Detect blank space as cell

[bassoonplayer]

Ricoh USA_MINGOIAS FAXWORLD INC_1099238443_12142023_MULTI.PDF
How do I tell pdfplumber that the blank spaces below each item is a cell?
Thank you for your help.
import pdfplumber

pdf = pdfplumber.open("/home/brian/Desktop/Ricoh USA_MINGOIAS FAXWORLD INC_1099238443_12142023_MULTI.PDF")
page = pdf.pages[1]
tables = page.find_tables()
header_row = tables[0].rows[0].cells
vertical_lines = [cell[0] for cell in header_row] + [header_row[-1][2]]
table_settings = {
"vertical_strategy": "explicit",
"horizontal_strategy": "lines",
"explicit_vertical_lines": vertical_lines, # [Decimal('48.625'), Decimal('399.503'), Decimal('684.000')]

}

im = page.to_image()

im.debug_tablefinder(table_settings).annotated.show()

[bassoonplayer]

`import re
from pprint import pprint
import pdfplumber
import pandas as pd
import numpy as np

pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)
pd.set_option('display.width', 1000)

pdf = pdfplumber.open("/home/brian/Desktop/Ricoh USA_MINGOIAS FAXWORLD INC_1099238443_12142023_MULTI.PDF")
table_settings = {
"vertical_strategy": "lines",
"horizontal_strategy": "text",
"snap_y_tolerance": 5,
"intersection_x_tolerance": 15,
}
for page in pdf.pages:
tables = page.find_tables(table_settings)
t1 = tables[0].extract(x_tolerance = 5)
t2 = tables[1].extract(x_tolerance = 5)

    #print(text)

df1 = pd.DataFrame(t1[1:2], columns=t1[0])
df1 = df1.filter(regex='Equi|Tot|Inv')
df1 = pd.DataFrame(df1)

print (df1)
df2 = pd.DataFrame(t2[1:], columns=t2[0])
df2 = df2.filter(regex='Equi|Tot|Inv')
b = df2['Equipment Details'].str.startswith(tuple('0123456789')).cumsum()
df2 = df2.groupby(b, as_index=False).agg({'Equipment Details': ' '.join, 'Total': 'first'})
df2 = pd.DataFrame(df2)
df2 = df2.iloc[1:]

print (df2)

df3 = pd.concat([df1, df2])

df3 = df3.reset_index(drop=True)

df3['Inv#'] = df3['Invoice Number'].ffill()
df3['Date'] = df3['Invoice Date'].ffill()
df3 = df3.iloc[1:]

print (df3)

df3['Account']= np.where(df3['Equipment Details'].str.contains("-RM|LT|CAB|BU|PB|SR|Serial"), '4305',
np.where(df3['Equipment Details'].str.contains("SURCH|SHIP"), '4420',
np.where(df3['Equipment Details'].str.contains("STAPLE|TONER|PRINT|PHOTO|PCU"), '4330', '4318')))

df3['Total'] .replace(',','', regex=True, inplace=True)
df3['Amt'] = pd.to_numeric(df3['Total'], errors='coerce')

export = df3.groupby(['Inv#','Account','Date']).sum()
print (export)

im = page.to_image()
im.reset().debug_tablefinder(table_settings).annotated.show()

`