How to change font-weight of the text for the row in dataframe that matches the criteria which contains a mixture of float and string values?

First, your data dictionary should be corrected. All nan should be set as np.nan. Something for you have to think about next time when you create data for others to try!

Second, you added the style code with the function which you didn't really show what the function does. You didn't included the function detail with this.

Assuming your first function to style works, the problem with the function is that you're checking this for each column. Since you're using .apply without axis, it defaults to 0 i.e. columns - one column at a time. Since your first column seems to be string, it can't convert the value to string; hence, the error.

So, to fix this, your __highlight_pvalue__ function needs to be changed to check only for pvalue.

data = {'Rest (N=11570)': {4: '7369 (62.28%)', 1: '7016 (59.98%)', 37: '734 (6.14%)', 40: '9829 (79.09%)', 43: '37 (2.42%)', 5: '3080 (25.29%)', 6: '1273 (9.43%)', 12: '6992 (62.61%)', 15: '777 (5.47%)', 18: '332 (2.10%)', 21: '7 (3.44%)', 24: '2013 (14.39%)', 27: '290 (1.81%)', 30: '2048 (14.98%)', 33: '9 (3.85%)', 36: '353 (2.15%)', 351: '0.08 [0.05 - 0.14]', 354: '0.08 [0.05 - 0.13]', 357: '0.08 [0.04 - 0.14]', 168: '4.00 [3.72 - 4.30]', 171: '3.96 [3.62 - 4.27]', 174: '4.22 [3.92 - 4.50]', 177: '3.81 [3.44 - 4.12]', 180: '3.93 [3.60 - 4.20]'}, 'p_value': {4: '<0.001', 1: '0.005', 37: '0.056', 40: '<0.001', 43: '<0.001', 5: '<0.001', 6: '<0.001', 12: '<0.001', 15: '<0.001', 18: '<0.001', 21: '<0.001', 24: '<0.001', 27: '<0.001', 30: '<0.001', 33: '<0.001', 36: '<0.001', 351: '0.366', 354: '<0.001', 357: '0.008', 168: '0.012', 171: '0.004', 174: '0.220', 177: '0.047', 180: '0.025'}, 'ks_score': {4: np.nan, 1: np.nan, 37: np.nan, 40: np.nan, 43: np.nan, 5: np.nan, 6: np.nan, 12: np.nan, 15: np.nan, 18: np.nan, 21: np.nan, 24: np.nan, 27: np.nan, 30: np.nan, 33: np.nan, 36: np.nan, 351: '0.02', 354: '0.05', 357: '0.03', 168: '0.03', 171: '0.03', 174: '0.02', 177: '0.02', 180: '0.03'}}
data = pd.DataFrame(data)

def __highlight_pvalue__(x):
        
    if x.p_value == '<0.001' or float(x.p_value) < 0.05:
        return ['font-weight: bold'] * x.shape[0] # number of colums
    else: 
        return ''
          
      

The resulting dataframe