You are using tolist incorrectly. You want: .values followed by tolist()

  type   tsneX      tsneY  
0  A      53.828863  20.740931  
1  B      57.816909  18.478468  
2  A      55.913429  22.948167  
3  C      56.603005  15.738954 

For the above dataframe, to get your X and Y values as a list you can do:

tsneY_data = df['tsneY'].values.tolist()
>> [20.740931, 18.478468, 22.948167, 15.7389541]

tsneX_data = df['tsneX'].values.tolist()
>> [53.828863, 57.816909, 55.913429, 56.603005]

As you have tried to set this to the column of a new dataframe, you can do:

new_data = pd.DataFrame()
new_data['tsneY'] = df['tsneY'].values.tolist()

> new_data
       tsneY
0  20.740931
1  18.478468
2  22.948167
3  15.738954

You have an XY Problem. You've described what you are trying to achieve in your comments, but your approach is not appropriate for Pandas.

Avoid for loops and list

With Pandas, you should look to avoid explicit for loops or conversion to Python list. Pandas builds on NumPy arrays which support vectorised column-wise operations.

So let's look at how you can rewrite:

for col in columns:
    # values less than 0 set to NaN
    # calculate the mean of the column with 0 for NaN
    # then apply the mean to all NaNs

You can now use Pandas methods to achieve the above.

apply + pd.to_numeric + mask + fillna

You can define a function mean_update and use pd.DataFrame.apply to apply it to each series:

df = pd.DataFrame({'A': [1, -2, 3, np.nan],
                   'B': ['hello', 4, 5, np.nan],
                   'C': [-1.5, 3, np.nan, np.nan]})

def mean_update(s):
    s_num = pd.to_numeric(s, errors='coerce')  # convert to numeric
    s_num = s_num.mask(s_num < 0)              # replace values less than 0 with NaN
    s_mean = s_num.fillna(0).mean()            # calculate mean
    return s_num.fillna(s_mean)                # replace NaN with mean

df = df.apply(mean_update)                     # apply to each series

print(df)

     A     B     C
0  1.0  2.25  0.75
1  1.0  4.00  3.00
2  3.0  5.00  0.75
3  1.0  2.25  0.75

Check the version of your pandas library:

import pandas
print(pandas.__version__)

If your version is less than 0.24.1:

pip install --upgrade pandas

I think you can try ix this way:

df.ix[:, 0].tolist()

And as mentioned DSM in comments, you can use iloc this way, if you need select first column by position:

df.iloc[:, 0].tolist()

Not sure I got what you need done. Please try filter stroke==1, groupby and count;

df.query("Stroke==1").groupby('Residence_type')['Stroke'].agg('count').to_frame('Stroke_Count')

          

                   Stroke_Count
Residence_type              
Rural                      3
Urban                      2

You could try the following if you need the differences between categories

 df1 =df.query("Stroke==1").groupby('Residence_type')['Stroke'].agg('count').to_frame('Stroke_Count')
df1.loc['Diff'] = abs(df1.loc['Rural']-df1.loc['Urban'])
print(df1)
    

                    Stroke_Count
Residence_type              
Rural                      3
Urban                      2
Diff                       1

You might have a data frame with one column. Try squeeze() to coerce it to a pandas Series:

years = (pd.read_excel(r"/Users/vijayaswani/Downloads/years.xlsx")
           .squeeze()
           .tolist())