In Python with pandas, percentile for the sample data takes a few lines, shown below. It returns 38.1.
p-th percentile = the value below which p% of the data falls (interpolated between neighbouring values)
Step by step
- Install the libraries (pip install pandas) and save daily.csv next to your script.
- Run the script and compare the printed value with the expected result.
The code
import pandas as pd
df = pd.read_csv("daily.csv")
p90_orders = df["website_orders"].quantile(0.9)
print(round(p90_orders, 2))
Expected result on the sample data: 38.1.
What the result means
This is the 90th percentile of daily website orders over 30 days. With so few days it sits between the 27th and 28th sorted values.
Notes for Python
- pandas methods such as .std(), .var() and .quantile() match Excel's sample versions (STDEV.S, VAR.S, PERCENTILE.INC). NumPy's np.std() and np.var() default to the population version instead.
- pandas skips NaN values in these calculations by default. Count how many rows were used if data might be missing.
Where percentile misleads
- Several definitions exist. PERCENTILE.INC, pandas quantile() and PERCENTILE_CONT agree; PERCENTILE.EXC does not.
- High percentiles need many rows to be stable. With 30 days, the 99th percentile is little more than the maximum.
- A percentile of a value (where does 35 rank?) is a different calculation: PERCENTRANK.
The plain-language explanation is on the Percentile page.
The data
| day | website_orders |
|---|---|
| 1 | 24 |
| 2 | 27 |
| 3 | 22 |
| 4 | 30 |
| 5 | 28 |
| 6 | 25 |
| 7 | 31 |
| 8 | 29 |
| 9 | 26 |
| 10 | 33 |
| 11 | 30 |
| 12 | 28 |
| 13 | 35 |
| 14 | 32 |
| 15 | 29 |
| 16 | 34 |
| 17 | 61 |
| 18 | 31 |
| 19 | 36 |
| 20 | 33 |
| 21 | 30 |
| 22 | 37 |
| 23 | 35 |
| 24 | 32 |
| 25 | 38 |
| 26 | 36 |
| 27 | 34 |
| 28 | 40 |
| 29 | 37 |
| 30 | 39 |
Same method, other tools
Related methods
Learn more about Python
- Python and SQL for data analysis, side by side — 3 min read
- Data analysis methods and techniques, and when to use each — 3 min read
About the data on this page
The figures come from a small made-up dataset, printed above in full so you can check every number by hand. Download it as daily.csv. The results are computed from those rows when the page is built, and the Excel formulas, SQL and Python on these pages are run against the same rows before publishing, so the code and the answers agree.
To get these numbers from your own data without writing formulas, try AnalyzeIt: ask in plain words and it shows the working behind every figure.
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