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How can I update my Dataframe with new columns and rows while webscraping?

I'm trying to create a webscraping tool that will update a Dataframe with data from multiple tables.
The page I'm working on has a base table in which every row has a link that directs you to a new URL that has a secondary table with the data I'm looking for.
My objective is to create a unique Dataframe comprehensive of all the data present on all secondary tables of the site.
Problem is, every secondary table can have different sets of columns from the previous one, depending on whether that secondary table has a value for that specific column or not, and I cannot know all the possibile column types
I tried multiple solutions. What I'm working on at the moment is to create a for loop that constantly create a new Dataframe out of the new tables and merge them to the previous one.
But I'm stuck on trying to merge the two Dataframes on all the columns they have in common.
Please forgive me if I made amateur mistakes, I've been using python only for a week.
#create the main DataFrame
link1= links[0]
url_linked = url_l + link1
page_linked = requests.get (url_linked)
soup_linked = BeautifulSoup(page_linked.text,'lxml')
table_linked= soup_linked.find('table', class_="XXXXX")
headers_link=[]
headers_unique=[]
for i in table_linked.find_all('th'):
title_link=i.text
title_link=map(str,title_link)
headers_link.append(title_link)
headers_unique=headers_link
mydata_link = pd.DataFrame(columns=headers_link)
count = 1
for link in links:
url_linked = url_l + link
page_linked = requests.get (url_linked)
soup_linked = BeautifulSoup(page_linked.text,'lxml')
table_linked= soup_linked.find('table', class_="table table-directory-responsive")
row2=[]
n_columns =len(table_linked.find_all('th'))
#populating the main dataframe
if count == 1:
for j in table_linked.find_all('tr'):
row_data=j.find_all('td')
row=[i.text for i in row_data]
row2.append(row)
lenght_link= len(mydata_link)
row2.remove(['']) #To get rid of empty rows that have no th
mydata_link.loc[lenght_link]=row2
print(mydata_link)
print('Completato link '+ str(count))
count= count+1
#creating the secondary Dataframe
else:
headers_test=[]
for i in table_linked.find_all('th'):
title_test=i.text
title_test=map(str,title_test)
headers_test.append(title_test)
mydata_temp=pd.DataFrame(columns=headers_test)
for j in table_linked.find_all('tr'):
row_data=j.find_all('td')
row=[i.text for i in row_data]
row2.append(row)
lenght_link= len(mydata_link)
row2.remove(['']) #To get rid of empty rows that have no th
mydata_temp.loc[lenght_link]=row2
print(mydata_temp)
#merge the two DataFrames based on the unique set of columns they both have
headers_unique= set(headers_unique).intersection(headers_test)
mydata_link=mydata_link.merge(mydata_temp, on=[headers_unique], how='outer')
print(mydata_link)
print('Completed link '+ str(count))
count= count+1
What I need is basically a function that, given these sample dataFrames:
A
B
C
1
2
3
C
A
D
E
4
5
6
7
Will return the following dataframe:
A
B
C
D
E
1
2
3
Nan
Nan
5
Nan
4
6
7

Just concatenating all the secondary tables should do - build a list of all the secondary DataFrames, and then pd.concat(dfList).
Btw, have you considered just using .read_html instead of looping through the cells?
#create the main DataFrame
link1 = links[0]
url_linked = url_l + link1
page_linked = requests.get (url_linked)
soup_linked = BeautifulSoup(page_linked.text, 'lxml')
table_linked = soup_linked.find('table', class_="XXXXX")
if table_linked:
primaryDf = pd.read_html(table_linked.prettify())[0]
headers_link = [h.get_text(' ').strip() for h in table_linked.find_all('th')]
dfList = [pd.DataFrame(columns=headers_link if headers_link else primaryDf.columns)]
else: primaryDf, dfList = None, []
count = 0
for link in links:
count += 1
url_linked = url_l + link
page_linked = requests.get (url_linked)
soup_linked = BeautifulSoup(page_linked.text, 'lxml')
table_linked = soup_linked.find('table', class_="table table-directory-responsive")
if not table_linked:
## to see if any response errors or redirects
print(f'[{page_linked.status_code} {page_linked.reason} from {page_linked.url}]')
## print error message and move to next link
print(f'Found no tables with required class at link#{count}', url_linked)
continue
tempDf = pd.read_html(table_linked.prettify())[0] ## read table as df [if found]
## get rid of empty rows and empty columns
tempDf = tempDf.dropna(axis='rows', 'how'='all').dropna(axis='columns', 'how'='all')
dfList.append(tempDf.loc[:]) ## .loc[:] to append a copy, not original (just in case)
print(f'Completed link#{count} with {len(tempDf)} rows from {url_linked}')
combinedDF = pd.concat(dfList)

Pandas finding a text in row and assign a dummy variable value based on this

I have a data frame which contains a text column i.e. df["input"],
I would like to create a new variable which checks whether df["input"] column contains any of the word in a given list and assigns a value of 1 if previous dummy variable is equal to 0 (logic is 1) create a dummy variable that equals to zero 2) replace it to one if it contains any word in a given list and it was not contained in the previous lists.)
# Example lists
listings = ["amazon listing", "ecommerce", "products"]
scripting = ["subtitle", "film", "dubbing"]
medical = ["medical", "biotechnology", "dentist"]
df = pd.DataFrame({'input': ['amazon listing subtitle',
'medical',
'film biotechnology dentist']})
which looks like:
input
amazon listing subtitle
medical
film biotechnology dentist
final dataset should look like:
input listings scripting medical
amazon listing subtitle 1 0 0
medical 0 0 1
film biotechnology dentist 0 1 0

One possible implementation is to use str.contains in a loop to create the 3 columns, then use idxmax to get the column name (or the list name) of the first match, then create a dummy variable from these matches:
import numpy as np
d = {'listings':listings, 'scripting':scripting, 'medical':medical}
for k,v in d.items():
df[k] = df['input'].str.contains('|'.join(v))
arr = df[list(d)].to_numpy()
tmp = np.zeros(arr.shape, dtype='int8')
tmp[np.arange(len(arr)), arr.argmax(axis=1)] = arr.max(axis=1)
out = pd.DataFrame(tmp, columns=list(d)).combine_first(df)
But in this case, it might be more efficient to use a nested for-loop:
import re
def get_dummy_vars(col, lsts):
out = []
len_lsts = len(lsts)
for row in col:
tmp = []
# in the nested loop, we use the any function to check for the first match
# if there's a match, break the loop and pad 0s since we don't care if there's another match
for lst in lsts:
tmp.append(int(any(True for x in lst if re.search(fr"\b{x}\b", row))))
if tmp[-1]:
break
tmp += [0] * (len_lsts - len(tmp))
out.append(tmp)
return out
lsts = [listings, scripting, medical]
out = df.join(pd.DataFrame(get_dummy_vars(df['input'], lsts), columns=['listings', 'scripting', 'medical']))
Output:
input listings medical scripting
0 amazon listing subtitle 1 0 0
1 medical 0 1 0
2 film biotechnology dentist 0 0 1

Here is a simpler - more pandas vector style solution:
patterns = {} #<-- dictionary
patterns["listings"] = ["amazon listing", "ecommerce", "products"]
patterns["scripting"] = ["subtitle", "film", "dubbing"]
patterns["medical"] = ["medical", "biotechnology", "dentist"]
df = pd.DataFrame({'input': ['amazon listing subtitle',
'medical',
'film biotechnology dentist']})
#---------------------------------------------------------------#
# step 1, for each column create a reg-expression
for col, items in patterns.items():
# create a regex pattern (word1|word2|word3)
pattern = f"({'|'.join(items)})"
# find the pattern in the input column
df[col] = df['input'].str.contains(pattern, regex=True).astype(int)
# step 2, if the value to the left is 1, change its value to 0
## 2.1 create a mask
## shift the rows to the right,
## --> if the left column contains the same value as the current column: True, otherwise False
mask = (df == df.shift(axis=1)).values
# substract the mask from the df
## and clip the result --> negative values will become 0
df.iloc[:,1:] = np.clip( df[mask].iloc[:,1:] - mask[:,1:], 0, 1 )
print(df)
Result
input listings scripting medical
0 amazon listing subtitle 1 0 0
1 medical 0 0 1
2 film biotechnology dentist 0 1 0

Great question and good answers (I somehow missed it yesterday)! Here's another variation with .str.extractall():
search = {"listings": listings, "scripting": scripting, "medical": medical, "dummy": []}
pattern = "|".join(
f"(?P<{column}>" + "|".join(r"\b" + s + r"\b" for s in strings) + ")"
for column, strings in search.items()
)
result = (
df["input"].str.extractall(pattern).assign(dummy=True).groupby(level=0).any()
.idxmax(axis=1).str.get_dummies().drop(columns="dummy")
)

Openpyxl - combine matching rows of two tables into one long row

In an Excel file I have two large tables. Table A ("Dissection", 409 rows x 25 cols) contains unique entries, each separated by a unique ID. Table B ("Dissection", 234 rows x 39 columns) uses the ID of Table A in the first cell and extends it. To analyze the data in Minitab, all data must be in a single long row, meaning the values of "Damage" have to follow "Dissection". The whole thing looks like this:
Table A - i.e. Dissection
- ID1 [valueTabA] [valueTabA]
- ID2 [valueTabA] [valueTabA]
- ID3 [valueTabA] [valueTabA]
- ID4 [valueTabA] [valueTabA]
Table B - i.e. Damage
- ID1 [valueTabB1] [valueTabB1]
- ID1 [valueTabB2] [valueTabB2]
- ID4 [valueTabB] [valueTabB]
They are supposed to combine something like this:
Table A
- ID1 [valueTabA] [valueTabA] [valueTabB1] [valueTabB1] [valueTabB2] [valueTabB2]
- ID2 [valueTabA] [valueTabA]
- ID3 [valueTabA] [valueTabA]
- ID4 [valueTabA] [valueTabA] [valueTabB] [valueTabB]
What is the best way to do that?
The following describes my two approaches. Both use the same data in the same tables but in two different files, to be able to test both scenarios.
The first approach uses a file, where both tables are in the same worksheet, the second uses a file where both tables are in different worksheets.
Scenario: both tables are in the same worksheet, where I'm trying to move the row as a range
current_row = 415 # start without headers of table A
current_line = 2 # start without headers of table B
for row in ws.iter_rows(min_row=415, max_row=647):
# loop through damage
id_A = ws.cell(row=current_row, column=1).value
max_col = 25
for line in ws.iter_rows(min_row=2, max_row=409):
# loop through dissection
id_B = ws.cell(row=current_line, column=1).value
if id_A == id_B:
copy_range = ((ws.cell(row=current_line, column=2)).column_letter + str(current_line) + ":" +
(ws.cell(row=current_line, column=39)).column_letter + str(current_line))
ws.move_range(copy_range, rows=current_row, cols=max_col+1)
print("copied range: " + copy_range +" to: " + str(current_row) + ":"+str(max_col+1))
count += 1
break
if current_line > 409:
current_line = 2
else:
current_line += 1
current_row += 1
-> Here I'm struggling to append the range to the right row of Table A, without overwriting the previous row (see example ID1 above)
Scenario: both tables are located in separated sheets
dissection = wb["Dissection"]
damage = wb["Damage"]
recovery = wb["Recovery"]
current_row, current_line = 2, 2
for row in damage.iter_rows():
# loop through first table
id_A = damage.cell(row=current_row, column=1).value
for line in dissection.iter_rows():
# loop through second table
id_B = dissection.cell(row=current_line, column=1).value
copyData = []
if id_A == id_B:
for col in range(2, 39):
# add data to the list, skipping the ID
copyData.append(damage.cell(row=current_line, column=col).value)
# print(copyData) for debugging purposes
for item in copyData:
column_count = dissection.max_column
dissection.cell(row=current_row, column=column_count).value = item
column_count += 1
current_row += 1
break
if not current_line > 409:
# prevent looping out of range
current_line += 1
else:
current_line = 2
-> Same problem as in 1., at some point it's not adding the damage values to copyData anymore but None instead, and finally it's just not pasting the items (cells stay blank)
I've tried everything excel related that I could find, but unfortunately nothing worked. Would pandas be more useful here or am I just not seeing something?
Thanks for taking the time to read this :)

I highly recommend using pandas for situations like this. It is still a bit unclear how your data is formatted in the excel file, but given your second option I assume that the tables are both on different sheets in the excel file. I also assume that the first row contains the table title (e.g. Table A - i.e. Dissection). If this is not the case, just remove skiprows=1:
import pandas as pd
df = pd.concat(pd.read_excel("filename.xlsx", sheet_name=None, skiprows=1, header=None), axis=1, ignore_index=True)
df.to_excel('combined_data.xlsx) #save to excel
read_excel will load the excel file into a pandas dataframe. sheet_name=None indicates that all sheets should be loaded into an OrderedDict of dataframes. pd.concat will concatenate these dataframes into one single dataframe (axis=1 indicates the axis). You can explore the data with df.head(), or save the dataframe to excel with df.to_excel.

I ended up using the 2. scenario (one file, two worksheets) but this code should be adaptable to the 1. scenario (one file, one worksheet) as well.
I copied the rows of Table B using code taken from here.
And handled the offset with code from here.
Also, I added a few extras to my solution to make it more generic:
import openpyxl, os
from openpyxl.utils import range_boundaries
# Introduction
print("Welcome!\n[!] Advice: Always have a backup of the file you want to sort.\n[+] Please put the file to be sorted in the same directory as this program.")
print("[+] This program assumes that the value to be sorted by is located in the first column of the outgoing table.")
# File listing
while True:
files = [f for f in os.listdir('.') if os.path.isfile(f)]
valid_types = ["xlsx", "xltx", "xlt", "xls"]
print("\n[+] Current directory: " + os.getcwd())
print("[+] Excel files in the current directory: ")
for f in files:
if str(f).split(".")[1] in valid_types:
print(f)
file = input("\nWhich file would you like to sort: ")
try:
ending = file.split(".")[1]
except IndexError:
print("please only enter excel files.")
continue
if ending in valid_types:
break
else:
print("Please only enter excel files")
wb = openpyxl.load_workbook(file)
# Handling Worksheets
print("\nAvailable Worksheets: " + str(wb.sheetnames))
print("Which file would you like to sort? (please copy the name without the parenthesis)")
outgoing_sheet = wb[input("Outgoing sheet: ")]
print("\nAvailable Worksheets: " + str(wb.sheetnames))
print("Which is the receiving sheet? (please copy the name without the parenthesis)")
receiving_sheet = wb[input("Receiving sheet: ")]
# Declaring functions
def copy_row(source_range, target_start, source_sheet, target_sheet):
# Define start Range(target_start) in the new Worksheet
min_col, min_row, max_col, max_row = range_boundaries(target_start)
# Iterate Range you want to copy
for row, row_cells in enumerate(source_sheet[source_range], min_row):
for column, cell in enumerate(row_cells, min_col):
# Copy Value from Copy.Cell to given Worksheet.Cell
target_sheet.cell(row=row, column=column).value = cell.value
def ask_yes_no(prompt):
"""
:param prompt: The question to be asked
:return: Value to check
"""
while True:
answer = input(prompt + " (y/n): ")
if answer == "y":
return True
elif answer == "n":
return False
print("Please only enter y or n.")
def ask_integer(prompt):
while True:
try:
answer = int(input(prompt + ": "))
break
except ValueError:
print("Please only enter integers (e.g. 1, 2 or 3).")
return answer
def scan_empty(index):
print("Scanning for empty cells...")
scan, fill = False, False
min_col = outgoing_sheet.min_column
max_col = outgoing_sheet.max_column
cols = range(min_col, max_col+1)
break_loop = False
count = 0
if not scan:
search_index = index
for row in outgoing_sheet.iter_rows():
for n in cols:
cell = outgoing_sheet.cell(row=search_index, column=n).value
if cell:
pass
else:
choice = ask_yes_no("\n[!] Empty cells found, would you like to fill them? (recommended)")
if choice:
fill = input("Fill with: ")
scan = True
break_loop = True
break
else:
print("[!] Attention: This can produce to mismatches in the sorting algorithm.")
confirm = ask_yes_no("[>] Are you sure you don't want to fill them?\n[+] Hint: You can also enter spaces.\n(n)o I really don't want to\noka(y) I'll enter something, just let me sort already.\n")
if confirm:
fill = input("Fill with: ")
scan = True
break_loop = True
break
else:
print("You have chosen not to fill the empty cells.")
scan = True
break_loop = True
break
if break_loop:
break
search_index += 1
if fill:
search_index = index
for row in outgoing_sheet.iter_rows(max_row=outgoing_sheet.max_row-1):
for n in cols:
cell = outgoing_sheet.cell(row=search_index, column=n).value
if cell:
pass
elif cell != int(0):
count += 1
outgoing_sheet.cell(row=search_index, column=n).value = fill
search_index += 1
print("Filled " + str(count) + " cells with: " + fill)
return fill, count
# Declaring basic variables
first_value = ask_yes_no("Is the first row containing values the 2nd in both tables?")
if first_value:
current_row, current_line = 2, 2
else:
current_row = ask_integer("Sorting table first row")
current_line = ask_integer("Receiving table first row")
verbose = ask_yes_no("Verbose output?")
reset = current_line
rec_max = receiving_sheet.max_row
scan_empty(current_row)
count = 0
print("\nSorting: " + str(outgoing_sheet.max_row - 1) + " rows...")
for row in outgoing_sheet.iter_rows():
# loop through first table - Table you want to sort
id_A = outgoing_sheet.cell(row=current_row, column=1).value
if verbose:
print("\nCurrently at: " + str(current_row - 1) + "/" + str(outgoing_sheet.max_row - 1) + "")
try:
print("Sorting now: " + id_A)
except TypeError:
# Handling None type exceptions
pass
for line in receiving_sheet.iter_rows():
# loop through second table - The receiving table
id_B = receiving_sheet.cell(row=current_line, column=1).value
if id_A == id_B:
try:
# calculate the offset
offset = max((row.column for row in receiving_sheet[current_line] if row.value is not None)) + 1
except ValueError:
# typical "No idea why, but it doesn't work without it" - code
pass
start_paste_from = receiving_sheet.cell(row=current_line, column=offset).column_letter + str(current_line)
copy_Range = ((outgoing_sheet.cell(row=current_row, column=2)).column_letter + str(current_row) + ":" +
(outgoing_sheet.cell(row=current_row, column=outgoing_sheet.max_column)).column_letter + str(current_row))
# Don't copy the ID, alternatively set damage.min_column for the first and damage.max_column for the second
copy_row(copy_Range, start_paste_from, outgoing_sheet, receiving_sheet)
count += 1
current_row += 1
if verbose:
print("Copied " + copy_Range + " to: " + str(start_paste_from))
break
if not current_line > rec_max:
# prevent looping out of range
current_line += 1
else:
current_line = reset
wb.save(file)
print("\nSorted: " + str(count) + " rows.")
print("Saving the file to: " + os.getcwd())
print("Done.")
Note: The values of table B ("Damage") are sorted according to the ID, although that is not required. However, if you choose to do so, this can be done using pandas.
import pandas as pd
df = pd.read_excel("excel/separated.xlsx","Damage")
# open the correct worksheet
df.sort_values(by="Identification")
df.to_excel("sorted.xlsx")

Looping Over Each Unique Value

New to python and any help on this would be very much appreciated.
Ok, here's the scenario:
I have a data set that includes customer name, order number, number of evaporators on that specific order number and number of condensers on that specific order number. My objective is to calculate the percentage of orders that include an evaporator and condenser for each customer.
This is the code I came up with so far. The problem is that I can only specify one customer at a time this way. How do I make this run for each unique customer name?
DEvap = []
DComp = []
Evap_Comp = []
for row in data[1:]:
Customer_Name = row[0]
Sales_order_number = row[1]
Compressor = int(row[2])
Evaporator = int(row[3])
Value = int(row[4])
if Customer_Name == "AIRCOOLER CORPORATION" and Evaporator > 0:
DEvap.append(Value)
if Customer_Name == "AIRCOOLER CORPORATION" and Evaporator > 0 and Compressor >0:
Evap_Comp.append(Value)
total_evaporators = sum(DEvap)
total_evap_comp = sum(Evap_Comp)
attachment = total_evap_comp/total_evaporators
Here is a snippet of the data set. It includes a column labeled value which I used to append the empty list with.

So you want to only loop through unique customers. This can be accomplished with the use of a set to store the names of customers you already looped through and a continue statement to skip over the repeat customers.
Something like this
customers = set()
for row in data[1:]:
name = row[0]
orderNo = row[1]
compressor = int(row[2])
evaporator = int(row[3])
value = int(row[4])
if name in customers:
continue
customers.add(name)
# Check no of Compressors and Evaporators here
# If customers have placed multiple orders, only their first one will be checked here
Edit: In reply to your comment, the only way I could think of doing this without modifying the original data set is to create a new data set with the customers name and total orders.
Something like this
totalOrders= {}
for row in data[1:]:
name = row[0]
compressor = int(row[2])
evaporator = int(row[3])
if name in totalOrders:
totalOrders[name][0] += compressor
totalOrders[name][1] += evaporator
else:
totalOrders[name] = [compressor, evaporator]
This code creates a dictionary keyed by customer name with the value being a list [compressors ordered, evaporators ordered]
Multiple orders by the same customer will only update their existing entry in the dictionary. Then its a simple matter of looping through the dictionary and checking if both list values are > 0.
Edit 2:
Sure there is a way to do that:
totalOrders= {}
for row in data[1:]:
name = row[0]
orderNo = row[1]
compressor = int(row[2])
evaporator = int(row[3])
if name not in totalOrders:
totalOrders[name] = []
totalOrders[name].append([orderNo, compressor, evaporator])
Here you have a dictionary keyed by customer name with values being a list of lists.
I would be careful with multi-layered data structures, however. If they get too complex, it can make your code nigh unreadable and make making minor changes to the code quite difficult. Something like this shouldn't be much of a problem, though.
Edit 3:
In your original question, you stated you wanted to find the percentage of customers who bought both a condenser and an evaporator.
This would actually easier to do with the dictionary in my first Edit.
totalOrders= {}
for row in data[1:]:
name = row[0]
compressor = int(row[2])
evaporator = int(row[3])
if name in totalOrders:
totalOrders[name][0] += compressor
totalOrders[name][1] += evaporator
else:
totalOrders[name] = [compressor, evaporator]
evapCond = 0
for customer in totalOrders:
if totalOrders[customer][0] > 0 and totalOrdrs[customer][1] > 0:
evapCond += 1
percentBoth = evapCond/len(totalOrders) * 100
Here percentBoth would be the percentage of customers who bought both a condenser and an evaporator
To do this with the dictionary in my second edit, you would need a nested for loop
totalOrders= {}
for row in data[1:]:
name = row[0]
orderNo = row[1]
compressor = int(row[2])
evaporator = int(row[3])
if name not in totalOrders:
totalOrders[name] = []
totalOrders[name].append([orderNo, compressor, evaporator])
evapCond = 0
for customer in totalOrders:
cond = False
evap = False
for order in totalOrders[customer]:
if totalOrders[customer][order][2] > 0:
cond = True
if totalOrders[customer][order][3] > 0:
evap = True
if cond and evap:
evapCond += 1
percentBoth = evapCond/len(totalOrders) * 100

Python 3 openpyxl finding all of string in column

I am just starting to learn python and am looking for some direction on a script I am working on to text out daily pick up for my drivers. The vendor name is entered into a spreadsheet along with a purchase order # and notes. What i would like to do is cycle through column "A", find all instances of a vendor name, grab the corresponding B & C cell values and save all info to a text file. I can get it to work if I name the search string explicitly but not if its a variable. Here is what I have so far:
TestList=[]
TestDict= {}
LineNumber = 0
for i in range(1, maxrow + 1):
VendorName = sheet.cell(row = i, column = 1)
if VendorName.value == "CERTIFIED LETTERING":#here is where im lost
#print (VendorName.coordinate)
VendLoc = str(VendorName.coordinate)
TestList.append(VendLoc)
TestDict[VendorName.value]=[TestList]
test = (TestDict["CERTIFIED LETTERING"][0])
ListLength = (len(test))
ListPo = []
List_Notes = []
number = 0
for i in range (0, ListLength):
PO = (str('B'+ test[number][1]))
Note = (str('C'+ test[number][1]))
ListPo.append(PO)
List_Notes.append(Note)
number = number + 1
number = 0
TestVend =(str(VendorName.value))
sonnetFile = open('testsaveforpickups.txt', 'w')
sonnetFile.write("Pick up at:" + '\n')
sonnetFile.write(str(VendorName.value)+'\n')
for i in range (0, ListLength):
sonnetFile.write ("PO# "+ str(sheet[ListPo[number]].value)+'\n'
+"NOTES: " + str(sheet[List_Notes[number]].value)+'\n')
number = number + 1
sonnetFile.close()
the results are as follows:
Pick up at:
CERTIFIED LETTERING
PO# 1111111-00
NOTES: aaa
PO# 333333-00
NOTES: ccc
PO# 555555-00
NOTES: eee
I've tried everything i could think of to change the current string of "CERTIFIED LETTERING" to a variable name, including creating a list of all vendors in column A and using that as a dictionary to go off of. Any help or ideas to point me in the right direction would be appreciated. And I apologise for any formatting errors. I'm new to posting here.