How to clean a dataset having string values?

I am working on a dataset where few values in one of the column are string. due to that i am getting error while performing operations on dataset.

sample dataset:-

1.99    LOHARU  0.3 2   0   2   0.3 5   2   0   2   2
1.99    31  0.76    2   0   2   0.76    5   2   7.48    4   2
1.99    4   0.96    2   0   2   0.96    5   2   9.45    4   2
1.99    14  1.26    4   0   2   1.26    5   2   0   2   2
1.99    NUH 0.55    2   0   2   0.55    5   2   0.67    2   2
1.99    99999   0.29    2   0   2   0.29    5   2   0.06    2   2

full dataset can be found here:- https://www.kaggle.com/sid321axn/audit-data?select=trial.csv

I need to found the missing values and outlier in the dataset. below is the code i am using to find missing values:-

#Replacing zeros and 99999 with `np.NaN`

dataset[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]]=dataset[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]].replace(99999,np.NaN)

#if 12,14 and 17 can have zeroes then
dataset[[0,1,2,3,4,5,6,7,8,9,10,11,13,15,16]]=dataset[[0,1,2,3,4,5,6,7,8,9,10,11,13,15,16]].replace(0,np.NaN)

print(Dataset.isnull().sum())

but this doesn't replace 99999 with NaN

and to find outlier:-

i am calculating zscore

import scipy.stats as stats
array = Dataset.values
Z=stats.zscore(array)

but it gives me below error:

- TypeError: unsupported operand type(s) for /: 'str' and 'int'

source https://stackoverflow.com/questions/69381821/how-to-clean-a-dataset-having-string-values