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