Please help me understand how to approach this problem, I'm a beginner in Python.  

I have this specific task where I have to import data from an excel file (.xlsx) and take the column 'Count' to perform normalization in Python.  

**Then under Numpy library define a function in Python to perform this [normalization operation](https://i.stack.imgur.com/SKHh8.png) (or any operation in future) and print the output(Normalized result) to a new sheet in the same excel workbook**   

**Is it possible to do this task strictly using numpy?**\*   

[ formula used in excel -> ( =(A2-MIN($A$2:$A$11))/(MAX($A$2:$A$11)-MIN($A$2:$A$11))\*10 ) which is to be translated in to a function in python using numpy}   

**instructions provided to me is as follows:**   

```
import numpy as nd

def normalize (x):
    """ This function has the logic for normalization
    Inputs
    ------
      x: input count 
    Returns
    ------
      the transformed f(x)  
    """
    return x 

```

**Sample Data:**   

| Count | Constant |
| --- | --- |
| 10 | 100 |
| 20 | 100 |
| 30 | 100 |
| 40 | 100 |
| 50 | 100 |
| 60 | 100 |
| 70 | 100 |
| 80 | 100 |
| 90 | 100 |
| 100 | 100 |

**This is what I I've coded so far:-**   

```
import pandas as pd
import numpy as np

data = pd.read_excel(r"path of file") #import or read excel file
data = data['Count'] #to convert the column into dataframe
data2 = data.to_numpy() #to convert dataframe into numpy array  
print(data2)

def normalize(data2):
    return ((data2 - min(data2))/(max(data2)-min(data2)))*10
  print(normalize(data2))

```

**But this code doesn't seem like to be completely on par with the instructions provided**