Review the key moves
What is the main idea behind Data Science - Statistics Percentiles?
Lesson checks
Practice each idea before moving on
Short Mimo-style checks built from this lesson's code, terms, and sequence.
Which statement best captures the main point of this lesson?
Complete the missing token from the example code.
___ numpy as npPut the learning moves in the order that makes the concept easiest to apply.
Before charting or modeling a dataset, which move should come first?
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)- 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.