ARIMA Forecasting
I don't know exactly about your specific domain problem, but these things apply usually in general:

If the NA values represent 0 values for your domain specific problem, then replace them with 0 and then fit the ARIMA model (this would for example be the case if you are looking at daily sales and on some days you have 0 sales)
If the NA values represent unknown values for your domain specific problem then do not replace them and fit your ARIMA model. (this would be the case, if on a specific day the employee forgot to write down the amount of sales and it could be any number). 

I probably would not use imputation at all. There are methods to fit an ARIMA model on time series that have missing values. Usually these algorithms should probably also implemented somewhere in python. (but I don't know since I am mostly using R)
I have a time series data which looks something like this
Loan_id Loan_amount Loan_drawn_date
  id_001    2000000    2015-7-15
  id_003     100       2014-7-8
  id_009     78650     2012-12-23
  id_990      100      2018-11-12
I am trying to build a Arima forecasting model on this data which has round about 550  observations. These are the steps i have followed     

Converted the time series data into daily data and replaced NA values with 0. the data look something like this           
Loan_id Loan_amount Loan_drawn_date 
id_001    2000000    2015-7-15
id_001    0          2015-7-16
id_001    0          2015-7-17
id_001    0          2015-7-18
id_001    0          2015-7-19
id_001    0          2015-7-20
....
id_003     100       2014-7-8
id_003     0       2014-7-9
id_003     0       2014-7-10
id_003     0        2014-7-11
id_003     0       2014-7-12
id_003     0       2014-7-13
....
id_009     78650     2012-12-23
id_009     0     2012-12-24
id_009     0     2012-12-25
id_009     0     2012-12-26
id_009     0     2012-12-27
id_009     0     2012-12-28
...
id_990      100      2018-11-12
id_990      0      2018-11-13
id_990      0      2018-11-14
id_990      0      2018-11-15
id_990      0      2018-11-16
id_990      0      2018-11-17
id_990      0      2018-11-18
id_990      0      2018-11-19
Can Anyone please suggest me how do i proceed ahead with these 0 values now?
Seeing the variance in the loan amount numbers i would take log of the of the loan amount. i am trying to build the ARIMA model for the first time and I have read about all the methods of imputation but there is nothing i can find. Can anyone please tell me how do i proceed ahead in this data