In Python with pandas, z-score for the sample data takes a few lines, shown below. It returns 4.04.
z = (Value − Mean) ÷ Standard deviation
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")
x = df["website_orders"]
df["z"] = (x - x.mean()) / x.std()
z_day_17 = df.loc[df["day"] == 17, "z"].item()
print(round(z_day_17, 2))
Expected result on the sample data: 4.04.
What the result means
Day 17 had 61 website orders. Its z-score is computed with a mean and standard deviation that include day 17 itself, which shrinks the score; leaving the day out of the baseline gives a much larger one.
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 z-score misleads
- Outliers inflate the standard deviation and so hide themselves. For outlier hunting, a robust rule (IQR, or median absolute deviation) is safer.
- The "beyond 3 is rare" rule assumes roughly normal data. Skewed data produces large z-scores routinely.
- Use the same standard deviation version (sample or population) consistently; STANDARDIZE takes whichever you give it.
The plain-language explanation is on the Z-score 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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