In Python with pandas, interquartile range for the sample data takes a few lines, shown below. It returns 6.75.
IQR = Third quartile (75th percentile) − First quartile (25th percentile)
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")
q1, q3 = df["website_orders"].quantile([0.25, 0.75])
iqr_orders = q3 - q1
print(iqr_orders)
Expected result on the sample data: 6.75.
What the result means
Quartiles can be computed in several ways. The inclusive method used here (QUARTILE.INC, pandas quantile, PERCENTILE_CONT) interpolates between values and is the most common. On this data the IQR hardly notices the sale on day 17, while the range and standard deviation both jump because of it, which is exactly why the IQR is used for outlier rules.
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 interquartile range misleads
- QUARTILE.INC and QUARTILE.EXC give different answers on small datasets. Use the same method everywhere.
- The IQR says nothing about the tails. Report it with the median, or draw a box plot.
- For very small samples (under ten values) quartiles are unstable.
The plain-language explanation is on the Interquartile range (IQR) 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.
Legal entity
Devansh Yadav
Moradabad, Uttar Pradesh
Governing law: Courts at Moradabad, Uttar Pradesh
Phone: +91 8433475698
Legal: legal@analyzeit.in