In Python with pandas, correlation coefficient for the sample data takes a few lines, shown below. It returns 0.72.
r = Σ (x − x̄)(y − ȳ) ÷ √( Σ (x − x̄)² × Σ (y − ȳ)² )
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")
r = df["ad_spend"].corr(df["website_orders"])
print(round(r, 3))
Expected result on the sample data: 0.72.
What the result means
Ad spend and website orders are positively correlated here, but day 17 had both the highest spend and the most orders, and that one day carries much of it: leave it out and r roughly halves. Always check a correlation with and without its most extreme points.
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 correlation coefficient misleads
- Correlation is not causation. Spend and orders can both rise because of a season or a sale.
- It only detects straight-line relationships; a curved relationship can have r near zero.
- Two trending series are correlated even when unrelated. Correlate the day-to-day changes instead when both grow over time.
The plain-language explanation is on the Correlation coefficient page.
The data
| day | ad_spend | website_orders |
|---|---|---|
| 1 | 3,800 | 24 |
| 2 | 3,000 | 27 |
| 3 | 3,900 | 22 |
| 4 | 3,200 | 30 |
| 5 | 4,000 | 28 |
| 6 | 4,100 | 25 |
| 7 | 3,100 | 31 |
| 8 | 3,600 | 29 |
| 9 | 4,100 | 26 |
| 10 | 3,500 | 33 |
| 11 | 4,300 | 30 |
| 12 | 4,500 | 28 |
| 13 | 3,900 | 35 |
| 14 | 3,900 | 32 |
| 15 | 4,300 | 29 |
| 16 | 3,700 | 34 |
| 17 | 7,200 | 61 |
| 18 | 4,800 | 31 |
| 19 | 3,800 | 36 |
| 20 | 4,500 | 33 |
| 21 | 4,500 | 30 |
| 22 | 4,000 | 37 |
| 23 | 4,200 | 35 |
| 24 | 4,800 | 32 |
| 25 | 4,300 | 38 |
| 26 | 5,000 | 36 |
| 27 | 3,900 | 34 |
| 28 | 5,000 | 40 |
| 29 | 5,200 | 37 |
| 30 | 4,700 | 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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