In Python with pandas, standard deviation for the sample data takes a few lines, shown below. It returns 7.
Sample standard deviation = √( Σ (x − mean)² ÷ (n − 1) )
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")
sd_orders = df["website_orders"].std() # sample (n - 1), like STDEV.S
print(round(sd_orders, 2))
Expected result on the sample data: 7.
What the result means
Use the sample standard deviation (dividing by n − 1) when your rows are a sample of a longer process, which business data almost always is.
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 standard deviation misleads
- STDEV.S and STDEV.P differ: .S divides by n − 1 (a sample), .P by n (a whole population). pandas .std() is the sample version; NumPy np.std() is the population version by default.
- One extreme value inflates it. Here day 17 adds a lot; without it the standard deviation is much smaller.
- It assumes the mean is a sensible centre. For skewed data, the interquartile range describes spread more honestly.
The plain-language explanation is on the Standard deviation 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