How to interprete percentile information from the describe function in Pandas? I am a bit stumped on how to interpret the percentile information you see when you call the describe function on dataframes in Pandas.
I believe I have a basic understanding of what percentile means. For example if in a test someones score 40% which ranks at the 75% percentile, this means that the score is higher than 75% of the total scores.
But I don't know how to translate this knowledge to interpret what I see from the describe function.
To illustrate, given the following:
test = pd.DataFrame([1,2,3,4,5,1,1,1,1,9])
test.describe()

This prints out something similar to this:
| count | 10.000000 |
|-------|-----------|
| mean  | 2.800000  |
| std   | 2.616189  |
| min   | 1.000000  |
| 25%   | 1.000000  |
| 50%   | 1.500000  |
| 75%   | 3.750000  |
| max   | 9.000000  |

Now I do not know how to interpret the values assigned to 25%, 50% and 75%. For example 5 out of the 10 values is set to 1, but the 50% has a value of 1.50000, clearly it is not saying 1.5 has a value of 50% because there is not even 1.5 in the data set.
Also why is 25% set to 1.000000 and 75% set to 3.750000?
I know I am interpreting this wrong hence this question! Would appreciate if someone can help understand this
Percentiles indicate the percentage of scores that fall below a particular value. They tell you where a score stands relative to other scores.
For example:
a person height 215 cm is at the 91st percentile, which indicates that his hight is higher than 91 percent of other scores.
Percentiles are a great tool to use when you need to know the position of a value/score respect to a population/data distribution you're considering. Where does a value fall within a distribution of values? While the concept behind percentiles is straight forward, there are different mathematical methods for calculating them.
In your example 50% correspond to the median of the ordered values distribution.
In this case the median is calculated between two values: 1 and 2 so the median is calculated (in this case 'cause the number of values is even so the median as to be calculated between the fifth and sixth ordered values ) as the mean between them 1.5.