In Python with pandas, r-squared for the sample data takes a few lines, shown below. It returns 0.52.
R² = 1 − Σ (y − ŷ)² ÷ Σ (y − ȳ)² (for one predictor, the square of r)
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_squared = df["ad_spend"].corr(df["website_orders"]) ** 2
print(round(r_squared, 3))
Expected result on the sample data: 0.52.
What the result means
A high R² does not make a model useful, and a low one does not make it useless: a factor that explains a small share of a large number can still be worth a lot.
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 r-squared misleads
- Adding predictors always raises R²; use adjusted R² when comparing models with different numbers of inputs.
- One extreme point (day 17 here) can produce most of the R².
- R² says nothing about whether the relationship is causal.
The plain-language explanation is on the R-squared 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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