## Issue

I am R programmer, but need to achieve rankings in a table using python. Let’s say I have a column "test" with a list of number lists:

```
df = pd.DataFrame({"test":[[1,4,7], [4,2,6], [3,8,1]]})
```

I expected to rank each item at the same location across rows (lists), and average all ranks to get a final score:

expected:

```
test rank_list final_score
0 [1, 4, 7] [1, 2, 3] 2
1 [4, 2, 6] [3, 1, 2] 2
2 [3, 8, 1] [2, 3, 1] 2
```

I know it is not a good example that all final scores are the same, but with hundreds of rows, the results will be various. I hope I describe the questions clearly, but if not, please feel free to ask.

I don’t know if I can do it in pandas, but I tried zip + scipy, but `scipy.stats.rankdata `

did not give the rank on item at the same index:

```
l = list(dff["test"])
ranks_list = [scipy.stats.rankdata(x) for x in zip(*l)] # not right
estimated_rank = [sum(x) / len(x) for x in ranks_list]
```

I am open to any kinds of packages, whichever is convenient. Thank you!

## Solution

```
import numpy as np
# Create a numpy array
a = np.array([[1,4,7], [4,2,6], [3,8,1]])
# get the index of the sorted array along each row
# Python uses zero-based indexing so we add 1
rank_list = np.argsort(a, axis=0) + 1
# calculate the average rank of each column
final_score = np.mean(rank_list, axis=1)
```

Answered By – kwsp

Answer Checked By – David Goodson (BugsFixing Volunteer)