PDF Extraction Skill Overview This skill enables precise extraction of text, tables, and metadata from PDF documents using pdfplumber - the go-to library for PDF data extraction. Unlike basic PDF readers, pdfplumber provides detailed character-level positioning, accurate table detection, and visual debugging. How to Use Provide the PDF file you want to extract from Specify what you need: text, tables, images, or metadata I'll generate pdfplumber code and execute it Example prompts: "Extract all tables from this financial report" "Get text from pages 5-10 of this document" "Find and extract the invoice total from this PDF" "Convert this PDF table to CSV/Excel" Domain Knowledge pdfplumber Fundamentals import pdfplumber
Open PDF
with pdfplumber . open ( 'document.pdf' ) as pdf :
Access pages
first_page
pdf . pages [ 0 ]
Document metadata
print ( pdf . metadata )
Number of pages
print ( len ( pdf . pages ) ) PDF Structure PDF Document ├── metadata (title, author, creation date) ├── pages[] │ ├── chars (individual characters with position) │ ├── words (grouped characters) │ ├── lines (horizontal/vertical lines) │ ├── rects (rectangles) │ ├── curves (bezier curves) │ └── images (embedded images) └── outline (bookmarks/TOC) Text Extraction Basic Text with pdfplumber . open ( 'document.pdf' ) as pdf :
Single page
text
pdf . pages [ 0 ] . extract_text ( )
All pages
full_text
'' for page in pdf . pages : full_text += page . extract_text ( ) or '' Advanced Text Options
With layout preservation
text
page . extract_text ( x_tolerance = 3 ,
Horizontal tolerance for grouping
y_tolerance
3 ,
Vertical tolerance
layout
True ,
Preserve layout
x_density
7.25 ,
Chars per unit width
y_density
13
Chars per unit height
)
Extract words with positions
words
page . extract_words ( x_tolerance = 3 , y_tolerance = 3 , keep_blank_chars = False , use_text_flow = False )
Each word includes: text, x0, top, x1, bottom, etc.
for word in words : print ( f" { word [ 'text' ] } at ( { word [ 'x0' ] } , { word [ 'top' ] } )" ) Character-Level Access
Get all characters
chars
page . chars for char in chars : print ( f"' { char [ 'text' ] } ' at ( { char [ 'x0' ] } , { char [ 'top' ] } )" ) print ( f" Font: { char [ 'fontname' ] } , Size: { char [ 'size' ] } " ) Table Extraction Basic Table Extraction with pdfplumber . open ( 'report.pdf' ) as pdf : page = pdf . pages [ 0 ]
Extract all tables
tables
page . extract_tables ( ) for i , table in enumerate ( tables ) : print ( f"Table { i + 1 } :" ) for row in table : print ( row ) Advanced Table Settings
Custom table detection
table_settings
{ "vertical_strategy" : "lines" ,
or "text", "explicit"
"horizontal_strategy" : "lines" , "explicit_vertical_lines" : [ ] ,
Custom line positions
"explicit_horizontal_lines" : [ ] , "snap_tolerance" : 3 , "snap_x_tolerance" : 3 , "snap_y_tolerance" : 3 , "join_tolerance" : 3 , "edge_min_length" : 3 , "min_words_vertical" : 3 , "min_words_horizontal" : 1 , "intersection_tolerance" : 3 , "text_tolerance" : 3 , "text_x_tolerance" : 3 , "text_y_tolerance" : 3 , } tables = page . extract_tables ( table_settings ) Table Finding
Find tables (without extracting)
table_finder
page . find_tables ( ) for table in table_finder : print ( f"Table at: { table . bbox } " )
(x0, top, x1, bottom)
Extract specific table
data
table . extract ( ) Visual Debugging
Create visual debug image
im
page . to_image ( resolution = 150 )
Draw detected objects
im . draw_rects ( page . chars )
Character bounding boxes
im . draw_rects ( page . words )
Word bounding boxes
im . draw_lines ( page . lines )
Lines
im . draw_rects ( page . rects )
Rectangles
Save debug image
im . save ( 'debug.png' )
Debug tables
im . reset ( ) im . debug_tablefinder ( ) im . save ( 'table_debug.png' ) Cropping and Filtering Crop to Region
Define bounding box (x0, top, x1, bottom)
bbox
( 0 , 0 , 300 , 200 )
Crop page
cropped
page . crop ( bbox )
Extract from cropped area
text
cropped . extract_text ( ) tables = cropped . extract_tables ( ) Filter by Position
Filter characters by region
def within_bbox ( obj , bbox ) : x0 , top , x1 , bottom = bbox return ( obj [ 'x0' ]
= x0 and obj [ 'x1' ] <= x1 and obj [ 'top' ] = top and obj [ 'bottom' ] <= bottom ) bbox = ( 100 , 100 , 400 , 300 ) filtered_chars = [ c for c in page . chars if within_bbox ( c , bbox ) ] Filter by Font
Get text by font
def extract_by_font ( page , font_name ) : chars = [ c for c in page . chars if font_name in c [ 'fontname' ] ] return '' . join ( c [ 'text' ] for c in chars )
Extract bold text (often "Bold" in font name)
bold_text
extract_by_font ( page , 'Bold' )
Extract by size
large_chars
[ c for c in page . chars if c [ 'size' ]
14 ] Metadata and Structure with pdfplumber . open ( 'document.pdf' ) as pdf :
Document metadata
meta
pdf . metadata print ( f"Title: { meta . get ( 'Title' ) } " ) print ( f"Author: { meta . get ( 'Author' ) } " ) print ( f"Created: { meta . get ( 'CreationDate' ) } " )
Page info
- for
- i
- ,
- page
- in
- enumerate
- (
- .
- pages
- )
- :
- (
- f"Page
- {
- i
- +
- 1
- }
- :
- {
- page
- .
- width
- }
- x
- {
- page
- .
- height
- }
- "
- )
- (
- f" Rotation:
- {
- page
- .
- rotation
- }
- "
- )
- Best Practices
- Debug Visually
-
- Use
- to_image()
- to understand PDF structure
- Tune Table Settings
-
- Adjust tolerances for your specific PDF
- Handle Scanned PDFs
-
- Use OCR first (this skill is for native text)
- Process Page by Page
-
- For large PDFs, avoid loading all at once
- Check for Text
- Some PDFs are images - verify text exists
Common Patterns
Extract All Tables to DataFrames
import
pandas
as
pd
def
pdf_tables_to_dataframes
(
pdf_path
)
:
"""Extract all tables from PDF as pandas DataFrames."""
dfs
=
[
]
with
pdfplumber
.
open
(
pdf_path
)
as
pdf
:
for
i
,
page
in
enumerate
(
pdf
.
pages
)
:
tables
=
page
.
extract_tables
(
)
for
j
,
table
in
enumerate
(
tables
)
:
if
table
and
len
(
table
)
1 :
First row as header
df
pd . DataFrame ( table [ 1 : ] , columns = table [ 0 ] ) df [ '_page' ] = i + 1 df [ '_table' ] = j + 1 dfs . append ( df ) return dfs Extract Specific Region def extract_invoice_amount ( pdf_path ) : """Extract amount from typical invoice layout.""" with pdfplumber . open ( pdf_path ) as pdf : page = pdf . pages [ 0 ]
Search for "Total" and get nearby numbers
words
page . extract_words ( ) for i , word in enumerate ( words ) : if 'total' in word [ 'text' ] . lower ( ) :
Look at next few words
for next_word in words [ i + 1 : i + 5 ] : text = next_word [ 'text' ] . replace ( ',' , '' ) . replace ( '$' , '' ) try : return float ( text ) except ValueError : continue return None Multi-column Layout def extract_columns ( page , num_columns = 2 ) : """Extract text from multi-column layout.""" width = page . width col_width = width / num_columns columns = [ ] for i in range ( num_columns ) : x0 = i * col_width x1 = ( i + 1 ) * col_width cropped = page . crop ( ( x0 , 0 , x1 , page . height ) ) columns . append ( cropped . extract_text ( ) ) return columns Examples Example 1: Financial Report Table Extraction import pdfplumber import pandas as pd def extract_financial_tables ( pdf_path ) : """Extract tables from financial report and save to Excel.""" with pdfplumber . open ( pdf_path ) as pdf : all_tables = [ ] for page_num , page in enumerate ( pdf . pages ) :
Debug: save table visualization
im
page . to_image ( ) im . debug_tablefinder ( ) im . save ( f'debug_page_ { page_num + 1 } .png' )
Extract tables
tables
page . extract_tables ( { "vertical_strategy" : "lines" , "horizontal_strategy" : "lines" , "snap_tolerance" : 5 , } ) for table in tables : if table and len ( table )
1 :
Clean data
clean_table
[ ] for row in table : clean_row = [ cell . strip ( ) if cell else '' for cell in row ] clean_table . append ( clean_row ) df = pd . DataFrame ( clean_table [ 1 : ] , columns = clean_table [ 0 ] ) df [ 'Source Page' ] = page_num + 1 all_tables . append ( df )
Save to Excel with multiple sheets
with pd . ExcelWriter ( 'extracted_tables.xlsx' ) as writer : for i , df in enumerate ( all_tables ) : df . to_excel ( writer , sheet_name = f'Table_ { i + 1 } ' , index = False ) return all_tables tables = extract_financial_tables ( 'annual_report.pdf' ) print ( f"Extracted { len ( tables ) } tables" ) Example 2: Invoice Data Extraction import pdfplumber import re from datetime import datetime def extract_invoice_data ( pdf_path ) : """Extract structured data from invoice PDF.""" data = { 'invoice_number' : None , 'date' : None , 'total' : None , 'line_items' : [ ] } with pdfplumber . open ( pdf_path ) as pdf : page = pdf . pages [ 0 ] text = page . extract_text ( )
Extract invoice number
inv_match
re . search ( r'Invoice\s#?\s:?\s*(\w+)' , text , re . IGNORECASE ) if inv_match : data [ 'invoice_number' ] = inv_match . group ( 1 )
Extract date
date_match
re . search ( r'Date\s:?\s(\d{1,2}[/-]\d{1,2}[/-]\d{2,4})' , text ) if date_match : data [ 'date' ] = date_match . group ( 1 )
Extract total
total_match
re . search ( r'Total\s:?\s\$?([\d,]+.?\d*)' , text , re . IGNORECASE ) if total_match : data [ 'total' ] = float ( total_match . group ( 1 ) . replace ( ',' , '' ) )
Extract line items from table
tables
page . extract_tables ( ) for table in tables : if table and any ( 'description' in str ( row ) . lower ( ) for row in table [ : 2 ] ) :
Found line items table
for row in table [ 1 : ] :
Skip header
if row and len ( row )
= 3 : data [ 'line_items' ] . append ( { 'description' : row [ 0 ] , 'quantity' : row [ 1 ] if len ( row )
1 else None , 'amount' : row [ - 1 ] } ) return data invoice = extract_invoice_data ( 'invoice.pdf' ) print ( f"Invoice # { invoice [ 'invoice_number' ] } " ) print ( f"Total: $ { invoice [ 'total' ] } " ) Example 3: Resume/CV Parser import pdfplumber def parse_resume ( pdf_path ) : """Extract structured sections from resume.""" with pdfplumber . open ( pdf_path ) as pdf : full_text = '' for page in pdf . pages : full_text += ( page . extract_text ( ) or '' ) + '\n'
Common resume sections
sections
{ 'contact' : '' , 'summary' : '' , 'experience' : '' , 'education' : '' , 'skills' : '' }
Split by common headers
import re section_patterns = { 'summary' : r'(summary|objective|profile)' , 'experience' : r'(experience|employment|work history)' , 'education' : r'(education|academic)' , 'skills' : r'(skills|competencies|technical)' } lines = full_text . split ( '\n' ) current_section = 'contact' for line in lines : line_lower = line . lower ( ) . strip ( )
Check if line is a section header
for section , pattern in section_patterns . items ( ) : if re . match ( pattern , line_lower ) : current_section = section break sections [ current_section ] += line + '\n' return sections resume = parse_resume ( 'resume.pdf' ) print ( "Skills:" , resume [ 'skills' ] ) Limitations Cannot extract from scanned/image PDFs (use OCR first) Complex layouts may need manual tuning Some PDF encryption types not supported Embedded fonts may affect text extraction No direct PDF editing capability Installation pip install pdfplumber
For image debugging (optional)
pip install Pillow Resources pdfplumber Documentation Table Extraction Guide Visual Debugging