Here's the code to drop missing values of the `cust_id` column in `banking`, compute estimated account amounts, impute missing values of `acct_amount`, and print the number of missing values:
```python
# Drop missing values of cust_id
banking_fullid = banking.dropna(subset=['cust_id'])
# Compute estimated acct_amount
acct_imp = banking_fullid['inv_amount'] * 5
# Impute missing acct_amount with corresponding acct_imp
banking_imputed = banking_fullid.fillna({'acct_amount': acct_imp})
# Print number of missing values
print(banking_imputed.isna().sum())
```
In this code, we first use the `.dropna()` function to drop missing values of the `cust_id` column in `banking`. We specify the `subset` parameter as `['cust_id']` to only consider missing values in that column. We store the result in a new DataFrame called `banking_fullid`.
We then compute the estimated account amounts by multiplying the `inv_amount` column by 5, and store the result in a new Series called `acct_imp`.
Finally, we use the `.fillna()` function to impute missing values of `acct_amount` in `banking_fullid` with the corresponding values from `acct_imp`. We pass a dictionary with the key `'acct_amount'` and the value `acct_imp` to the `fillna()` function.
We print the number of missing values in the resulting DataFrame using the `.isna().sum()` method.