Select column with the most unique values from csv, python - pandas

Question

I'm trying to come up with a way to select from a csv file the one numeric column that shows the most unique values. If there are multiple with the same amount of unique values it should be the left-most one. The output should be either the name of the column or the index.
Position,Experience in Years,Salary,Starting Date,Floor,Room
Middle Management,5,5584.10,2019-02-03,12,100
Lower Management,2,3925.52,2016-04-18,12,100
Upper Management,1,7174.46,2019-01-02,10,200
Middle Management,5,5461.25,2018-02-02,14,300
Middle Management,7,7471.43,2017-09-09,17,400
Upper Management,10,12021.31,2020-01-01,11,500
Lower Management,2,2921.92,2019-08-17,11,500
Middle Management,5,5932.94,2017-11-21,15,600
Upper Management,7,10192.14,2018-08-18,18,700
So here I would want 'Floor' or 4 as my output given that Floor and Room have the same amount of unique values but Floor is the left-most one (I need it in pure python, i can't use pandas)
I have this nested in a whole bunch of other code for what I need to do as a whole, i will spare you the details but these are the used elements in the code:
new_types_list = [str, int, str, datetime.datetime, int, int] #all the datatypes of the columns
l1_listed = ['Position', 'Experience in Years', 'Salary', 'Starting Date', 'Floor', 'Room'] #the header for each column
difference = [3, 5, 9, 9, 6, 7] #is basically the amount of unique values each column has
And here I try to do exactly what I mentioned before:
another_list = [] #now i create another list
for i in new_types_list: # this is where the error occurs, it only fills the list with the index of the first integer 3 times instead of with the individual indices
if i== int:
another_list.append(new_types_list.index(i))
integer_listi = [difference[i] for i in another_list] #and this list is the corresponding unique values from the integers
for i in difference: #now we want to find out the one that is the highest
if i== max(integer_listi):
chosen_one_i = difference.index(i) #the index of the column with the most unique values is the chosen one -
MUV_LMNC = l1_listed[chosen_one_i]
```

Answer 1

You can use .nunique() to get number of unique in each column:
df = pd.read_csv("your_file.csv")
print(df.nunique())
Prints:
Position 3
Experience in Years 5
Salary 9
Starting Date 9
Floor 7
Room 7
dtype: int64
Then to find max, use .idxmax():
print(df.nunique().idxmax())
Prints:
Salary
EDIT: To select only integer columns:
print(df.loc[:, df.dtypes == np.integer].nunique().idxmax())
Prints:
Floor