Looking for conditional string-appended values in csv.reader

I have a vendor payables aging report I'm trying to automate which is provided as a .csv file exported from a financial system. In the report, a line called 'functional amount not invoiced' is listed, followed by a $xx.xx amount for each vendor on the list. Below is an example of the report output (with numbers changed):

1000000 Vendor1 USD PO Number 1/1/1900
Item1, Description 
100 Each $1.00

INV000000 1/1/1900 000 Each 100 0 $1.00 $24.00
0 0  $24.00

INV000001 1/1/1900 000 Each 50 0 $1.00 $10.50
0 0  $10.50

-------------------
Functional Amount Not Invoiced: $250.00
Amount Not Invoiced Less Returned: $250.00


1000001 Vendor2 USD PO2061994 6/2/2015
Item2, Description 30 Each $38.00

INV000002 7/23/2015 000 Each 9 0 $38.00 $342.00
0 0  $342.00


INV000003 7/23/2015 000 Each 7 0 $38.00 $266.00
0 0  $266.00


-------------------
Functional Amount Not Invoiced: $346,955.00
Amount Not Invoiced Less Returned: $1,245.00

I would like to know how I can parse a .csv file for all instances of 'Functional Amount Not Invoiced' greater than or equal to $10,000.00, and in those cases, take the first two strings and return them (in the case above, I would return 1000000 Vendor1). Here's my code so far:

companyList={'1000000':'Vendor1',...}

with open('Vendor Report.csv',mode='r',encoding='latin1') as file:
csvreader=csv.reader(file)
for row in csvreader:
    print(' '.join(row))
    if 'Functional Amount Not Invoiced:' in row:
        ...

I've gotten to the ... part, and I know the logic is 'if amount after string is at least $10,000.00, find the vendor ID and vendor name and return them. The goal would be to have a list of all vendors over $10,000.00 appended automatically to a list. My expected output would be as follows:

Vendor ID Vendor Name $346,955.00
...

IIUC, here is one option with [tag:pandas] by using read_fwf and extract :

#pip install pandas
import pandas as pd

MIN_AMOUNT = 10000

df = pd.read_fwf("input.csv", header=None)
​
vendor_vals = df[0].str.extract(r"(\d+) ([a-zA-Z]+\d+)", expand=False).ffill()
fani_vals = (df.pop(0).str.extract(r"Functional Amount Not Invoiced: $(.*)",
                expand=False).replace({r",|\.0+": ""}, regex=True).astype(float))
​
companyList = (
                df.assign(VENDOR = vendor_vals, FANI = fani_vals).dropna()
                  .loc[lambda df_: df_["FANI"].gt(MIN_AMOUNT)].to_dict("list")
               ) 
​

Output :

>>> print(companyList)

{'VENDOR': ['1000001 Vendor2'], 'FANI': [346955.0]}

Update :

If you need a dataframe (to make a .csv), use this :

df = pd.read_fwf("input.csv", header=None)
​
out = (
        df.join(df[0].str.extract(r"(\d+) ([a-zA-Z]+\d+)")
                .rename(columns={0: "VENDOR_ID", 1:"VENDOR_NAME"}).ffill())
          .assign(FANI = lambda df_: df_.pop(0).str.extract(r"Functional Amount Not Invoiced: $(.*)",
                expand=False).replace({r",|\.0+": ""}, regex=True).astype(float))
          .dropna().loc[lambda df_: df_["FANI"].gt(MIN_AMOUNT)].reset_index(drop=True)
       ) 

Output :
​

>>> print(out)

  VENDOR_ID VENDOR_NAME      FANI
0   1000001     Vendor2  346955.0